INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(98769, 2019, 'bmc', 'Optimum contribution selection for animal breeding and conservation: the R package optiSel', 'Wellmann, Robin', 'Institute of Animal Science, University of Hohenheim, Garbenstraße, Stuttgart, Germany. r.wellmann@uni-hohenheim.de.', 'Germany', 10, 'Animals|Cattle|Female|Genetic Markers|Genetic Variation|Male|Pedigree|Selection, Genetic|Selective Breeding|Software', 'Journal Article', '10.1186/s12859-018-2450-5', '30642239', 'Selecting animals for breeding in the optimum way plays an essential role for the management of genetic resources and in selective breeding of livestock species. It requires to compute the optimum genetic contribution of each selection candidate to the next generation. Current software packages for optimum contribution selection (OCS) are not able to handle the main conflicting objectives of animal breeding programs simultaneously, which includes to increase genetic gain, to increase or to maintain genetic diversity, to recover the original genetic background of endangered breeds with historic introgression, and to maintain or increase genetic diversity at native alleles.'),
(98770, 2019, 'bmc', 'Exploiting locational and topological overlap model to identify modules in protein interaction networks', 'Cheng, Lixin|Liu, Pengfei|Wang, Dong|Leung, Kwong-Sak', 'Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong. lxcheng@cse.cuhk.edu.hk.|Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong.|Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China. wangdong@ems.hrbmu.edu.cn.|Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong. ksleung@cse.cuhk.edu.hk.', 'Hong Kong|Hong Kong|China|Hong Kong', 0, 'Algorithms|Computational Biology|Databases, Protein|Humans|Neoplasm Proteins|Protein Interaction Mapping|Protein Interaction Maps|Saccharomyces cerevisiae Proteins', 'Journal Article', '10.1186/s12859-019-2598-7', '30642247', 'Clustering molecular network is a typical method in system biology, which is effective in predicting protein complexes or functional modules. However, few studies have realized that biological molecules are spatial-temporally regulated to form a dynamic cellular network and only a subset of interactions take place at the same location in cells.'),
(98771, 2019, 'bmc', 'AMYCO: evaluation of mutational impact on prion-like proteins aggregation propensity', 'Iglesias, Valentin|Conchillo-Sole, Oscar|Batlle, Cristina|Ventura, Salvador', 'Institut de Biotecnologia i Biomedicina, Universitat Autònoma de Barcelona, Bellaterra, 08193, Spain.|Institut de Biotecnologia i Biomedicina, Universitat Autònoma de Barcelona, Bellaterra, 08193, Spain.|Institut de Biotecnologia i Biomedicina, Universitat Autònoma de Barcelona, Bellaterra, 08193, Spain.|Institut de Biotecnologia i Biomedicina, Universitat Autònoma de Barcelona, Bellaterra, 08193, Spain. salvador.ventura@uab.cat.', 'Spain|Spain|Spain|Spain', 0, 'Algorithms|Computational Biology|Humans|Mutation|Prion Proteins|Protein Aggregates|Protein Domains', 'Evaluation Studies', '10.1186/s12859-019-2601-3', '30642249', 'Around 1% of human proteins are predicted to contain a disordered and low complexity prion-like domain (PrLD). Mutations in PrLDs have been shown promote a transition towards an aggregation-prone state in several diseases.'),
(98772, 2019, 'bmc', 'SMoLR: visualization and analysis of single-molecule localization microscopy data in R', 'Paul, Maarten W|de Gruiter, H Martijn|Lin, Zhanmin|Baarends, Willy M|van Cappellen, Wiggert A|Houtsmuller, Adriaan B|Slotman, Johan A', 'Erasmus Optical Imaging Centre, Erasmus MC, Wytemaweg 80, 3015 CN, Rotterdam, The Netherlands.|Erasmus Optical Imaging Centre, Erasmus MC, Wytemaweg 80, 3015 CN, Rotterdam, The Netherlands.|Department of Neuroscience, Erasmus MC, Wytemaweg 80, 3015 CN, Rotterdam, The Netherlands.|Department of Developmental Biology, Erasmus MC, Wytemaweg 80, 3015 CN, Rotterdam, The Netherlands.|Erasmus Optical Imaging Centre, Erasmus MC, Wytemaweg 80, 3015 CN, Rotterdam, The Netherlands.|Erasmus Optical Imaging Centre, Erasmus MC, Wytemaweg 80, 3015 CN, Rotterdam, The Netherlands. a.houtsmuller@erasmusmc.nl.|Erasmus Optical Imaging Centre, Erasmus MC, Wytemaweg 80, 3015 CN, Rotterdam, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 2, 'Algorithms|Computer Graphics|DNA Repair Enzymes|Data Interpretation, Statistical|Humans|Microscopy, Fluorescence|Single-Cell Analysis|Software', 'Journal Article', '10.1186/s12859-018-2578-3', '30646838', 'Single-molecule localization microscopy is a super-resolution microscopy technique that allows for nanoscale determination of the localization and organization of proteins in biological samples. For biological interpretation of the data it is essential to extract quantitative information from the super-resolution data sets. Due to the complexity and size of these data sets flexible and user-friendly software is required.'),
(98773, 2019, 'bmc', 'sim1000G: a user-friendly genetic variant simulator in R for unrelated individuals and family-based designs', 'Dimitromanolakis, Apostolos|Xu, Jingxiong|Krol, Agnieszka|Briollais, Laurent', 'Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, 60, Murray Street, Toronto, ON, M5T 3L9, Canada.|Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, 60, Murray Street, Toronto, ON, M5T 3L9, Canada.|Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, 60, Murray Street, Toronto, ON, M5T 3L9, Canada.|Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, 60, Murray Street, Toronto, ON, M5T 3L9, Canada. laurent@lunenfeld.ca.', 'Canada|Canada|Canada|Canada', 0, 'Computational Biology|Female|Genetic Linkage|Genetic Markers|Genetics, Population|Humans|Linkage Disequilibrium|Male|Models, Genetic|Pedigree|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1186/s12859-019-2611-1', '30646839', 'Simulation of genetic variants data is frequently required for the evaluation of statistical methods in the fields of human and animal genetics. Although a number of high-quality genetic simulators have been developed, many of them require advanced knowledge in population genetics or in computation to be used effectively. In addition, generating simulated data in the context of family-based studies demands sophisticated methods and advanced computer programming.'),
(98774, 2019, 'bmc', 'pblat: a multithread blat algorithm speeding up aligning sequences to genomes', 'Wang, Meng|Kong, Lei', 'Center for Bioinformatics, State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, 100871, Peoples Republic of China.|Center for Bioinformatics, State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, 100871, Peoples Republic of China. kongl@mail.cbi.pku.edu.cn.', 'China|China', 7, 'Algorithms|Computational Biology|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-019-2597-8', '30646844', 'The blat is a widely used sequence alignment tool. It is especially useful for aligning long sequences and gapped mapping, which cannot be performed properly by other fast sequence mappers designed for short reads. However, the blat tool is single threaded and when used to map whole genome or whole transcriptome sequences to reference genomes this program can take days to finish, making it unsuitable for large scale sequencing projects and iterative analysis. Here, we present pblat (parallel blat), a parallelized blat algorithm with multithread and cluster computing support, which functions to rapidly fine map large scale DNA/RNA sequences against genomes.'),
(98775, 2019, 'bmc', 'iRODS metadata management for a cancer genome analysis workflow', 'Nieroda, Lech|Maas, Lukas|Thiebes, Scott|Lang, Ulrich|Sunyaev, Ali|Achter, Viktor|Peifer, Martin', 'Regional Computing Center (RRZK), University of Cologne, Cologne, 50931, Germany.|Department of Translational Genomics, Center of Integrated Oncology Cologne-Bonn, Medical Faculty, University of Cologne, Cologne, 50931, Germany.|Department of Economics and Management, Karlsruhe Institute of Technology, Karlsruhe, 76133, Germany.|Regional Computing Center (RRZK), University of Cologne, Cologne, 50931, Germany.|Department of Economics and Management, Karlsruhe Institute of Technology, Karlsruhe, 76133, Germany.|Regional Computing Center (RRZK), University of Cologne, Cologne, 50931, Germany. vachter@uni-koeln.de.|Department of Translational Genomics, Center of Integrated Oncology Cologne-Bonn, Medical Faculty, University of Cologne, Cologne, 50931, Germany. mpeifer@uni-koeln.de.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Computer Security|Computing Methodologies|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Metadata|Neoplasms|Polymorphism, Genetic|Sequence Analysis, DNA|Software|Workflow', 'Journal Article', '10.1186/s12859-018-2576-5', '30646845', 'The massive amounts of data from next generation sequencing (NGS) methods pose various challenges with respect to data security, storage and metadata management. While there is a broad range of data analysis pipelines, these challenges remain largely unaddressed to date.'),
(98776, 2019, 'bmc', 'Optimising orbit counting of arbitrary order by equation selection', 'Melckenbeeck, Ine|Audenaert, Pieter|Van Parys, Thomas|Van De Peer, Yves|Colle, Didier|Pickavet, Mario', 'Ghent University - imec, IDLab, Technologiepark 15, Ghent, 9052, Belgium.|Ghent University - imec, IDLab, Technologiepark 15, Ghent, 9052, Belgium. pieter.audenaert@ugent.be.|Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium.|Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium.|Ghent University - imec, IDLab, Technologiepark 15, Ghent, 9052, Belgium.|Ghent University - imec, IDLab, Technologiepark 15, Ghent, 9052, Belgium.', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 0, 'Algorithms|Computational Biology|Computer Graphics|Computer Simulation|Data Interpretation, Statistical|Diabetes Mellitus|Humans|Models, Biological|Protein Interaction Mapping|Proteins', 'Journal Article', '10.1186/s12859-018-2483-9', '30646859', 'Graphlets are useful for bioinformatics network analysis. Based on the structure of Hoxc4x8devar and Demxc5xa1ars ORCA algorithm, we have created an orbit counting algorithm, named Jesse. This algorithm, like ORCA, uses equations to count the orbits, but unlike ORCA it can count graphlets of any order. To do so, it generates the required internal structures and equations automatically. Many more redundant equations are generated, however, and Jesses running time is highly dependent on which of these equations are used. Therefore, this paper aims to investigate which equations are most efficient, and which factors have an effect on this efficiency.'),
(98777, 2019, 'bmc', 'MITK-ModelFit: A generic open-source framework for model fits and their exploration in medical imaging - design, implementation and application on the example of DCE-MRI', 'Debus, Charlotte|Floca, Ralf|Ingrisch, Michael|Kompan, Ina|Maier-Hein, Klaus|Abdollahi, Amir|Nolden, Marco', 'German Cancer Consortium (DKTK), Heidelberg, Germany. c.debus@dkfz-heidelberg.de.|Heidelberg Institute of Radiation Oncology (HIRO), Heidelberg, Germany. r.floca@dkfz-heidelberg.de.|Department of Radiology, University Hospital Munich, Ludwig-Maximilians-University Munich, Munich, Germany.|Heidelberg Institute of Radiation Oncology (HIRO), Heidelberg, Germany.|Heidelberg Institute of Radiation Oncology (HIRO), Heidelberg, Germany.|German Cancer Consortium (DKTK), Heidelberg, Germany.|Division of Medical Image Computing, German Cancer Research Center DKFZ, Heidelberg, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 5, 'Algorithms|Contrast Media|Diagnostic Imaging|Diffusion Magnetic Resonance Imaging|Glioblastoma|Humans|Image Enhancement|Software|Tomography, X-Ray Computed', 'Journal Article', '10.1186/s12859-018-2588-1', '30651067', 'Many medical imaging techniques utilize fitting approaches for quantitative parameter estimation and analysis. Common examples are pharmacokinetic modeling in dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI)/computed tomography (CT), apparent diffusion coefficient calculations and intravoxel incoherent motion modeling in diffusion-weighted MRI and Z-spectra analysis in chemical exchange saturation transfer MRI. Most available software tools are limited to a special purpose and do not allow for own developments and extensions. Furthermore, they are mostly designed as stand-alone solutions using external frameworks and thus cannot be easily incorporated natively in the analysis workflow.'),
(98778, 2019, 'bmc', 'Barcode identification for single cell genomics', 'Tambe, Akshay|Pachter, Lior', 'Division of Biology and Biological Engineering, California Institute of Technology, 116 Kerckhoff Laboratory, Pasadena, CA, 91125, USA.|Departments of Biology and Computing & Mathematical Sciences, California Institute of Technology, 116 Kerckhoff Laboratory, Pasadena, CA, 91125, USA. lpachter@caltech.edu.', 'United States|United States', 0, 'DNA|Genomics|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-019-2612-0', '30654736', 'Single-cell sequencing experiments use short DNA barcode tags to identify reads that originate from the same cell. In order to recover single-cell information from such experiments, reads must be grouped based on their barcode tag, a crucial processing step that precedes other computations. However, this step can be difficult due to high rates of mismatch and deletion errors that can afflict barcodes.'),
(98779, 2019, 'bmc', 'PIXER: an automated particle-selection method based on segmentation using a deep neural network', 'Zhang, Jingrong|Wang, Zihao|Chen, Yu|Han, Renmin|Liu, Zhiyong|Sun, Fei|Zhang, Fa', 'High Performance Computer Research Center, Institute of Computing Technology Chinese Academy of Sciences, No. 6 Kexueyuan South Road, Haidian District, Beijing, 100190, China.|High Performance Computer Research Center, Institute of Computing Technology Chinese Academy of Sciences, No. 6 Kexueyuan South Road, Haidian District, Beijing, 100190, China.|High Performance Computer Research Center, Institute of Computing Technology Chinese Academy of Sciences, No. 6 Kexueyuan South Road, Haidian District, Beijing, 100190, China.|Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Thuwal, 23955-6900, Saudi Arabia.|High Performance Computer Research Center, Institute of Computing Technology Chinese Academy of Sciences, No. 6 Kexueyuan South Road, Haidian District, Beijing, 100190, China.|University of Chinese Academy of Sciences, Beijing, China.|High Performance Computer Research Center, Institute of Computing Technology Chinese Academy of Sciences, No. 6 Kexueyuan South Road, Haidian District, Beijing, 100190, China. zhangfa@ict.ac.cn.', 'China|China|China|Saudi Arabia|China|China|China', 0, 'Algorithms|Image Processing, Computer-Assisted|Neural Networks (Computer)', 'Journal Article', '10.1186/s12859-019-2614-y', '30658571', 'Cryo-electron microscopy (cryo-EM) has become a widely used tool for determining the structures of proteins and macromolecular complexes. To acquire the input for single-particle cryo-EM reconstruction, researchers must select hundreds of thousands of particles from micrographs. As the signal-to-noise ratio (SNR) of micrographs is extremely low, the performance of automated particle-selection methods is still unable to meet research requirements. To free researchers from this laborious work and to acquire a large number of high-quality particles, we propose an automated particle-selection method (PIXER) based on the idea of segmentation using a deep neural network.'),
(98780, 2019, 'bmc', 'Comparative analysis of differential gene expression analysis tools for single-cell RNA sequencing data', 'Wang, Tianyu|Li, Boyang|Nelson, Craig E|Nabavi, Sheida', 'Computer Science and Engineering Department, University of Connecticut, Storrs, CT, USA.|Department of Molecular & Cell Biology, University of Connecticut, Storrs, CT, USA.|Department of Molecular & Cell Biology, The Institute for Systems Genomics, CLAS, University of Connecticut, Storrs, CT, USA.|Computer Science and Engineering Department, The Institute for Systems Genomics, University of Connecticut, Storrs, CT, USA. sheida.nabavi@uconn.edu.', 'United States|United States|United States|United States', 20, 'Gene Expression Profiling|Humans|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-019-2599-6', '30658573', 'The analysis of single-cell RNA sequencing (scRNAseq) data plays an important role in understanding the intrinsic and extrinsic cellular processes in biological and biomedical research. One significant effort in this area is the detection of differentially expressed (DE) genes. scRNAseq data, however, are highly heterogeneous and have a large number of zero counts, which introduces challenges in detecting DE genes. Addressing these challenges requires employing new approaches beyond the conventional ones, which are based on a nonzero difference in average expression. Several methods have been developed for differential gene expression analysis of scRNAseq data. To provide guidance on choosing an appropriate tool or developing a new one, it is necessary to evaluate and compare the performance of differential gene expression analysis methods for scRNAseq data.'),
(98781, 2019, 'bmc', 'Automated cell segmentation in FIJI® using the DRAQ5 nuclear dye', 'Schwendy, Mischa|Unger, Ronald E|Bonn, Mischa|Parekh, Sapun H', 'Max Planck Institute for Polymer Research, Ackermannweg 10, 55128, Mainz, Germany. schwendym@mpip-mainz.mpg.de.|Institute of Pathology, Universitätsmedizin-Mainz, Langenbeckstraße 1, 55131, Mainz, Germany.|Max Planck Institute for Polymer Research, Ackermannweg 10, 55128, Mainz, Germany.|Max Planck Institute for Polymer Research, Ackermannweg 10, 55128, Mainz, Germany. parekh@mpip-mainz.mpg.de.', 'Germany|Germany|Germany|Germany', 0, 'Algorithms|Anthraquinones|Cell Separation|Humans|Image Processing, Computer-Assisted', 'Journal Article', '10.1186/s12859-019-2602-2', '30658582', 'Image segmentation and quantification are essential steps in quantitative cellular analysis. In this work, we present a fast, customizable, and unsupervised cell segmentation method that is based solely on Fiji (is just ImageJ)®, one of the most commonly used open-source software packages for microscopy analysis. In our method, the leaky fluorescence from the DNA stain DRAQ5 is used for automated nucleus detection and 2D cell segmentation.'),
(98782, 2019, 'bmc', 'BPG: Seamless, automated and interactive visualization of scientific data', 'Png, Christine|Green, Jeffrey|Chong, Lauren C|Waggott, Daryl|Prokopec, Stephenie D|Shamsi, Mehrdad|Nguyen, Francis|Mak, Denise Y F|Lam, Felix|Albuquerque, Marco A|Wu, Ying|Jung, Esther H|Starmans, Maud H W|Chan-Seng-Yue, Michelle A|Yao, Cindy Q|Liang, Bianca|Lalonde, Emilie|Haider, Syed|Simone, Nicole A|Sendorek, Dorota|Chu, Kenneth C|Moon, Nathalie C|Fox, Natalie S|Grzadkowski, Michal R|Harding, Nicholas J|Fung, Clement|Murdoch, Amanda R|Houlahan, Kathleen E|Wang, Jianxin|Garcia, David R|de Borja, Richard|Sun, Ren X|Lin, Xihui|Chen, Gregory M|Lu, Aileen|Shiah, Yu-Jia|Zia, Amin|Kearns, Ryan|Boutros, Paul C', 'Ontario Institute for Cancer Research, Toronto, Canada.|Ontario Institute for Cancer Research, Toronto, Canada.|Ontario Institute for Cancer Research, Toronto, Canada.|Ontario Institute for Cancer Research, Toronto, Canada.|Ontario Institute for Cancer Research, Toronto, Canada.|Ontario Institute for Cancer Research, Toronto, Canada.|Ontario Institute for Cancer Research, Toronto, Canada.|Ontario Institute for Cancer Research, Toronto, Canada.|Ontario Institute for Cancer Research, Toronto, Canada.|Ontario Institute for Cancer Research, Toronto, Canada.|Ontario Institute for Cancer Research, Toronto, Canada.|Ontario Institute for Cancer Research, Toronto, Canada.|Ontario Institute for Cancer Research, Toronto, Canada.|Ontario Institute for Cancer Research, Toronto, Canada.|Ontario Institute for Cancer Research, Toronto, Canada.|Ontario Institute for Cancer Research, Toronto, Canada.|Ontario Institute for Cancer Research, Toronto, Canada.|Ontario Institute for Cancer Research, Toronto, Canada.|Ontario Institute for Cancer Research, Toronto, Canada.|Ontario Institute for Cancer Research, Toronto, Canada.|Ontario Institute for Cancer Research, Toronto, Canada.|Ontario Institute for Cancer Research, Toronto, Canada.|Ontario Institute for Cancer Research, Toronto, Canada.|Ontario Institute for Cancer Research, Toronto, Canada.|Ontario Institute for Cancer Research, Toronto, Canada.|Ontario Institute for Cancer Research, Toronto, Canada.|Ontario Institute for Cancer Research, Toronto, Canada.|Ontario Institute for Cancer Research, Toronto, Canada.|Ontario Institute for Cancer Research, Toronto, Canada.|Ontario Institute for Cancer Research, Toronto, Canada.|Ontario Institute for Cancer Research, Toronto, Canada.|Ontario Institute for Cancer Research, Toronto, Canada.|Ontario Institute for Cancer Research, Toronto, Canada.|Ontario Institute for Cancer Research, Toronto, Canada.|Ontario Institute for Cancer Research, Toronto, Canada.|Ontario Institute for Cancer Research, Toronto, Canada.|Ontario Institute for Cancer Research, Toronto, Canada.|Ontario Institute for Cancer Research, Toronto, Canada.|Ontario Institute for Cancer Research, Toronto, Canada. pboutros@mednet.ucla.edu.', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Ca', 14, 'Data Analysis|Humans|Simulation Training|Software', 'Journal Article', '10.1186/s12859-019-2610-2', '30665349', 'We introduce BPG, a framework for generating publication-quality, highly-customizable plots in the R statistical environment.'),
(98783, 2019, 'bmc', 'An integrated package for bisulfite DNA methylation data analysis with Indel-sensitive mapping', 'Zhou, Qiangwei|Lim, Jing-Quan|Sung, Wing-Kin|Li, Guoliang', 'National Key Laboratory of Crop Genetic Improvement, Agricultural Bioinformatics Key Laboratory of Hubei Province, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China.|Department of Computer Science, National University of Singapore, Singapore, 117417, Singapore.|Department of Computer Science, National University of Singapore, Singapore, 117417, Singapore. ksung@comp.nus.edu.sg.|National Key Laboratory of Crop Genetic Improvement, Agricultural Bioinformatics Key Laboratory of Hubei Province, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China. guoliang.li@mail.hzau.edu.cn.', 'China|Singapore|Singapore|China', 0, 'Algorithms|DNA Methylation|Data Analysis|Humans|Sequence Analysis, DNA|Software|Sulfites', 'Journal Article', '10.1186/s12859-018-2593-4', '30669962', 'DNA methylation plays crucial roles in most eukaryotic organisms. Bisulfite sequencing (BS-Seq) is a sequencing approach that provides quantitative cytosine methylation levels in genome-wide scope and single-base resolution. However, genomic variations such as insertions and deletions (indels) affect methylation calling, and the alignment of reads near/across indels becomes inaccurate in the presence of polymorphisms. Hence, the simultaneous detection of DNA methylation and indels is important for exploring the mechanisms of functional regulation in organisms.'),
(98784, 2019, 'bmc', 's-dePooler: determination of polymorphism carriers from overlapping DNA pools', 'Zhernakov, Aleksandr Igorevich|Afonin, Alexey Mikhailovich|Gavriliuk, Natalia Dmitrievna|Moiseeva, Olga Mikhailovna|Zhukov, Vladimir Aleksandrovich', 'Research Department of Non-Coronary Heart Diseases, Almazov National Medical Research Center, Ministry of Health of Russia, 2 Akkuratova St., St. Petersburg, 197341, Russia. AZhernakov@arriam.ru.|All-Russia Research Institute for Agricultural Microbiology (ARRIAM), 3 Podbelsky Ch., St. Petersburg - Pushkin, 196608, Russia.|Research Department of Non-Coronary Heart Diseases, Almazov National Medical Research Center, Ministry of Health of Russia, 2 Akkuratova St., St. Petersburg, 197341, Russia.|Research Department of Non-Coronary Heart Diseases, Almazov National Medical Research Center, Ministry of Health of Russia, 2 Akkuratova St., St. Petersburg, 197341, Russia.|Research Department of Non-Coronary Heart Diseases, Almazov National Medical Research Center, Ministry of Health of Russia, 2 Akkuratova St., St. Petersburg, 197341, Russia.', '-|-|-|-|-', 0, 'DNA|Genome-Wide Association Study|Humans|Polymorphism, Genetic|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-019-2616-9', '30669964', 'Samples pooling is a method widely used in studies to reduce costs and labour. DNA sample pooling combined with massive parallel sequencing is a powerful tool for discovering DNA variants (polymorphisms) in large analysing populations, which is the base of such research fields as Genome-Wide Association Studies, evolutionary and population studies, etc. Usage of overlapping pools where each sample is present in multiple pools can enhance the accuracy of polymorphism detection and allow identifying carriers of rare-variants. Surprisingly there is a lack of tools for result interpretation and carrier identification, i.e. for depooling.'),
(98785, 2019, 'bmc', 'Correction to: Computational approaches for cancer 2017 workshop overview', 'Chandrasekaran, Sunita|Stahlberg, Eric', 'Department of Computer and Information Sciences, University of Delaware, Newark, DE, USA.|Biomedical Informatics and Data Science Directorate, Frederick National Laboratory, Frederick, MD, USA. eric.stahlberg@nih.gov.', 'United States|United States', 0, '-', 'Journal Article', '10.1186/s12859-019-2631-x', '30669966', 'xe1x85x9f.'),
(98786, 2019, 'bmc', 'Real world scenarios in rare variant association analysis: the impact of imbalance and sample size on the power in silico', 'Zhang, Xinyuan|Basile, Anna O|Pendergrass, Sarah A|Ritchie, Marylyn D', 'Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.|Department of Biomedical Informatics, Columbia University, New York, NY, USA.|Biomedical and Translational Informatics Institute, Geisinger, Danville, PA, USA.|Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. marylyn@pennmedicine.upenn.edu.', 'United States|United States|United States|United States', 0, 'Computer Simulation|Genetic Association Studies|Humans|Models, Genetic|Research Design|Sample Size', 'Journal Article', '10.1186/s12859-018-2591-6', '30669967', 'The development of sequencing techniques and statistical methods provides great opportunities for identifying the impact of rare genetic variation on complex traits. However, there is a lack of knowledge on the impact of sample size, case numbers, the balance of cases vs controls for both burden and dispersion based rare variant association methods. For example, Phenome-Wide Association Studies may have a wide range of case and control sample sizes across hundreds of diagnoses and traits, and with the application of statistical methods to rare variants, it is important to understand the strengths and limitations of the analyses.'),
(98787, 2019, 'bmc', 'Comprehensive anticancer drug response prediction based on a simple cell line-drug complex network model', 'Wei, Dong|Liu, Chuanying|Zheng, Xiaoqi|Li, Yushuang', 'School of Science, Yanshan University, Qinhuangdao, 066004, China.|School of Science, Yanshan University, Qinhuangdao, 066004, China.|Department of Mathematics, Shanghai Normal University, Shanghai, 200234, China. xqzheng@shnu.edu.cn.|School of Science, Yanshan University, Qinhuangdao, 066004, China. yushuangli@ysu.edu.cn.', 'China|China|China|China', 0, 'Antineoplastic Agents|Cell Line, Tumor|Humans|Precision Medicine', 'Journal Article', '10.1186/s12859-019-2608-9', '30670007', 'Accurate prediction of anticancer drug responses in cell lines is a crucial step to accomplish the precision medicine in oncology. Although many popular computational models have been proposed towards this non-trivial issue, there is still room for improving the prediction performance by combining multiple types of genome-wide molecular data.'),
(98788, 2019, 'bmc', 'VAPiD: a lightweight cross-platform viral annotation pipeline and identification tool to facilitate virus genome submissions to NCBI GenBank', 'Shean, Ryan C|Makhsous, Negar|Stoddard, Graham D|Lin, Michelle J|Greninger, Alexander L', 'Department of Laboratory Medicine, University of Washington, Seattle, WA, USA.|Department of Laboratory Medicine, University of Washington, Seattle, WA, USA.|Department of Neurobiology, University of Washington, Seattle, WA, USA.|Department of Laboratory Medicine, University of Washington, Seattle, WA, USA.|Department of Laboratory Medicine, University of Washington, Seattle, WA, USA. agrening@uw.edu.', 'United States|United States|United States|United States|United States', 0, 'Databases, Nucleic Acid|Genome, Viral|Genomics|Humans', 'Journal Article', '10.1186/s12859-019-2606-y', '30674273', 'With sequencing technologies becoming cheaper and easier to use, more groups are able to obtain whole genome sequences of viruses of public health and scientific importance. Submission of genomic data to NCBI GenBank is a requirement prior to publication and plays a critical role in making scientific data publicly available. GenBank currently has automatic prokaryotic and eukaryotic genome annotation pipelines but has no viral annotation pipeline beyond influenza virus. Annotation and submission of viral genome sequence is a non-trivial task, especially for groups that do not routinely interact with GenBank for data submissions.'),
(98789, 2019, 'bmc', 'A deep learning method to more accurately recall known lysine acetylation sites', 'Wu, Meiqi|Yang, Yingxi|Wang, Hui|Xu, Yan', 'Department of Information and Computer Science, University of Science and Technology Beijing, Beijing, 100083, China.|Department of Information and Computer Science, University of Science and Technology Beijing, Beijing, 100083, China.|Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China.|Department of Information and Computer Science, University of Science and Technology Beijing, Beijing, 100083, China. xuyan@ustb.edu.cn.', 'China|China|China|China', 7, 'Acetylation|Deep Learning|Lysine|Protein Processing, Post-Translational', 'Journal Article', '10.1186/s12859-019-2632-9', '30674277', 'Lysine acetylation in protein is one of the most important post-translational modifications (PTMs). It plays an important role in essential biological processes and is related to various diseases. To obtain a comprehensive understanding of regulatory mechanism of lysine acetylation, the key is to identify lysine acetylation sites. Previously, several shallow machine learning algorithms had been applied to predict lysine modification sites in proteins. However, shallow machine learning has some disadvantages. For instance, it is not as effective as deep learning for processing big data.'),
(98790, 2019, 'bmc', 'PaperBot: open-source web-based search and metadata organization of scientific literature', 'Maraver, Patricia|Armañanzas, Rubén|Gillette, Todd A|Ascoli, Giorgio A', 'Center for Neural Informatics, Structures, & Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, USA.|Center for Neural Informatics, Structures, & Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, USA.|Center for Neural Informatics, Structures, & Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, USA.|Center for Neural Informatics, Structures, & Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, USA. ascoli@gmu.edu.', 'United States|United States|United States|United States', 0, 'Biomedical Research|Databases, Bibliographic|Information Storage and Retrieval|Internet|Metadata|Publications|Software|User-Computer Interface', 'Journal Article', '10.1186/s12859-019-2613-z', '30678631', 'The biomedical literature is expanding at ever-increasing rates, and it has become extremely challenging for researchers to keep abreast of new data and discoveries even in their own domains of expertise. We introduce PaperBot, a configurable, modular, open-source crawler to automatically find and efficiently index peer-reviewed publications based on periodic full-text searches across publisher web portals.'),
(98791, 2019, 'bmc', 'IMOS: improved Meta-aligner and Minimap2 On Spark', 'Hadadian Nejad Yousefi, Mostafa|Goudarzi, Maziar|Motahari, Seyed Abolfazl', 'Department of Computer Engineering, Sharif University of Technology, Azadi, Tehran, Iran.|Department of Computer Engineering, Sharif University of Technology, Azadi, Tehran, Iran. goudarzi@sharif.edu.|Department of Computer Engineering, Sharif University of Technology, Azadi, Tehran, Iran.', '-|-|-', 0, 'Algorithms|Chromosome Mapping|Computational Biology|Databases, Factual|Databases, Genetic|Genome, Human|Genomics|Humans|Sequence Alignment|Sequence Analysis, DNA|Software|Workflow', 'Journal Article', '10.1186/s12859-018-2592-5', '30678641', 'Long reads provide valuable information regarding the sequence composition of genomes. Long reads are usually very noisy which renders their alignments on the reference genome a daunting task. It may take days to process datasets enough to sequence a human genome on a single node. Hence, it is of primary importance to have an aligner which can operate on distributed clusters of computers with high performance in accuracy and speed.'),
(98792, 2019, 'bmc', 'Parametric and non-parametric gradient matching for network inference: a comparison', 'Dony, Leander|He, Fei|Stumpf, Michael P H', 'Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK.|Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK.|Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK. mstumpf@unimelb.edu.au.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Gene Regulatory Networks|Normal Distribution', 'Journal Article', '10.1186/s12859-018-2590-7', '30683048', 'Reverse engineering of gene regulatory networks from time series gene-expression data is a challenging problem, not only because of the vast sets of candidate interactions but also due to the stochastic nature of gene expression. We limit our analysis to nonlinear differential equation based inference methods. In order to avoid the computational cost of large-scale simulations, a two-step Gaussian process interpolation based gradient matching approach has been proposed to solve differential equations approximately.'),
(98793, 2019, 'bmc', 'A controlled comparison of thickness, volume and surface areas from multiple cortical parcellation packages', 'Mikhael, Shadia S|Pernet, Cyril', 'University of Edinburgh, Centre for Clinical Brain Sciences (CCBS), The Chancellors Building, 49 Little France Crescent, Edinburgh, EH16 4SB, UK. s1163658@sms.ed.ac.uk.|University of Edinburgh, Centre for Clinical Brain Sciences (CCBS), The Chancellors Building, 49 Little France Crescent, Edinburgh, EH16 4SB, UK.', 'France|France', 0, 'Algorithms|Cerebral Cortex|Female|Gray Matter|Humans|Male|Middle Aged|Neuroimaging|Organ Size|Reproducibility of Results|Software|White Matter', 'Comparative Study', '10.1186/s12859-019-2609-8', '30691385', '-'),
(98794, 2019, 'bmc', 'Assessment of weighted topological overlap (wTO) to improve fidelity of gene co-expression networks', 'Voigt, André|Almaas, Eivind', 'Network Systems Biology Group, Department of Biotechnology, NTNU - Norwegian University of Science and Technology, Trondheim, Norway. andre.voigt@ntnu.no.|Network Systems Biology Group, Department of Biotechnology, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.', 'Norway|Norway', 3, 'Algorithms|Animals|Gene Expression Regulation|Gene Regulatory Networks|Humans|Mice|Systems Biology', 'Journal Article', '10.1186/s12859-019-2596-9', '30691386', 'For more than a decade, gene expression data sets have been used as basis for the construction of co-expression networks used in systems biology investigations, leading to many important discoveries in a wide range of subjects spanning human disease to evolution and the development of organisms. A commonly encountered challenge in such investigations is first that of detecting, then subsequently removing, spurious correlations (i.e. links) in these networks. While access to a large number of measurements per gene would reduce this problem, often only a small number of measurements are available. The weighted Topological Overlap (wTO) measure, which incorporates information from the shared network-neighborhood of a given gene-pair into a single score, is a metric that is frequently used with the implicit expectation of producing higher-quality networks. However, the actual extent to which wTO improves on the accuracy of a co-expression analysis has not been quantified.'),
(98795, 2019, 'bmc', 'ModelExplorer - software for visual inspection and inconsistency correction of genome-scale metabolic reconstructions', 'Martyushenko, Nikolay|Almaas, Eivind', 'Department of Biotechnology, NTNU - Norwegian University of Science and Technology, Trondheim, N-7491, Norway. nikolay.martyushenko@ntnu.no.|Department of Biotechnology, NTNU - Norwegian University of Science and Technology, Trondheim, N-7491, Norway. eivind.almaas@ntnu.no.', 'Norway|Norway', 3, 'Algorithms|Genome|Metabolic Networks and Pathways|Software|Time Factors', 'Journal Article', '10.1186/s12859-019-2615-x', '30691403', 'Genome-scale metabolic network reconstructions are low level chemical representations of biological organisms. These models allow the system-level investigation of metabolic phenotypes using a variety of computational approaches. The link between a metabolic network model and an organisms higher-level behaviour is usually found using a constraint-based analysis approach, such as FBA (Flux Balance Analysis). However, the process of model reconstruction rarely proceeds without error. Often, considerable parts of a model cannot carry flux under any condition. This is termed model inconsistency and is caused by faulty topology and/or stoichiometry of the underlying reconstructed network. While there exist several automated gap-filling tools that may solve some of the inconsistencies, much of the work still needs to be carried out manually. The common linear list format of writing biochemical reactions makes it difficult to intuit what is at the root of the inconsistent behaviour. Unfortunately, we have frequently observed that model builders do not correct their models past the abilities of automated tools, leaving many widely used models significantly inconsistent.'),
(98796, 2019, 'bmc', 'First step toward gene expression data integration: transcriptomic data acquisition with COMMAND>_', 'Moretto, Marco|Sonego, Paolo|Villaseñor-Altamirano, Ana B|Engelen, Kristof', 'Unit of Computational Biology, Research and Innovation Centre, Fondazione Edmund Mach, via E. Mach 1, 38010, San Michele allAdige, Italy. marco.moretto@fmach.it.|Unit of Computational Biology, Research and Innovation Centre, Fondazione Edmund Mach, via E. Mach 1, 38010, San Michele allAdige, Italy.|Laboratorio Internacional de Investigación Sobre el Genoma Humano, Universidad Nacional Autónoma De México, 76230, Juriquilla, Querétaro, Mexico.|Unit of Computational Biology, Research and Innovation Centre, Fondazione Edmund Mach, via E. Mach 1, 38010, San Michele allAdige, Italy. engelen.kristof@gmail.com.', 'Italy|Italy|Mexico|Italy', 2, 'Databases, Genetic|Gene Expression Profiling|Gene Expression Regulation|Humans|Software|Transcriptome|Workflow', 'Journal Article', '10.1186/s12859-019-2643-6', '30691411', 'Exploring cellular responses to stimuli using extensive gene expression profiles has become a routine procedure performed on a daily basis. Raw and processed data from these studies are available on public databases but the opportunity to fully exploit such rich datasets is limited due to the large heterogeneity of data formats. In recent years, several approaches have been proposed to effectively integrate gene expression data for analysis and exploration at a broader level. Despite the different goals and approaches towards gene expression data integration, the first step is common to any proposed method: data acquisition. Although it is seemingly straightforward to extract valuable information from a set of downloaded files, things can rapidly get complicated, especially as the number of experiments grows. Transcriptomic datasets are deposited in public databases with little regard to data format and thus retrieving raw data might become a challenging task. While for RNA-seq experiments such problem is partially mitigated by the fact that raw reads are generally available on databases such as the NCBI SRA, for microarray experiments standards are not equally well established, or enforced during submission, and thus a multitude of data formats has emerged.'),
(98797, 2019, 'bmc', 'Statistical significance approximation for local similarity analysis of dependent time series data', 'Zhang, Fang|Sun, Fengzhu|Luan, Yihui', 'School of Mathematics, Shandong University, Jinan, Shandong, 250100, China.|Quantitative and Computational Biology Program, Department of Biological Sciences, University of Southern California, 1050 Childs Way, Los Angeles, 90089, CA, USA.|School of Mathematics, Shandong University, Jinan, Shandong, 250100, China. yhluan@sdu.edu.cn.', 'China|United States|China', 0, 'Algorithms|Aquatic Organisms|Databases as Topic|Female|Humans|Male|Microbiota|Models, Statistical|Time Factors', 'Journal Article', '10.1186/s12859-019-2595-x', '30691412', 'Local similarity analysis (LSA) of time series data has been extensively used to investigate the dynamics of biological systems in a wide range of environments. Recently, a theoretical method was proposed to approximately calculate the statistical significance of local similarity (LS) scores. However, the method assumes that the time series data are independent identically distributed, which can be violated in many problems.'),
(98798, 2019, 'bmc', 'Integrating random walk and binary regression to identify novel miRNA-disease association', 'Niu, Ya-Wei|Wang, Guang-Hui|Yan, Gui-Ying|Chen, Xing', 'School of Mathematics, Shandong University, Jinan, 250100, China.|School of Mathematics, Shandong University, Jinan, 250100, China. ghwang@sdu.edu.cn.|Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China.|School of Information and Control Engineering, China University of Mining and Technology, No.1, Daxue Road, Xuzhou, 221116, Jiangsu, China. xingchen@amss.ac.cn.', 'China|China|China|China', 0, 'Algorithms|Computer Simulation|Female|Genetic Predisposition to Disease|Humans|Male|MicroRNAs|Neoplasms', 'Journal Article', '10.1186/s12859-019-2640-9', '30691413', 'In the last few decades, cumulative experimental researches have witnessed and verified the important roles of microRNAs (miRNAs) in the development of human complex diseases. Benefitting from the rapid growth both in the availability of miRNA-related data and the development of various analysis methodologies, up until recently, some computational models have been developed to predict human disease related miRNAs, efficiently and quickly.'),
(98799, 2019, 'bmc', 'GenESysV: a fast, intuitive and scalable genome exploration open source tool for variants generated from high-throughput sequencing projects', 'Zia, Mohammad|Spurgeon, Paul|Levesque, Adrian|Furlani, Thomas|Wang, Jianxin', 'Center for Computational Research, University at Buffalo, Buffalo, NY, USA.|Center for Computational Research, University at Buffalo, Buffalo, NY, USA.|Center for Computational Research, University at Buffalo, Buffalo, NY, USA.|Center for Computational Research, University at Buffalo, Buffalo, NY, USA.|Center for Computational Research, University at Buffalo, Buffalo, NY, USA. jw24@buffalo.edu.', 'United States|United States|United States|United States|United States', 0, 'Genetic Variation|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Software|Time Factors', 'Journal Article', '10.1186/s12859-019-2636-5', '30704396', 'High throughput sequencing technologies have been increasingly used in basic genetic research as well as in clinical applications. More and more variants underlying Mendelian and complex diseases are being discovered and documented using these technologies. However, identifying and obtaining a short list of candidate disease-causing variants remains challenging for most of the users after variant calling, especially for people without computational skills.'),
(98800, 2019, 'bmc', 'InTAD: chromosome conformation guided analysis of enhancer target genes', 'Okonechnikov, Konstantin|Erkek, Serap|Korbel, Jan O|Pfister, Stefan M|Chavez, Lukas', 'Hopp-Childrens Cancer Center at the NCT Heidelberg (KiTZ), Heidelberg, Germany.|Hopp-Childrens Cancer Center at the NCT Heidelberg (KiTZ), Heidelberg, Germany.|Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.|Hopp-Childrens Cancer Center at the NCT Heidelberg (KiTZ), Heidelberg, Germany.|Department of Medicine, Division of Medical Genetics, University of California San Diego (UCSD), San Diego, USA. lukaschavez@ucsd.edu.', 'Germany|Germany|Germany|Germany|United States', 2, 'Chromatin|Chromosomes|Computational Biology|Enhancer Elements, Genetic|Epigenesis, Genetic|Humans|Molecular Conformation|Software', 'Journal Article', '10.1186/s12859-019-2655-2', '30704404', 'High-throughput technologies for analyzing chromosome conformation at a genome scale have revealed that chromatin is organized in topologically associated domains (TADs). While TADs are relatively stable across cell types, intra-TAD activities are cell type specific. Epigenetic profiling of different tissues and cell-types has identified a large number of non-coding epigenetic regulatory elements (enhancers) that can be located far away from coding genes. Linear proximity is a commonly chosen criterion for associating enhancers with their potential target genes. While enhancers frequently regulate the closest gene, unambiguous identification of enhancer regulated genes remains to be a challenge in the absence of sample matched chromosome conformation data.'),
(98801, 2019, 'bmc', 'Intelligent diagnosis with Chinese electronic medical records based on convolutional neural networks', 'Li, Xiaozheng|Wang, Huazhen|He, Huixin|Du, Jixiang|Chen, Jian|Wu, Jinzhun', 'College of Computer Science and Technology, Huaqiao University, Xiamen, 361021, China.|College of Computer Science and Technology, Huaqiao University, Xiamen, 361021, China. wanghuazhen@hqu.edu.cn.|College of Computer Science and Technology, Huaqiao University, Xiamen, 361021, China.|College of Computer Science and Technology, Huaqiao University, Xiamen, 361021, China.|Research Department, Zhiye software, Xiamen, 361021, China.|Pediatric Department, The First Affiliated Hospital of Xiamen University, Xiamen, 361003, China.', 'China|China|China|China|China|China', 7, 'Dictionaries as Topic|Electronic Health Records|Humans|Language|Natural Language Processing|Neural Networks (Computer)|Semantics|Vocabulary', 'Journal Article', '10.1186/s12859-019-2617-8', '30709336', 'Benefiting from big data, powerful computation and new algorithmic techniques, we have been witnessing the renaissance of deep learning, particularly the combination of natural language processing (NLP) and deep neural networks. The advent of electronic medical records (EMRs) has not only changed the format of medical records but also helped users to obtain information faster. However, there are many challenges regarding researching directly using Chinese EMRs, such as low quality, huge quantity, imbalance, semi-structure and non-structure, particularly the high density of the Chinese language compared with English. Therefore, effective word segmentation, word representation and model architecture are the core technologies in the literature on Chinese EMRs.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(98802, 2019, 'bmc', 'Drug candidate identification based on gene expression of treated cells using tensor decomposition-based unsupervised feature extraction for large-scale data', 'Taguchi, Y-H', 'Department of Physics, Chuo University, 1-13-27 Kasuga, Bunkyo-ku, Tokyo, 112-8551, Japan. tag@granular.com.', 'Japan', 0, 'Algorithms|Cell Line|Computer Simulation|Drug Discovery|Humans|Principal Component Analysis|Protein Interaction Maps|Transcription Factors|Transcriptome', 'Journal Article', '10.1186/s12859-018-2395-8', '30717646', 'Although in silico drug discovery is necessary for drug development, two major strategies, a structure-based and ligand-based approach, have not been completely successful. Currently, the third approach, inference of drug candidates from gene expression profiles obtained from the cells treated with the compounds under study requires the use of a training dataset. Here, the purpose was to develop a new approach that does not require any pre-existing knowledge about the drug-protein interactions, but these interactions can be inferred by means of an integrated approach using gene expression profiles obtained from the cells treated with the analysed compounds and the existing data describing gene-gene interactions.'),
(98803, 2019, 'bmc', 'Characterization and identification of lysine glutarylation based on intrinsic interdependence between positions in the substrate sites', 'Huang, Kai-Yao|Kao, Hui-Ju|Hsu, Justin Bo-Kai|Weng, Shun-Long|Lee, Tzong-Yi', 'School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172, China.|Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, 518172, China.|Department of Medical Research, Taipei Medical University Hospital, Taipei city, 110, Taiwan.|Department of Medicine, Mackay Medical College, New Taipei City, 252, Taiwan.|School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172, China. francislee0215@gmail.com.', 'China|China|-|-|China', 3, 'Amino Acid Motifs|Amino Acid Sequence|Animals|Computational Biology|Databases, Protein|Glutarates|Lysine|Mice|Proteins|ROC Curve|Reproducibility of Results|Substrate Specificity|Support Vector Machine|User-Computer Interface', 'Journal Article', '10.1186/s12859-018-2394-9', '30717647', 'Glutarylation, the addition of a glutaryl group (five carbons) to a lysine residue of a protein molecule, is an important post-translational modification and plays a regulatory role in a variety of physiological and biological processes. As the number of experimentally identified glutarylated peptides increases, it becomes imperative to investigate substrate motifs to enhance the study of protein glutarylation. We carried out a bioinformatics investigation of glutarylation sites based on amino acid composition using a public database containing information on 430 non-homologous glutarylation sites.'),
(98804, 2019, 'bmc', 'Diverse dynamics features of novel protein kinase C (PKC) isozymes determine the selectivity of a fluorinated balanol analogue for PKCxcexb5', 'Hardianto, Ari|Khanna, Varun|Liu, Fei|Ranganathan, Shoba', 'Department of Molecular Sciences, Macquarie University, Sydney, NSW, 2109, Australia.|Department of Molecular Sciences, Macquarie University, Sydney, NSW, 2109, Australia.|Department of Molecular Sciences, Macquarie University, Sydney, NSW, 2109, Australia.|Department of Molecular Sciences, Macquarie University, Sydney, NSW, 2109, Australia. shoba.ranganathan@mq.edu.au.', 'Australia|Australia|Australia|Australia', 1, 'Adenosine Triphosphate|Azepines|Cluster Analysis|Halogenation|Humans|Hydroxybenzoates|Isoenzymes|Molecular Dynamics Simulation|Phosphorylation|Protein Conformation|Protein Kinase C-epsilon|Ribose|Sequence Alignment|Static Electricity|Substrate Specificity', 'Journal Article', '10.1186/s12859-018-2373-1', '30717648', '(-)-Balanol is an ATP-mimicking inhibitor that non-selectively targets protein kinase C (PKC) isozymes and cAMP-dependent protein kinase (PKA). While PKA constantly shows tumor promoting activities, PKC isozymes can ambiguously be tumor promoters or suppressors. In particular, PKCxcexb5 is frequently implicated in tumorigenesis and a potential target for anticancer drugs. We recently reported that the C5(S)-fluorinated balanol analogue (balanoid 1c) had improved binding affinity and selectivity for PKCxcexb5 but not to the other novel PKC isozymes, which share a highly similar ATP site. The underlying basis for this fluorine-based selectivity is not entirely comprehended and needs to be investigated further for the development of ATP mimic inhibitors specific for PKCxcexb5.'),
(98805, 2019, 'bmc', 'EpiMethEx: a tool for large-scale integrated analysis in methylation hotspots linked to genetic regulation', 'Candido, Saverio|Parasiliti Palumbo, Giuseppe Alessandro|Pennisi, Marzio|Russo, Giulia|Sgroi, Giuseppe|Di Salvatore, Valentina|Libra, Massimo|Pappalardo, Francesco', 'Department of Biomedical and Biotechnological Sciences, University of Catania, Via Santa Sofia, 97, Catania, 95123, Italy.|Department of Mathematics and Computer Science, University of Catania, Viale A. Doria, 6, Catania, 95125, Italy.|Department of Mathematics and Computer Science, University of Catania, Viale A. Doria, 6, Catania, 95125, Italy.|Department of Biomedical and Biotechnological Sciences, University of Catania, Via Santa Sofia, 97, Catania, 95123, Italy.|Department of Mathematics and Computer Science, University of Catania, Viale A. Doria, 6, Catania, 95125, Italy.|Department of Biomedical and Biotechnological Sciences, University of Catania, Via Santa Sofia, 97, Catania, 95123, Italy.|Department of Biomedical and Biotechnological Sciences, University of Catania, Via Santa Sofia, 97, Catania, 95123, Italy.|Department of Drug Sciences, University of Catania, Viale A. Doria, 6, Catania, 95125, Italy. francesco.pappalardo@unict.it.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 0, 'Computational Biology|CpG Islands|DNA Methylation|Epigenesis, Genetic|Gene Expression Regulation, Neoplastic|Humans|Melanoma|Software', 'Journal Article', '10.1186/s12859-018-2397-6', '30717649', 'DNA methylation is an epigenetic mechanism of genomic regulation involved in the maintenance of homeostatic balance. Dysregulation of DNA methylation status is one of the driver alterations occurring in neoplastic transformation and cancer progression. The identification of methylation hotspots associated to gene dysregulation may contribute to discover new prognostic and diagnostic biomarkers, as well as, new therapeutic targets.'),
(98806, 2019, 'bmc', 'GlyStruct: glycation prediction using structural properties of amino acid residues', 'Reddy, Hamendra Manhar|Sharma, Alok|Dehzangi, Abdollah|Shigemizu, Daichi|Chandra, Abel Avitesh|Tsunoda, Tatushiko', 'School of Engineering & Physics, University of the South Pacific, Suva, Fiji. hamendra.reddy@gmail.com.|School of Engineering & Physics, University of the South Pacific, Suva, Fiji. alok.sharma@griffith.edu.au.|Department of Computer Science, Morgan State University, Baltimore, MD, USA.|Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Tokyo, Japan.|School of Engineering & Physics, University of the South Pacific, Suva, Fiji.|Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Tokyo, Japan.', 'Fiji|Fiji|United States|Japan|Fiji|Japan', 8, 'Algorithms|Amino Acid Sequence|Amino Acids|Area Under Curve|Benchmarking|Computational Biology|Glycosylation|Humans|Peptides|Support Vector Machine', 'Journal Article', '10.1186/s12859-018-2547-x', '30717650', 'Glycation is a one of the post-translational modifications (PTM) where sugar molecules and residues in protein sequences are covalently bonded. It has become one of the clinically important PTM in recent times attributed to many chronic and age related complications. Being a non-enzymatic reaction, it is a great challenge when it comes to its prediction due to the lack of significant bias in the sequence motifs.'),
(98807, 2019, 'bmc', 'Understanding the evolutionary trend of intrinsically structural disorders in cancer relevant proteins as probed by Shannon entropy scoring and structure network analysis', 'Sen, Sagnik|Dey, Ashmita|Chowdhury, Sourav|Maulik, Ujjwal|Chattopadhyay, Krishnananda', 'Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032, India. sagnik.sen2008@gmail.com.|Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032, India.|CSIR-Indian Institute of Chemical Biology, Raja S.C. Mullick Road, Kolkata, 700032, India.|Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032, India.|Chemistry and Chemical Biology, Harvard University, 12 Oxford Street, Cambridge, Massachusetts, 02138, USA.', 'India|India|India|India|United States', 3, 'Animals|Entropy|Evolution, Molecular|Humans|Hydrophobic and Hydrophilic Interactions|Intrinsically Disordered Proteins|Neoplasms', 'Journal Article', '10.1186/s12859-018-2552-0', '30717651', 'Malignant diseases have become a threat for health care system. A panoply of biological processes is involved as the cause of these diseases. In order to unveil the mechanistic details of these diseased states, we analyzed protein families relevant to these diseases.'),
(98808, 2019, 'bmc', 'Discovering MoRFs by trisecting intrinsically disordered protein sequence into terminals and middle regions', 'Sharma, Ronesh|Sharma, Alok|Patil, Ashwini|Tsunoda, Tatsuhiko', 'School of Engineering and Physics, The University of the South Pacific, Suva, Fiji. sharmaronesh@yahoo.com.|School of Engineering and Physics, The University of the South Pacific, Suva, Fiji.|Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, 108-8639, Japan.|Laboratory of Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan.', 'Fiji|Fiji|Japan|Japan', 0, 'Amino Acid Sequence|Area Under Curve|Computational Biology|Databases, Protein|Intrinsically Disordered Proteins|Protein Domains', 'Journal Article', '10.1186/s12859-018-2396-7', '30717652', 'Molecular Recognition Features (MoRFs) are short protein regions present in intrinsically disordered protein (IDPs) sequences. MoRFs interact with structured partner protein and upon interaction, they undergo a disorder-to-order transition to perform various biological functions. Analyses of MoRFs are important towards understanding their function.'),
(98809, 2019, 'bmc', 'MIGREW: database on molecular identification of genes for resistance in wheat', 'Kazantsev, Fedor V|Skolotneva, Ekaterina S|Kelbin, Vasiliy N|Salina, Elena A|Lashin, Sergey A', 'Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia. kazfdr@bionet.nsc.ru.|Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia.|Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia.|Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia.|Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia.', '-|-|-|-|-', 1, 'Databases, Genetic|Disease Resistance|Genes, Plant|Genetic Markers|Plant Diseases|Triticum|Virulence', 'Journal Article', '10.1186/s12859-018-2569-4', '30717653', 'Population structure of fungal infections in wheat differs between wheat varieties and environments. Taking into account evolution of host-pathogen interactions, genetic diversity of both wheat and fungus must be a monitored. In order to catalogue information to support need of wheat pathologists and breeders, who use conventional methods and Molecular Assisted Selection (MAS) techniques, we have developed the Molecular Identification of Genes for Resistance in Wheat (MIGREW) database. The main goal of this database is to support wheat breeding efforts to develop immunity to rusts and powdery mildew. MIGREW is also focused on effectiveness of wheat resistance genes in different regions of Russia to provide users relevant information on the rapidly changing population structure of pathogens.'),
(98810, 2019, 'bmc', 'Benchmarking of different molecular docking methods for protein-peptide docking', 'Agrawal, Piyush|Singh, Harinder|Srivastava, Hemant Kumar|Singh, Sandeep|Kishore, Gaurav|Raghava, Gajendra P S', 'Center for Computation Biology, Indraprastha Institute of Information Technology, Okhla Phase III, New Delhi, 110020, India.|CSIR-Institute of Microbial Technology, Sector 39A, Chandigarh, India.|CSIR-Institute of Microbial Technology, Sector 39A, Chandigarh, India.|CSIR-Institute of Microbial Technology, Sector 39A, Chandigarh, India.|CSIR-Institute of Microbial Technology, Sector 39A, Chandigarh, India.|Center for Computation Biology, Indraprastha Institute of Information Technology, Okhla Phase III, New Delhi, 110020, India. raghava@iiitd.ac.in.', 'India|India|India|India|India|India', 19, 'Algorithms|Benchmarking|Binding Sites|Databases, Protein|Molecular Docking Simulation|Peptides|Protein Binding|Protein Structure, Secondary|Proteins|Reproducibility of Results', 'Journal Article', '10.1186/s12859-018-2449-y', '30717654', 'Molecular docking studies on protein-peptide interactions are a challenging and time-consuming task because peptides are generally more flexible than proteins and tend to adopt numerous conformations. There are several benchmarking studies on protein-protein, protein-ligand and nucleic acid-ligand docking interactions. However, a series of docking methods is not rigorously validated for protein-peptide complexes in the literature. Considering the importance and wide application of peptide docking, we describe benchmarking of 6 docking methods on 133 protein-peptide complexes having peptide length between 9 to 15 residues. The performance of docking methods was evaluated using CAPRI parameters like FNAT, I-RMSD, L-RMSD.'),
(98811, 2019, 'bmc', 'Identification and analysis of structurally critical fragments in HopS2', 'Borah, Sapna M|Jha, Anupam Nath', 'Department of Molecular Biology and Biotechnology, Tezpur University, Tezpur, Assam, 784028, India.|Department of Molecular Biology and Biotechnology, Tezpur University, Tezpur, Assam, 784028, India. anjha@tezu.ernet.in.', 'India|India', 0, 'Amino Acid Sequence|Bacterial Proteins|Computational Biology|Hydrogen Bonding|Models, Molecular|Molecular Dynamics Simulation|Principal Component Analysis|Protein Structure, Secondary|Pseudomonas syringae|Reproducibility of Results|Sequence Analysis, Protein|Structure-Activity Relationship', 'Journal Article', '10.1186/s12859-018-2551-1', '30717655', 'Among the diverse roles of the Type III secretion-system (T3SS), one of the notable functions is that it serves as unique nano machineries in gram-negative bacteria that facilitate the translocation of effector proteins from bacteria into their host. These effector proteins serve as potential targets to control the pathogenicity conferred to the bacteria. Despite being ideal choices to disrupt bacterial systems, it has been quite an ordeal in the recent times to experimentally reveal and establish a concrete sequence-structure-function relationship for these effector proteins. This work focuses on the disease-causing spectrum of an effector protein, HopS2 secreted by the phytopathogen Pseudomonas syringae pv. tomato DC3000.'),
(98812, 2019, 'bmc', 'Exploitation of reverse vaccinology and immunoinformatics as promising platform for genome-wide screening of new effective vaccine candidates against Plasmodium falciparum', 'Pritam, Manisha|Singh, Garima|Swaroop, Suchit|Singh, Akhilesh Kumar|Singh, Satarudra Prakash', 'Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow Campus, Lucknow, 226028, India.|Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow Campus, Lucknow, 226028, India.|Department of Zoology, University of Lucknow, Lucknow, 226007, India.|Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow Campus, Lucknow, 226028, India.|Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow Campus, Lucknow, 226028, India. spsingh5@amity.edu.', 'India|India|India|India|India', 10, 'Amino Acid Sequence|Antigens, Protozoan|Cluster Analysis|Computational Biology|Epitopes, T-Lymphocyte|Genome|Humans|Malaria Vaccines|Malaria, Falciparum|Molecular Docking Simulation|Plasmodium falciparum|Protozoan Proteins|Vaccinology', 'Journal Article', '10.1186/s12859-018-2482-x', '30717656', 'In the current scenario, designing of world-wide effective malaria vaccine against Plasmodium falciparum remain challenging despite the significant progress has been made in last few decades. Conventional vaccinology (isolate, inactivate and inject) approaches are time consuming, laborious and expensive, therefore, the use of computational vaccinology tools are imperative, which can facilitate the design of new and promising vaccine candidates.'),
(98813, 2019, 'bmc', 'Automated craniofacial landmarks detection on 3D image using geometry characteristics information', 'Abu, Arpah|Ngo, Chee Guan|Abu-Hassan, Nur Idayu Adira|Othman, Siti Adibah', 'Institute of Biological Sciences, Faculty of Science, University of Malaya, 50603, Kuala Lumpur, Malaysia. arpah@um.edu.my.|Institute of Biological Sciences, Faculty of Science, University of Malaya, 50603, Kuala Lumpur, Malaysia.|Department of Diagnostic & Allied Health Science, Faculty of Health & Life Sciences, Management & Science University, Shah Alam, Selangor, Malaysia.|Department of Paediatric Dentistry and Orthodontics / Clinical Craniofacial Dentistry Research Group, Faculty of Dentistry, University of Malaya, 50603, Kuala Lumpur, Malaysia.', 'Malaysia|Malaysia|Malaysia|Malaysia', 2, 'Adult|Anatomic Landmarks|Automation|Face|Female|Humans|Imaging, Three-Dimensional|Male|Reproducibility of Results|Skull|Software|User-Computer Interface', 'Journal Article', '10.1186/s12859-018-2548-9', '30717658', 'Indirect anthropometry (IA) is one of the craniofacial anthropometry methods to perform the measurements on the digital facial images. In order to get the linear measurements, a few definable points on the structures of individual facial images have to be plotted as landmark points. Currently, most anthropometric studies use landmark points that are manually plotted on a 3D facial image by the examiner. This method is time-consuming and leads to human biases, which will vary from intra-examiners to inter-examiners when involving large data sets. Biased judgment also leads to a wider gap in measurement error. Thus, this work aims to automate the process of landmarks detection to help in enhancing the accuracy of measurement. In this work, automated craniofacial landmarks (ACL) on a 3D facial image system was developed using geometry characteristics information to identify the nasion (n), pronasale (prn), subnasale (sn), alare (al), labiale superius (ls), stomion (sto), labiale inferius (li), and chelion (ch). These landmarks were detected on the 3D facial image in .obj file format. The IA was also performed by manually plotting the craniofacial landmarks using Mirror software. In both methods, once all landmarks were detected, the eight linear measurements were then extracted. Paired t-test was performed to check the validity of ACL (i) between the subjects and (ii) between the two methods, by comparing the linear measurements extracted from both ACL and AI. The tests were performed on 60 subjects (30 males and 30 females).'),
(98814, 2019, 'bmc', 'BioReader: a text mining tool for performing classification of biomedical literature', 'Simon, Christian|Davidsen, Kristian|Hansen, Christina|Seymour, Emily|Barnkob, Mike Bogetofte|Olsen, Lars Rønn', 'Disease Systems Biology, Novo Nordisk Center for Protein Research, University of Copenhagen, 2200, Copenhagen, Denmark.|Department of Health Technology, Technical University of Denmark, 2800, Lyngby, Denmark.|Department of Health Technology, Technical University of Denmark, 2800, Lyngby, Denmark.|La Jolla Institute for Allergy and Immunology, La Jolla, CA, 92037, USA.|MRC Human Immunology Unit, Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 9DU, UK.|Department of Health Technology, Technical University of Denmark, 2800, Lyngby, Denmark. lro@bioinformatics.dtu.dk.', 'Denmark|Denmark|Denmark|United Kingdom|United Kingdom|Denmark', 6, 'Algorithms|Biomedical Research|Data Mining|Databases, Factual|Disease|Humans|Journal Impact Factor|Publications|Workflow', 'Journal Article', '10.1186/s12859-019-2607-x', '30717659', 'Scientific data and research results are being published at an unprecedented rate. Many database curators and researchers utilize data and information from the primary literature to populate databases, form hypotheses, or as the basis for analyses or validation of results. These efforts largely rely on manual literature surveys for collection of these data, and while querying the vast amounts of literature using keywords is enabled by repositories such as PubMed, filtering relevant articles from such query results can be a non-trivial and highly time consuming task.'),
(98815, 2019, 'bmc', 'Stepwise large genome assembly approach: a case of Siberian larch (Larix sibirica Ledeb)', 'Kuzmin, Dmitry A|Feranchuk, Sergey I|Sharov, Vadim V|Cybin, Alexander N|Makolov, Stepan V|Putintseva, Yuliya A|Oreshkova, Natalya V|Krutovsky, Konstantin V', 'Laboratory of Forest Genomics, Genome Research and Education Center, Siberian Federal University, 660036, Krasnoyarsk, Russia.|Laboratory of Forest Genomics, Genome Research and Education Center, Siberian Federal University, 660036, Krasnoyarsk, Russia.|Laboratory of Forest Genomics, Genome Research and Education Center, Siberian Federal University, 660036, Krasnoyarsk, Russia.|Laboratory of Forest Genomics, Genome Research and Education Center, Siberian Federal University, 660036, Krasnoyarsk, Russia.|Laboratory of Forest Genomics, Genome Research and Education Center, Siberian Federal University, 660036, Krasnoyarsk, Russia.|Laboratory of Forest Genomics, Genome Research and Education Center, Siberian Federal University, 660036, Krasnoyarsk, Russia.|Laboratory of Forest Genomics, Genome Research and Education Center, Siberian Federal University, 660036, Krasnoyarsk, Russia.|Laboratory of Forest Genomics, Genome Research and Education Center, Siberian Federal University, 660036, Krasnoyarsk, Russia. konstantin.krutovsky@forst.uni-goettingen.de.', '-|-|-|-|-|-|-|-', 7, 'Arabidopsis|Genome, Plant|Larix|Prunus|Sequence Analysis, DNA|Time Factors', 'Journal Article', '10.1186/s12859-018-2570-y', '30717661', 'De novo assembling of large genomes, such as in conifers (~xe2x80x8912-30 Gbp), which also consist of ~xe2x80x8980% of repetitive DNA, is a very complex and computationally intense endeavor. One of the main problems in assembling such genomes lays in computing limitations of nucleotide sequence assembly programs (DNA assemblers). As a rule, modern assemblers are usually designed to assemble genomes with a length not exceeding the length of the human genome (3.24 Gbp). Most assemblers cannot handle the amount of input sequence data required to provide sufficient coverage needed for a high-quality assembly.'),
(98816, 2019, 'bmc', 'Computational discovery and annotation of conserved small open reading frames in fungal genomes', 'Mat-Sharani, Shuhaila|Firdaus-Raih, Mohd', 'Centre for Frontier Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, UKM, 43600, Bangi, Selangor, Malaysia.|Centre for Frontier Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, UKM, 43600, Bangi, Selangor, Malaysia. firdaus@mfrlab.org.', 'Malaysia|Malaysia', 5, 'Amino Acid Sequence|Computational Biology|Conserved Sequence|Gene Ontology|Genome, Fungal|Molecular Sequence Annotation|Open Reading Frames|Phylogeny', 'Journal Article', '10.1186/s12859-018-2550-2', '30717662', 'Small open reading frames (smORF/sORFs) that encode short protein sequences are often overlooked during the standard gene prediction process thus leading to many sORFs being left undiscovered and/or misannotated. For many genomes, a second round of sORF targeted gene prediction can complement the existing annotation. In this study, we specifically targeted the identification of ORFs encoding for 80 amino acid residues or less from 31 fungal genomes. We then compared the predicted sORFs and analysed those that are highly conserved among the genomes.'),
(98817, 2019, 'bmc', 'Latent network-based representations for large-scale gene expression data analysis', 'Dhifli, Wajdi|Puig, Julia|Dispot, Aurélien|Elati, Mohamed', 'University of Lille, 42, rue Paul Duez, Lille, 59000, France.|University of Lille, 42, rue Paul Duez, Lille, 59000, France.|University of Lille, 42, rue Paul Duez, Lille, 59000, France.|University of Lille, 42, rue Paul Duez, Lille, 59000, France. mohamed.elati@univ-lille.fr.', 'France|France|France|France', 1, 'Algorithms|Area Under Curve|Computational Biology|Data Analysis|Databases, Genetic|Gene Expression Regulation|Gene Regulatory Networks|Humans', 'Journal Article', '10.1186/s12859-018-2481-y', '30717663', 'With the recent advancements in high-throughput experimental procedures, biologists are gathering huge quantities of data. A main priority in bioinformatics and computational biology is to provide system level analytical tools capable of meeting an ever-growing production of high-throughput biological data while taking into account its biological context. In gene expression data analysis, genes have widely been considered as independent components. However, a systemic view shows that they act synergistically in living cells, forming functional complexes and more generally a biological system.'),
(98818, 2019, 'bmc', 'ENVirT: inference of ecological characteristics of viruses from metagenomic data', 'Jayasundara, Duleepa|Herath, Damayanthi|Senanayake, Damith|Saeed, Isaam|Yang, Cheng-Yu|Sun, Yuan|Chang, Bill C|Tang, Sen-Lin|Halgamuge, Saman K', 'School of Public Health and Community Medicine, University of New South Wales, Randwick, NSW 2052, Australia. duleepalj@gmail.com.|Optimisation and Pattern Recognition Research Group, Department of Mechanical Engineering, Melbourne School of Engineering, The University of Melbourne, Parkville, VIC 3010, Australia.|Optimisation and Pattern Recognition Research Group, Department of Mechanical Engineering, Melbourne School of Engineering, The University of Melbourne, Parkville, VIC 3010, Australia.|Optimisation and Pattern Recognition Research Group, Department of Mechanical Engineering, Melbourne School of Engineering, The University of Melbourne, Parkville, VIC 3010, Australia.|Biodiversity Research Center, Academia Sinica, Nan-Kang, Taipei 11529, Taiwan.|Optimisation and Pattern Recognition Research Group, Department of Mechanical Engineering, Melbourne School of Engineering, The University of Melbourne, Parkville, VIC 3010, Australia.|Yourgene Bioscience, No. 376-5, Fuxing Rd., Shu-Lin District, New Taipei City, Taiwan.|Biodiversity Research Center, Academia Sinica, Nan-Kang, Taipei 11529, Taiwan. sltang@gate.sinica.edu.tw.|Optimisation and Pattern Recognition Research Group, Department of Mechanical Engineering, Melbourne School of Engineering, The University of Melbourne, Parkville, VIC 3010, Australia.', 'Australia|Australia|Australia|Australia|-|Australia|-|-|Australia', 1, 'Algorithms|Computer Simulation|Ecological and Environmental Phenomena|Fermented Foods|Gastrointestinal Microbiome|Genome, Viral|Humans|Lakes|Metagenomics|Time Factors|Viruses', 'Journal Article', '10.1186/s12859-018-2398-5', '30717665', 'Estimating the parameters that describe the ecology of viruses,particularly those that are novel, can be made possible using metagenomic approaches. However, the best-performing existing methods require databases to first estimate an average genome length of a viral community before being able to estimate other parameters, such as viral richness. Although this approach has been widely used, it can adversely skew results since the majority of viruses are yet to be catalogued in databases.'),
(98819, 2019, 'bmc', 'Inverse similarity and reliable negative samples for drug side-effect prediction', 'Zheng, Yi|Peng, Hui|Ghosh, Shameek|Lan, Chaowang|Li, Jinyan', 'Advanced Analytics Institute, FEIT, University of Technology Sydney, 15 Broadway, Ultimo, NSW 2007, Australia.|Advanced Analytics Institute, FEIT, University of Technology Sydney, 15 Broadway, Ultimo, NSW 2007, Australia.|Advanced Analytics Institute, FEIT, University of Technology Sydney, 15 Broadway, Ultimo, NSW 2007, Australia.|Advanced Analytics Institute, FEIT, University of Technology Sydney, 15 Broadway, Ultimo, NSW 2007, Australia.|Advanced Analytics Institute, FEIT, University of Technology Sydney, 15 Broadway, Ultimo, NSW 2007, Australia. Jinyan.Li@uts.edu.au.', 'Australia|Australia|Australia|Australia|Australia', 7, 'Algorithms|Computational Biology|Databases as Topic|Drug-Related Side Effects and Adverse Reactions|Humans|Machine Learning|Proteins', 'Journal Article', '10.1186/s12859-018-2563-x', '30717666', 'In silico prediction of potential drug side-effects is of crucial importance for drug development, since wet experimental identification of drug side-effects is expensive and time-consuming. Existing computational methods mainly focus on leveraging validated drug side-effect relations for the prediction. The performance is severely impeded by the lack of reliable negative training data. Thus, a method to select reliable negative samples becomes vital in the performance improvement.'),
(98820, 2019, 'bmc', 'Functional homogeneity and specificity of topological modules in human proteome', 'Kaalia, Rama|Rajapakse, Jagath C', 'School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore.|School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore. asjagath@ntu.edu.sg.', 'Singapore|Singapore', 1, 'Algorithms|Humans|Protein Interaction Maps|Proteome|Reproducibility of Results', 'Journal Article', '10.1186/s12859-018-2549-8', '30717667', 'Functional modules in protein-protein interaction networks (PPIN) are defined by maximal sets of functionally associated proteins and are vital to understanding cellular mechanisms and identifying disease associated proteins. Topological modules of the human proteome have been shown to be related to functional modules of PPIN. However, the effects of the weights of interactions between protein pairs and the integration of physical (direct) interactions with functional (indirect expression-based) interactions have not been investigated in the detection of functional modules of the human proteome.'),
(98821, 2019, 'bmc', 'GCAC: galaxy workflow system for predictive model building for virtual screening', 'Bharti, Deepak R|Hemrom, Anmol J|Lynn, Andrew M', 'School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, 110067, India.|School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, 110067, India.|School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, 110067, India. andrew@jnu.ac.in.', 'India|India|India', 4, 'Computational Biology|Drug Discovery|Drug Evaluation, Preclinical|Models, Theoretical|Quantitative Structure-Activity Relationship|Reproducibility of Results|Software|User-Computer Interface|Workflow', 'Journal Article', '10.1186/s12859-018-2492-8', '30717669', 'Traditional drug discovery approaches are time-consuming, tedious and expensive. Identifying a potential drug-like molecule using high throughput screening (HTS) with high confidence is always a challenging task in drug discovery and cheminformatics. A small percentage of molecules that pass the clinical trial phases receives FDA approval. This whole process takes 10-12xe2x80x89years and millions of dollar of investment. The inconsistency in HTS is also a challenge for reproducible results. Reproducible research in computational research is highly desirable as a measure to evaluate scientific claims and published findings. This paper describes the development and availability of a knowledge based predictive model building system using the R Statistical Computing Environment and its ensured reproducibility using Galaxy workflow system.'),
(98822, 2019, 'bmc', 'Bioinformatics research at BGRS-2018', 'Tatarinova, Tatiana V|Chen, Ming|Orlov, Yuriy L', 'Department of Biology, University of La Verne, La Verne, CA, USA.|Zhejiang University, Hangzhou, 310058, China.|Institute of Cytology and Genetics SB RAS, 630090, Novosibirsk, Russia. orlov@bionet.nsc.ru.', 'United States|China|United States', 7, 'Algorithms|Computational Biology|Data Mining|Genome|Genome, Plastid|Plants|Research', 'Congress', '10.1186/s12859-018-2566-7', '30717672', '-'),
(98823, 2019, 'bmc', 'Siberian larch (Larix sibirica Ledeb) chloroplast genome and development of polymorphic chloroplast markers', 'Bondar, Eugeniya I|Putintseva, Yuliya A|Oreshkova, Nataliya V|Krutovsky, Konstantin V', 'Laboratory of Forest Genomics, Genome Research and Education Center, Siberian Federal University, 660036, Krasnoyarsk, Russian Federation.|Laboratory of Forest Genomics, Genome Research and Education Center, Siberian Federal University, 660036, Krasnoyarsk, Russian Federation.|Laboratory of Forest Genomics, Genome Research and Education Center, Siberian Federal University, 660036, Krasnoyarsk, Russian Federation.|Laboratory of Forest Genomics, Genome Research and Education Center, Siberian Federal University, 660036, Krasnoyarsk, Russian Federation. konstantin.krutovsky@forst.uni-goettingen.de.', 'Russian Federation|Russian Federation|Russian Federation|Russian Federation', 5, 'Chloroplasts|Chromosome Mapping|Genetic Markers|Genome, Chloroplast|Larix|Microsatellite Repeats|Phylogeny|Polymorphism, Genetic|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/s12859-018-2571-x', '30717673', 'The main objectives of this study were sequencing, assembling, and annotation of chloroplast genome of one of the main Siberian boreal forest tree conifer species Siberian larch (Larix sibirica Ledeb.) and detection of polymorphic genetic markers - microsatellite loci or simple sequence repeats (SSRs) and single nucleotide polymorphisms (SNPs).'),
(98824, 2019, 'bmc', 'Development of electronic medical records for clinical and research purposes: the breast cancer module using an implementation framework in a middle income country- Malaysia', 'Mohd Nor, Nurul Aqilah|Taib, Nur Aishah|Saad, Marniza|Zaini, Hana Salwani|Ahmad, Zahir|Ahmad, Yamin|Dhillon, Sarinder Kaur', 'Data Science and Bioinformatics Laboratory, Institute of Biological Sciences, Faculty of Science, University of Malaya, 50603, Kuala Lumpur, Malaysia.|Department of Surgery, University Malaya Medical Centre, 50603, Kuala Lumpur, Malaysia. naisha@um.edu.my.|Department of Oncology, University Malaya Medical Centre, 50603, Kuala Lumpur, Malaysia.|Department of Information Technology, University Malaya Medical Centre, 50603, Kuala Lumpur, Malaysia.|Department of Information Technology, University Malaya Medical Centre, 50603, Kuala Lumpur, Malaysia.|Department of Information Technology, University Malaya Medical Centre, 50603, Kuala Lumpur, Malaysia.|Data Science and Bioinformatics Laboratory, Institute of Biological Sciences, Faculty of Science, University of Malaya, 50603, Kuala Lumpur, Malaysia. sarinder@um.edu.my.', 'Malaysia|Malaysia|Malaysia|Malaysia|Malaysia|Malaysia|Malaysia', 1, 'Biomedical Research|Breast Neoplasms|Data Accuracy|Developing Countries|Electronic Health Records|Female|Humans|Income|Information Storage and Retrieval|Malaysia|User-Computer Interface', 'Journal Article', '10.1186/s12859-018-2406-9', '30717675', 'Advances in medical domain has led to an increase of clinical data production which offers enhancement opportunities for clinical research sector. In this paper, we propose to expand the scope of Electronic Medical Records in the University Malaya Medical Center (UMMC) using different techniques in establishing interoperability functions between multiple clinical departments involving diagnosis, screening and treatment of breast cancer and building automatic systems for clinical audits as well as for potential data mining to enhance clinical breast cancer research in the future.'),
(98825, 2019, 'bmc', 'A new version of the ANDSystem tool for automatic extraction of knowledge from scientific publications with expanded functionality for reconstruction of associative gene networks by considering tissue-specific gene expression', 'Ivanisenko, Vladimir A|Demenkov, Pavel S|Ivanisenko, Timofey V|Mishchenko, Elena L|Saik, Olga V', 'Laboratory of Computer-Assisted Proteomics, Institute of Cytology and Genetics, Siberian Branch, Russian Academy of Sciences, Prospekt Lavrentyeva 10, Novosibirsk, 630090, Russia. salix@bionet.nsc.ru.|Laboratory of Computer-Assisted Proteomics, Institute of Cytology and Genetics, Siberian Branch, Russian Academy of Sciences, Prospekt Lavrentyeva 10, Novosibirsk, 630090, Russia.|Laboratory of Computer-Assisted Proteomics, Institute of Cytology and Genetics, Siberian Branch, Russian Academy of Sciences, Prospekt Lavrentyeva 10, Novosibirsk, 630090, Russia.|Laboratory of Computer-Assisted Proteomics, Institute of Cytology and Genetics, Siberian Branch, Russian Academy of Sciences, Prospekt Lavrentyeva 10, Novosibirsk, 630090, Russia.|Laboratory of Computer-Assisted Proteomics, Institute of Cytology and Genetics, Siberian Branch, Russian Academy of Sciences, Prospekt Lavrentyeva 10, Novosibirsk, 630090, Russia.', '-|-|-|-|-', 3, 'Apoptosis|Data Mining|Gene Expression Regulation|Gene Regulatory Networks|Humans|Organ Specificity|Publications|Semantics|Signal Transduction', 'Journal Article', '10.1186/s12859-018-2567-6', '30717676', 'Consideration of tissue-specific gene expression in reconstruction and analysis of molecular genetic networks is necessary for a proper description of the processes occurring in a specified tissue. Currently, there are a number of computer systems that allow the user to reconstruct molecular-genetic networks using the data automatically extracted from the texts of scientific publications. Examples of such systems are STRING, Pathway Commons, MetaCore and Ingenuity. The MetaCore and Ingenuity systems permit taking into account tissue-specific gene expression during the reconstruction of gene networks. Previously, we developed the ANDSystem tool, which also provides an automated extraction of knowledge from scientific texts and allows the reconstruction of gene networks. The main difference between our system and other tools is in the different types of interactions between objects, which makes the ANDSystem complementary to existing well-known systems. However, previous versions of the ANDSystem did not contain any information on tissue-specific expression.'),
(98826, 2019, 'bmc', 'Application of the geographic population structure (GPS) algorithm for biogeographical analyses of wild and captive gorillas', 'Das, Ranajit|Upadhyai, Priyanka', 'Manipal Centre for Natural Sciences (MCNS), Manipal Academy of Higher Education (MAHE), University building, Lab 11, Madhav Nagar, Manipal, Karnataka, 576104, India. ranajit.das@manipal.edu.|Department of Medical Genetics, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, Karnataka, India.', 'India|India', 0, 'Algorithms|Animals|Cluster Analysis|Gene Pool|Genetics, Population|Genome|Gorilla gorilla|Phylogeography|Population Dynamics|Principal Component Analysis', 'Journal Article', '10.1186/s12859-018-2568-5', '30717677', 'The utilization of high resolution genome data has important implications for the phylogeographical evaluation of non-human species. Biogeographical analyses can yield detailed understanding of their population biology and facilitate the geo-localization of individuals to promote their efficacious management, particularly when bred in captivity. The Geographic Population Structure (GPS) algorithm is an admixture based tool for inference of biogeographical affinities and has been employed for the geo-localization of various human populations worldwide. Here, we applied the GPS tool for biogeographical analyses and localization of the ancestral origins of wild and captive gorilla genomes, of unknown geographic source, available in the Great Ape Genome Project (GAGP), employing Gorillas with known ancestral origin as the reference data.'),
(98827, 2019, 'bmc', 'DeepPVP: phenotype-based prioritization of causative variants using deep learning', 'Boudellioua, Imane|Kulmanov, Maxat|Schofield, Paul N|Gkoutos, Georgios V|Hoehndorf, Robert', 'Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology, 4700 KAUST, Thuwal, 23955-6900, Kingdom of Saudi Arabia.|Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology, 4700 KAUST, Thuwal, 23955-6900, Kingdom of Saudi Arabia.|Department of Physiology, Development & Neuroscience, University of Cambridge, Downing Street, Cambridge, CB2 3EG, UK.|College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, Birmingham, B15 2TT, UK.|Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology, 4700 KAUST, Thuwal, 23955-6900, Kingdom of Saudi Arabia. robert.hoehndorf@kaust.edu.sa.', 'Saudi Arabia|Saudi Arabia|United Kingdom|United Kingdom|Saudi Arabia', 7, 'Deep Learning|Exome|Gene Frequency|Genetic Variation|Humans|Neural Networks (Computer)|Phenotype|Software|Whole Exome Sequencing', 'Journal Article', '10.1186/s12859-019-2633-8', '30727941', 'Prioritization of variants in personal genomic data is a major challenge. Recently, computational methods that rely on comparing phenotype similarity have shown to be useful to identify causative variants. In these methods, pathogenicity prediction is combined with a semantic similarity measure to prioritize not only variants that are likely to be dysfunctional but those that are likely involved in the pathogenesis of a patients phenotype.'),
(98828, 2019, 'bmc', 'Shambhala: a platform-agnostic data harmonizer for gene expression data', 'Borisov, Nicolas|Shabalina, Irina|Tkachev, Victor|Sorokin, Maxim|Garazha, Andrew|Pulin, Andrey|Eremin, Ilya I|Buzdin, Anton', 'I.M. Sechenov First Moscow State Medical University, Sechenov University, Moscow, 119991, Russia. borisov@oncobox.com.|Faculty of Mathematics and Information Technologies, Petrozavodsk State University, Anokhina str., 20, Petrozavodsk, 185910, Russia.|Department of bioinformatics and molecular networks, OmicsWay Corporation, Walnut, CA, USA.|I.M. Sechenov First Moscow State Medical University, Sechenov University, Moscow, 119991, Russia.|Department of bioinformatics and molecular networks, OmicsWay Corporation, Walnut, CA, USA.|Laboratory for Cell Biology and Developmental Pathology, Federal State Institution Institute of General Pathology and Pathophysiology, FSBSI IGPP, Moscow, Russia.|Department for Regenerative Medicine, JSC Generium, Moscow, Russia.|I.M. Sechenov First Moscow State Medical University, Sechenov University, Moscow, 119991, Russia.', 'United States|United States|United States|United States|United States|United States|United States|United States', 11, 'Cluster Analysis|Gene Expression Profiling|Gene Expression Regulation|Humans|Reproducibility of Results|Software', 'Journal Article', '10.1186/s12859-019-2641-8', '30727942', 'Harmonization techniques make different gene expression profiles and their sets compatible and ready for comparisons. Here we present a new bioinformatic tool termed Shambhala for harmonization of multiple human gene expression datasets obtained using different experimental methods and platforms of microarray hybridization and RNA sequencing.'),
(98829, 2019, 'bmc', 'Estimation of duplication history under a stochastic model for tandem repeats', 'Farnoud, Farzad|Schwartz, Moshe|Bruck, Jehoshua', 'Department of Electrical and Computer Engineering, Department of Computer Science, University of Virginia, Charlottesville, USA. farzad@virginia.edu.|Department of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer Sheva, Israel.|Department of Electrical Engineering, California Institute of Technology, Pasadena, USA.', 'United States|Israel|United States', 7, 'Algorithms|Chromosomes, Human, X|Computer Simulation|Gene Duplication|Genome, Human|Humans|Monte Carlo Method|Mutation|Mutation Rate|Stochastic Processes|Tandem Repeat Sequences', 'Journal Article', '10.1186/s12859-019-2603-1', '30727948', 'Tandem repeat sequences are common in the genomes of many organisms and are known to cause important phenomena such as gene silencing and rapid morphological changes. Due to the presence of multiple copies of the same pattern in tandem repeats and their high variability, they contain a wealth of information about the mutations that have led to their formation. The ability to extract this information can enhance our understanding of evolutionary mechanisms.'),
(98830, 2019, 'bmc', 'CERENKOV2: improved detection of functional noncoding SNPs using data-space geometric features', 'Yao, Yao|Liu, Zheng|Wei, Qi|Ramsey, Stephen A', 'School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, 97330, OR, USA.|School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, 97330, OR, USA.|School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, 97330, OR, USA.|School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, 97330, OR, USA. stephen.ramsey@oregonstate.edu.', 'United States|United States|United States|United States', 2, 'DNA, Intergenic|Genetic Loci|Genome-Wide Association Study|Humans|Machine Learning|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1186/s12859-019-2637-4', '30727967', 'We previously reported on CERENKOV, an approach for identifying regulatory single nucleotide polymorphisms (rSNPs) that is based on 246 annotation features. CERENKOV uses the xgboost classifier and is designed to be used to find causal noncoding SNPs in loci identified by genome-wide association studies (GWAS). We reported that CERENKOV has state-of-the-art performance (by two traditional measures and a novel GWAS-oriented measure, AVGRANK) in a comparison to nine other tools for identifying functional noncoding SNPs, using a comprehensive reference SNP set (OSU17, 15,331 SNPs). Given that SNPs are grouped within loci in the reference SNP set and given the importance of the data-space manifold geometry for machine-learning model selection, we hypothesized that within-locus inter-SNP distances would have class-based distributional biases that could be exploited to improve rSNP recognition accuracy. We thus defined an intralocus SNP radius as the average data-space distance from a SNP to the other intralocus neighbors, and explored radius likelihoods for five distance measures.'),
(98831, 2019, 'bmc', 'CeModule: an integrative framework for discovering regulatory patterns from genomic data in cancer', 'Xiao, Qiu|Luo, Jiawei|Liang, Cheng|Cai, Jie|Li, Guanghui|Cao, Buwen', 'College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China.|College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China. luojiawei@hnu.edu.cn.|College of Information Science and Engineering, Shandong Normal University, Jinan, 250000, China.|College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China.|College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China.|College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China.', 'China|China|China|China|China|China', 4, 'Algorithms|Databases, Genetic|Female|Gene Expression Regulation, Neoplastic|Gene Ontology|Gene Regulatory Networks|Genomics|Humans|MicroRNAs|Neoplasms|Ovarian Neoplasms|RNA, Long Noncoding|RNA, Messenger|Reproducibility of Results', 'Journal Article', '10.1186/s12859-019-2654-3', '30732558', 'Non-coding RNAs (ncRNAs) are emerging as key regulators and play critical roles in a wide range of tumorigenesis. Recent studies have suggested that long non-coding RNAs (lncRNAs) could interact with microRNAs (miRNAs) and indirectly regulate miRNA targets through competing interactions. Therefore, uncovering the competing endogenous RNA (ceRNA) regulatory mechanism of lncRNAs, miRNAs and mRNAs in post-transcriptional level will aid in deciphering the underlying pathogenesis of human polygenic diseases and may unveil new diagnostic and therapeutic opportunities. However, the functional roles of vast majority of cancer specific ncRNAs and their combinational regulation patterns are still insufficiently understood.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(98832, 2019, 'bmc', 'Predicting protein functions by applying predicate logic to biomedical literature', 'Taha, Kamal|Iraqi, Youssef|Al Aamri, Amira', 'Department of Electrical and Computer Engineering, Khalifa University, Abu Dhabi, United Arab Emirates. kamal.taha@ku.ac.ae.|Department of Electrical and Computer Engineering, Khalifa University, Abu Dhabi, United Arab Emirates.|Department of Electrical and Computer Engineering, Khalifa University, Abu Dhabi, United Arab Emirates.', 'United Arab Emirates|United Arab Emirates|United Arab Emirates', 2, 'Databases, Genetic|Gene Ontology|Genome, Fungal|Information Storage and Retrieval|Logic|Molecular Sequence Annotation|Proteins|Publications|Reproducibility of Results|Saccharomyces cerevisiae', 'Journal Article', '10.1186/s12859-019-2594-y', '30736739', 'A large number of computational methods have been proposed for predicting protein functions. The underlying techniques adopted by most of these methods revolve around predicting the functions of an unannotated protein p from already annotated proteins that have similar characteristics as p. Recent Information Extraction methods take advantage of the huge growth of biomedical literature to predict protein functions. They extract biological molecule terms that directly describe protein functions from biomedical texts. However, they consider only explicitly mentioned terms that co-occur with proteins in texts. We observe that some important biological molecule terms pertaining functional categories may implicitly co-occur with proteins in texts. Therefore, the methods that rely solely on explicitly mentioned terms in texts may miss vital functional information implicitly mentioned in the texts.'),
(98833, 2019, 'bmc', 'Predicting clinically promising therapeutic hypotheses using tensor factorization', 'Yao, Jin|Hurle, Mark R|Nelson, Matthew R|Agarwal, Pankaj', 'Computational Biology, GSK R&D, 1250 S. Collegeville Road, UP12-200, Collegeville, PA, USA. jin.8.yao@gsk.com.|Computational Biology, GSK R&D, 1250 S. Collegeville Road, UP12-200, Collegeville, PA, USA.|Genetics, GSK R&D, 1250 S. Collegeville Road, UP12-200, Collegeville, PA, USA.|Computational Biology, GSK R&D, 1250 S. Collegeville Road, UP12-200, Collegeville, PA, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Bayes Theorem|Benchmarking|Clinical Trials as Topic|Drug Delivery Systems|Drug Discovery|Humans|Logistic Models|Models, Theoretical', 'Journal Article', '10.1186/s12859-019-2664-1', '30736745', 'Determining which target to pursue is a challenging and error-prone first step in developing a therapeutic treatment for a disease, where missteps are potentially very costly given the long-time frames and high expenses of drug development. With current informatics technology and machine learning algorithms, it is now possible to computationally discover therapeutic hypotheses by predicting clinically promising drug targets based on the evidence associating drug targets with disease indications. We have collected this evidence from Open Targets and additional databases that covers 17 sources of evidence for target-indication association and represented the data as a tensor of 21,437xe2x80x89xc3x97xe2x80x892211xe2x80x89xc3x97xe2x80x8917.'),
(98834, 2019, 'bmc', 'Genomic prediction of tuberculosis drug-resistance: benchmarking existing databases and prediction algorithms', 'Ngo, Tra-My|Teo, Yik-Ying', 'NUS Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore, 119077, Singapore.|NUS Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore, 119077, Singapore. statyy@nus.edu.sg.', 'Singapore|Singapore', 5, 'Algorithms|Antitubercular Agents|Benchmarking|Calibration|Databases, Genetic|Drug Resistance, Bacterial|Genomics|Humans|Microbial Sensitivity Tests|Mycobacterium tuberculosis|Sensitivity and Specificity|Software|Tuberculosis', 'Journal Article', '10.1186/s12859-019-2658-z', '30736750', 'It is possible to predict whether a tuberculosis (TB) patient will fail to respond to specific antibiotics by sequencing the genome of the infecting Mycobacterium tuberculosis (Mtb) and observing whether the pathogen carries specific mutations at drug-resistance sites. This advancement has led to the collation of TB databases such as PATRIC and ReSeqTB that possess both whole genome sequences and drug resistance phenotypes of infecting Mtb isolates. Bioinformatics tools have also been developed to predict drug resistance from whole genome sequencing (WGS) data. Here, we evaluate the performance of four popular tools (TBProfiler, MyKrobe, KvarQ, PhyResSE) with 6746 isolates compiled from publicly available databases, and subsequently identify highly probable phenotyping errors in the databases by genetically predicting the drug phenotypes using all four software.'),
(98835, 2019, 'bmc', 'Analyzing a co-occurrence gene-interaction network to identify disease-gene association', 'Al-Aamri, Amira|Taha, Kamal|Al-Hammadi, Yousof|Maalouf, Maher|Homouz, Dirar', 'Department of Electrical and Computer Engineering, Abu Dhabi, United Arab Emirates.|Department of Electrical and Computer Engineering, Abu Dhabi, United Arab Emirates.|Department of Electrical and Computer Engineering, Abu Dhabi, United Arab Emirates.|Department of Industrial and Systems Engineering, Abu Dhabi, United Arab Emirates.|Department of Physics, Khalifa University of Science and Technology, Abu Dhabi, P.O. Box 127788,, United Arab Emirates. dirar.homouz@ku.ac.ae.', 'United Arab Emirates|United Arab Emirates|United Arab Emirates|United Arab Emirates|United Arab Emir', 7, 'Epistasis, Genetic|Gene Regulatory Networks|Genetic Predisposition to Disease|Humans|Logistic Models|Male|Prostatic Neoplasms|ROC Curve', 'Journal Article', '10.1186/s12859-019-2634-7', '30736752', 'Understanding the genetic networks and their role in chronic diseases (e.g., cancer) is one of the important objectives of biological researchers. In this work, we present a text mining system that constructs a gene-gene-interaction network for the entire human genome and then performs network analysis to identify disease-related genes. We recognize the interacting genes based on their co-occurrence frequency within the biomedical literature and by employing linear and non-linear rare-event classification models. We analyze the constructed network of genes by using different network centrality measures to decide on the importance of each gene. Specifically, we apply betweenness, closeness, eigenvector, and degree centrality metrics to rank the central genes of the network and to identify possible cancer-related genes.'),
(98836, 2019, 'bmc', 'IMMAN: an R/Bioconductor package for Interolog protein network reconstruction, mapping and mining analysis', 'Ashtiani, Minoo|Nickchi, Payman|Jahangiri-Tazehkand, Soheil|Safari, Abdollah|Mirzaie, Mehdi|Jafari, Mohieddin', 'School of Biological Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.|School of Biological Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.|School of Biological Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.|Department of Statistics and Actuarial Science, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada. a.saffari72@gmail.com.|Department of Applied Mathematics, Faculty of Mathematical Sciences, Tarbiat Modares University, Tehran, Iran. mirzaie@modares.ac.ir.|School of Biological Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran. mohieddin.jafari@helsinki.fi.', '-|-|-|Canada|-|-', 0, 'Animals|Benchmarking|Data Mining|Humans|Protein Interaction Mapping|Protein Interaction Maps|Software', 'Journal Article', '10.1186/s12859-019-2659-y', '30755155', 'Reconstruction of protein-protein interaction networks (PPIN) has been riddled with controversy for decades. Particularly, false-negative and -positive interactions make this progress even more complicated. Also, lack of a standard PPIN limits us in the comparison studies and results in the incompatible outcomes. Using an evolution-based concept, i.e. interolog which refers to interacting orthologous protein sets, pave the way toward an optimal benchmark.'),
(98837, 2019, 'bmc', 'From POS tagging to dependency parsing for biomedical event extraction', 'Nguyen, Dat Quoc|Verspoor, Karin', 'School of Computing and Information Systems, The University of Melbourne, Melbourne, Australia. dqnguyen@unimelb.edu.au.|School of Computing and Information Systems, The University of Melbourne, Melbourne, Australia.', 'Australia|Australia', 7, 'Algorithms|Biomedical Research|Humans|Information Storage and Retrieval|Natural Language Processing|Neural Networks (Computer)|Publications|Speech|Vocabulary', 'Journal Article', '10.1186/s12859-019-2604-0', '30755172', 'Given the importance of relation or event extraction from biomedical research publications to support knowledge capture and synthesis, and the strong dependency of approaches to this information extraction task on syntactic information, it is valuable to understand which approaches to syntactic processing of biomedical text have the highest performance.'),
(98838, 2019, 'bmc', 'Disease Pathway Cut for Multi-Target drugs', 'Bang, Sunjoo|Son, Sangjoon|Kim, Sooyoung|Shin, Hyunjung', 'Department of Industrial Engineering, Ajou University, 206, World cup-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, 16499, Republic of Korea.|Department of Psychiatry, Ajou University School of Medicine, 206, World cup-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, 16499, Republic of Korea.|Department of Surgery, Thyroid Cancer Center, Gangnam Severance Hospital, Institute of Refractory Thyroid Cancer, Yonsei University College of Medicine, 211, Eonju-ro, Gangnam-gu, Seoul, Republic of Korea.|Department of Industrial Engineering, Ajou University, 206, World cup-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, 16499, Republic of Korea. shin@ajou.ac.kr.', '-|-|-|-', 1, 'Algorithms|Disease|Disease Progression|Drug Development|Humans|Software', 'Journal Article', '10.1186/s12859-019-2638-3', '30760209', 'Biomarker discovery studies have been moving the focus from a single target gene to a set of target genes. However, the number of target genes in a drug should be minimum to avoid drug side-effect or toxicity. But still, the set of target genes should effectively block all possible paths of disease progression.'),
(98839, 2019, 'bmc', 'WGDdetector: a pipeline for detecting whole genome duplication events using the genome or transcriptome annotations', 'Yang, Yongzhi|Li, Ying|Chen, Qiao|Sun, Yongshuai|Lu, Zhiqiang', 'CAS Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla, 666303, Yunnan, China.|State Key Laboratory of Grassland Agro-Ecosystem, College of Life Sciences, Lanzhou University, Lanzhou, China.|State Key Laboratory of Grassland Agro-Ecosystem, College of Life Sciences, Lanzhou University, Lanzhou, China.|CAS Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla, 666303, Yunnan, China. sunyongshuai@xtbg.ac.cn.|CAS Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla, 666303, Yunnan, China. luzhiqiang@xtbg.ac.cn.', 'China|China|China|China|China', 0, 'Animals|Arabidopsis|Gene Duplication|Genome|Genomics|Juglans|Molecular Sequence Annotation|Populus|Software|Transcriptome|Xenopus laevis', 'Journal Article', '10.1186/s12859-019-2670-3', '30760221', 'With the availability of well-assembled genomes of a growing number of organisms, identifying the bioinformatic basis of whole genome duplication (WGD) is a growing field of genomics. The most extant software for detecting footprints of WGDs has been restricted to a well-assembled genome. However, the massive poor quality genomes and the more accessible transcriptomes have been largely ignored, and in theoretically they are also likely to contribute to detect WGD using dS based method. Here, to resolve these problems, we have designed a universal and simple technical tool WGDdetector for detecting WGDs using either genome or transcriptome annotations in different organisms based on the widely used dS based method.'),
(98840, 2019, 'bmc', 'ADS-HCSpark: A scalable HaplotypeCaller leveraging adaptive data segmentation to accelerate variant calling on Spark', 'Xiao, Anghong|Wu, Zongze|Dong, Shoubin', 'Communication & Computer Network Lab of Guangdong, School of Computer Science & Engineering, South China University of Technology, Wushan Road, Guangzhou, 510641, China.|Communication & Computer Network Lab of Guangdong, School of Computer Science & Engineering, South China University of Technology, Wushan Road, Guangzhou, 510641, China.|Communication & Computer Network Lab of Guangdong, School of Computer Science & Engineering, South China University of Technology, Wushan Road, Guangzhou, 510641, China. sbdong@scut.edu.cn.', 'China|China|China', 0, 'Algorithms|Databases, Genetic|Genetic Variation|Genome|Haplotypes|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software|Time Factors', 'Journal Article', '10.1186/s12859-019-2665-0', '30764760', 'The advance of next generation sequencing enables higher throughput with lower price, and as the basic of high-throughput sequencing data analysis, variant calling is widely used in disease research, clinical treatment and medicine research. However, current mainstream variant caller tools have a serious problem of computation bottlenecks, resulting in some long tail tasks when performing on large datasets. This prevents high scalability on clusters of multi-node and multi-core, and leads to long runtime and inefficient usage of computing resources. Thus, a high scalable tool which could run in distributed environment will be highly useful to accelerate variant calling on large scale genome data.'),
(98841, 2019, 'bmc', 'MultiDomainBenchmark: a multi-domain query and subject database suite', 'Carroll, Hyrum D|Spouge, John L|Gonzalez, Mileidy', 'TSYS School of Computer Science, Columbus State University, 4225 University Avenue, Columbus, 31907, GA, USA. carroll_hyrum@columbusstate.edu.|National Center for Biotechnology Information, Bethesda, National Institutes of Health, 8600 Rockville Pike, Bethesda, 20894, MD, USA.|National Center for Biotechnology Information, Bethesda, National Institutes of Health, 8600 Rockville Pike, Bethesda, 20894, MD, USA.', 'United States|United States|United States', 0, 'Algorithms|Amino Acid Sequence|Benchmarking|Databases, Protein|Phylogeny|Protein Structure, Tertiary|Proteins|Sequence Alignment', 'Journal Article', '10.1186/s12859-019-2660-5', '30764761', 'Genetic sequence database retrieval benchmarks play an essential role in evaluating the performance of sequence searching tools. To date, all phylogenetically diverse benchmarks known to the authors include only query sequences with single protein domains. Domains are the primary building blocks of protein structure and function. Independently, each domain can fulfill a single function, but most proteins (>80% in Metazoa) exist as multi-domain proteins. Multiple domain units combine in various arrangements or architectures to create different functions and are often under evolutionary pressures to yield new ones. Thus, it is crucial to create gold standards reflecting the multi-domain complexity of real proteins to more accurately evaluate sequence searching tools.'),
(98842, 2019, 'bmc', 'FunMappOne: a tool to hierarchically organize and visually navigate functional gene annotations in multiple experiments', 'Scala, Giovanni|Serra, Angela|Marwah, Veer Singh|Saarimäki, Laura Aliisa|Greco, Dario', 'Faculty of Medicine and Life Sciences, University of Tampere, Arvo Ylpön katu 34 - Arvo building, Tampere, FI-33014, Finland.|Faculty of Medicine and Life Sciences, University of Tampere, Arvo Ylpön katu 34 - Arvo building, Tampere, FI-33014, Finland.|Faculty of Medicine and Life Sciences, University of Tampere, Arvo Ylpön katu 34 - Arvo building, Tampere, FI-33014, Finland.|Faculty of Medicine and Life Sciences, University of Tampere, Arvo Ylpön katu 34 - Arvo building, Tampere, FI-33014, Finland.|Faculty of Medicine and Life Sciences, University of Tampere, Arvo Ylpön katu 34 - Arvo building, Tampere, FI-33014, Finland.', 'Finland|Finland|Finland|Finland|Finland', 5, 'Animals|Computational Biology|Computer Graphics|Databases, Factual|Gene Ontology|Genes|Humans|Mice|Molecular Sequence Annotation|Software', 'Journal Article', '10.1186/s12859-019-2639-2', '30767762', 'Functional annotation of genes is an essential step in omics data analysis. Multiple databases and methods are currently available to summarize the functions of sets of genes into higher level representations, such as ontologies and molecular pathways. Annotating results from omics experiments into functional categories is essential not only to understand the underlying regulatory dynamics but also to compare multiple experimental conditions at a higher level of abstraction. Several tools are already available to the community to represent and compare functional profiles of omics experiments. However, when the number of experiments and/or enriched functional terms is high, it becomes difficult to interpret the results even when graphically represented. Therefore, there is currently a need for interactive and user-friendly tools to graphically navigate and further summarize annotations in order to facilitate results interpretation also when the dimensionality is high.'),
(98843, 2019, 'bmc', 'BioVR: a platform for virtual reality assisted biological data integration and visualization', 'Zhang, Jimmy F|Paciorkowski, Alex R|Craig, Paul A|Cui, Feng', 'Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, One Lomb Memorial Drive, Rochester, NY, 14623, USA. jfz8009@rit.edu.|Departments of Neurology, Pediatrics, Biomedical Genetics, and Neuroscience, University of Rochester Medical Center, 601 Elmwood Ave, Rochester, NY, 14642, USA.|School of Chemistry and Materials Science, Rochester Institute of Technology, One Lomb Memorial Drive, Rochester, NY, 14623, USA.|Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, One Lomb Memorial Drive, Rochester, NY, 14623, USA. fxcsbi@rit.edu.', 'United States|United States|United States|United States', 0, 'Computer Graphics|DNA|Humans|Models, Biological|Protein Conformation|Proteins|RNA|Software|User-Computer Interface|Virtual Reality', 'Journal Article', '10.1186/s12859-019-2666-z', '30767777', 'Functional characterization of single nucleotide variants (SNVs) involves two steps, the first step is to convert DNA to protein and the second step is to visualize protein sequences with their structures. As massively parallel sequencing has emerged as a leading technology in genomics, resulting in a significant increase in data volume, direct visualization of SNVs together with associated protein sequences/structures in a new user interface (UI) would be a more effective way to assess their potential effects on protein function.'),
(98844, 2019, 'bmc', 'Iterative unsupervised domain adaptation for generalized cell detection from brightfield z-stacks', 'Liimatainen, Kaisa|Kananen, Lauri|Latonen, Leena|Ruusuvuori, Pekka', 'Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.|Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.|Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.|Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland. pekka.ruusuvuori@tuni.fi.', 'Finland|Finland|Finland|Finland', 0, 'Deep Learning|Humans|Image Processing, Computer-Assisted|Male|Prostatic Neoplasms|Tumor Cells, Cultured', 'Journal Article', '10.1186/s12859-019-2605-z', '30767778', 'Cell counting from cell cultures is required in multiple biological and biomedical research applications. Especially, accurate brightfield-based cell counting methods are needed for cell growth analysis. With deep learning, cells can be detected with high accuracy, but manually annotated training data is required. We propose a method for cell detection that requires annotated training data for one cell line only, and generalizes to other, unseen cell lines.'),
(98845, 2019, 'bmc', 'Functional Heatmap: an automated and interactive pattern recognition tool to integrate time with multi-omics assays', 'Williams, Joshua R|Yang, Ruoting|Clifford, John L|Watson, Daniel|Campbell, Ross|Getnet, Derese|Kumar, Raina|Hammamieh, Rasha|Jett, Marti', 'Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, 21702-5010, USA.|Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, 21702-5010, USA.|Integrative Systems Biology Program, US Army Center for Environmental Health Research, Fort Detrick, Frederick, MD, 21702-5010, USA.|Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, 21702-5010, USA.|Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, 21702-5010, USA.|Integrative Systems Biology Program, US Army Center for Environmental Health Research, Fort Detrick, Frederick, MD, 21702-5010, USA.|Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, 21702-5010, USA.|Integrative Systems Biology Program, US Army Center for Environmental Health Research, Fort Detrick, Frederick, MD, 21702-5010, USA.|Integrative Systems Biology Program, US Army Center for Environmental Health Research, Fort Detrick, Frederick, MD, 21702-5010, USA. marti.jett-tilton.civ@mail.mil.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 2, 'Computational Biology|Gene Expression Profiling|Gene Expression Regulation|Humans|Pattern Recognition, Automated|Radiation, Ionizing|Skin|Software|Time Factors|Transcriptome', 'Journal Article', '10.1186/s12859-019-2657-0', '30770734', 'Life science research is moving quickly towards large-scale experimental designs that are comprised of multiple tissues, time points, and samples. Omic time-series experiments offer answers to three big questions: what collective patterns do most analytes follow, which analytes follow an identical pattern or synchronize across multiple cohorts, and how do biological functions evolve over time. Existing tools fall short of robustly answering and visualizing all three questions in a unified interface.'),
(98846, 2019, 'bmc', 'Parameter estimation in models of biological oscillators: an automated regularised estimation approach', 'Pitt, Jake Alan|Banga, Julio R', '(Bio)Process Engineering Group, IIM-CSIC, Eduardo Cabello 6, Vigo, 36208, Spain.|(Bio)Process Engineering Group, IIM-CSIC, Eduardo Cabello 6, Vigo, 36208, Spain. julio@iim.csic.es.', 'Spain|Spain', 0, 'Algorithms|Biological Clocks|Calibration|Humans|Metabolic Networks and Pathways|Models, Biological|Signal Transduction|Systems Biology', 'Journal Article', '10.1186/s12859-019-2630-y', '30770736', 'Dynamic modelling is a core element in the systems biology approach to understanding complex biosystems. Here, we consider the problem of parameter estimation in models of biological oscillators described by deterministic nonlinear differential equations. These problems can be extremely challenging due to several common pitfalls: (i) a lack of prior knowledge about parameters (i.e. massive search spaces), (ii) convergence to local optima (due to multimodality of the cost function), (iii) overfitting (fitting the noise instead of the signal) and (iv) a lack of identifiability. As a consequence, the use of standard estimation methods (such as gradient-based local ones) will often result in wrong solutions. Overfitting can be particularly problematic, since it produces very good calibrations, giving the impression of an excellent result. However, overfitted models exhibit poor predictive power. Here, we present a novel automated approach to overcome these pitfalls. Its workflow makes use of two sequential optimisation steps incorporating three key algorithms: (1) sampling strategies to systematically tighten the parameter bounds reducing the search space, (2) efficient global optimisation to avoid convergence to local solutions, (3) an advanced regularisation technique to fight overfitting. In addition, this workflow incorporates tests for structural and practical identifiability.'),
(98847, 2019, 'bmc', 'Statistical assessment and visualization of synergies for large-scale sparse drug combination datasets', 'Amzallag, Arnaud|Ramaswamy, Sridhar|Benes, Cyril H', 'The Center of Cancer Research, Massachusetts General Hospital, 149 13th Street, Charlestown, MA, 02129, USA. arnaud.amzallag@gmail.com.|The Center of Cancer Research, Massachusetts General Hospital, 149 13th Street, Charlestown, MA, 02129, USA.|The Center of Cancer Research, Massachusetts General Hospital, 149 13th Street, Charlestown, MA, 02129, USA. cbenes@mgh.harvard.edu.', 'United States|United States|United States', 4, 'Antineoplastic Combined Chemotherapy Protocols|Cell Line, Tumor|Computer Graphics|Datasets as Topic|Drug Evaluation, Preclinical|Drug Synergism|Humans|Linear Models|Models, Statistical', 'Journal Article', '10.1186/s12859-019-2642-7', '30777010', 'Drug combinations have the potential to improve efficacy while limiting toxicity. To robustly identify synergistic combinations, high-throughput screens using full dose-response surface are desirable but require an impractical number of data points. Screening of a sparse number of doses per drug allows to screen large numbers of drug pairs, but complicates statistical assessment of synergy. Furthermore, since the number of pairwise combinations grows with the square of the number of drugs, exploration of large screens necessitates advanced visualization tools.'),
(98848, 2019, 'bmc', 'CirGO: an alternative circular way of visualising gene ontology terms', 'Kuznetsova, Irina|Lugmayr, Artur|Siira, Stefan J|Rackham, Oliver|Filipovska, Aleksandra', 'Harry Perkins Institute of Medical Research, Nedlands, Western Australia, 6009, Australia. irina.kuznetsova@perkins.uwa.edu.au.|Visualisation and Interactive Media (VisLab), Curtin University, Perth, 6102, Australia.|Harry Perkins Institute of Medical Research, Nedlands, Western Australia, 6009, Australia.|Harry Perkins Institute of Medical Research, Nedlands, Western Australia, 6009, Australia.|Harry Perkins Institute of Medical Research, Nedlands, Western Australia, 6009, Australia. aleksandra.filipovska@perkins.uwa.edu.au.', 'Australia|Australia|Australia|Australia|Australia', 0, 'Algorithms|Cluster Analysis|Computer Graphics|Gene Expression|Gene Ontology|Humans|Software', 'Journal Article', '10.1186/s12859-019-2671-2', '30777018', 'Prioritisation of gene ontology terms from differential gene expression analyses in a two-dimensional format remains a challenge with exponentially growing data volumes. Typically, gene ontology terms are represented as tree-maps that enclose all data into defined space. However, large datasets make this type of visualisation appear cluttered and busy, and often not informative as some labels are omitted due space limits, especially when published in two-dimensional (2D) figures.'),
(98849, 2019, 'bmc', 'DeepUbi: a deep learning framework for prediction of ubiquitination sites in proteins', 'Fu, Hongli|Yang, Yingxi|Wang, Xiaobo|Wang, Hui|Xu, Yan', 'Department of Information and Computing Science, University of Science and Technology Beijing, Beijing, 100083, China.|Department of Information and Computing Science, University of Science and Technology Beijing, Beijing, 100083, China.|Department of Information and Computing Science, University of Science and Technology Beijing, Beijing, 100083, China.|Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China.|Department of Information and Computing Science, University of Science and Technology Beijing, Beijing, 100083, China. xuyan@ustb.edu.cn.', 'China|China|China|China|China', 9, 'Deep Learning|Humans|Lysine|Neural Networks (Computer)|Proteome|Proteomics|Software|Ubiquitinated Proteins|Ubiquitination', 'Journal Article', '10.1186/s12859-019-2677-9', '30777029', 'Protein ubiquitination occurs when the ubiquitin protein binds to a target protein residue of lysine (K), and it is an important regulator of many cellular functions, such as signal transduction, cell division, and immune reactions, in eukaryotes. Experimental and clinical studies have shown that ubiquitination plays a key role in several human diseases, and recent advances in proteomic technology have spurred interest in identifying ubiquitination sites. However, most current computing tools for predicting target sites are based on small-scale data and shallow machine learning algorithms.'),
(98850, 2019, 'bmc', 'Identifying cancer prognostic modules by module network analysis', 'Zhou, Xiong-Hui|Chu, Xin-Yi|Xue, Gang|Xiong, Jiang-Hui|Zhang, Hong-Yu', 'Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, Peoples Republic of China.|Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, Peoples Republic of China.|Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, Peoples Republic of China.|State Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and Training Center, Beijing, Peoples Republic of China.|Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, Peoples Republic of China. zhy630@mail.hzau.edu.cn.', 'China|China|China|China|China', 0, 'Algorithms|Female|Gene Expression Profiling|Gene Regulatory Networks|Humans|Neoplasms|Prognosis|Survival Analysis', 'Journal Article', '10.1186/s12859-019-2674-z', '30777030', 'The identification of prognostic genes that can distinguish the prognostic risks of cancer patients remains a significant challenge. Previous works have proven that functional gene sets were more reliable for this task than the gene signature. However, few works have considered the cross-talk among functional gene sets, which may result in neglecting important prognostic gene sets for cancer.'),
(98851, 2019, 'bmc', 'SLIM: a flexible web application for the reproducible processing of environmental DNA metabarcoding data', 'Dufresne, Yoann|Lejzerowicz, Franck|Perret-Gentil, Laure Apotheloz|Pawlowski, Jan|Cordier, Tristan', 'Department of Genetics and Evolution, University of Geneva, Science III, 4 Boulevard dYvoy, 1205, Geneva, Switzerland.|Department of Genetics and Evolution, University of Geneva, Science III, 4 Boulevard dYvoy, 1205, Geneva, Switzerland.|Department of Genetics and Evolution, University of Geneva, Science III, 4 Boulevard dYvoy, 1205, Geneva, Switzerland.|Department of Genetics and Evolution, University of Geneva, Science III, 4 Boulevard dYvoy, 1205, Geneva, Switzerland.|Department of Genetics and Evolution, University of Geneva, Science III, 4 Boulevard dYvoy, 1205, Geneva, Switzerland. tristan.cordier@gmail.com.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 0, 'Algorithms|DNA Barcoding, Taxonomic|Internet|Reproducibility of Results|Software|User-Computer Interface', 'Journal Article', '10.1186/s12859-019-2663-2', '30782112', 'High-throughput amplicon sequencing of environmental DNA (eDNA metabarcoding) has become a routine tool for biodiversity survey and ecological studies. By including sample-specific tags in the primers prior PCR amplification, it is possible to multiplex hundreds of samples in a single sequencing run. The analysis of millions of sequences spread into hundreds to thousands of samples prompts for efficient, automated yet flexible analysis pipelines. Various algorithms and software have been developed to perform one or multiple processing steps, such as paired-end reads assembly, chimera filtering, Operational Taxonomic Unit (OTU) clustering and taxonomic assignment. Some of these software are now well established and widely used by scientists as part of their workflow. Wrappers that are capable to process metabarcoding data from raw sequencing data to annotated OTU-to-sample matrix were also developed to facilitate the analysis for non-specialist users. Yet, most of them require basic bioinformatic or command-line knowledge, which can limit the accessibility to such integrative toolkits. Furthermore, for flexibility reasons, these tools have adopted a step-by-step approach, which can prevent an easy automation of the workflow, and hence hamper the analysis reproducibility.'),
(98852, 2019, 'bmc', 'Prediction of lncRNA-disease associations by integrating diverse heterogeneous information sources with RWR algorithm and positive pointwise mutual information', 'Fan, Xiao-Nan|Zhang, Shao-Wu|Zhang, Song-Yao|Zhu, Kunju|Lu, Songjian', 'Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, 127 West Youyi Road, Xian, 710072, Shaanxi, China.|Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, 127 West Youyi Road, Xian, 710072, Shaanxi, China. zhangsw@nwpu.edu.cn.|Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, 127 West Youyi Road, Xian, 710072, Shaanxi, China.|Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Blvd, Pittsburgh, PA, 15206, USA.|Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Blvd, Pittsburgh, PA, 15206, USA. songjian@pitt.edu.', 'China|China|China|United States|United States', 12, 'Algorithms|Colorectal Neoplasms|Computational Biology|Genetic Association Studies|Genetic Predisposition to Disease|Humans|RNA, Long Noncoding|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-019-2675-y', '30782113', 'Long non-coding RNAs play an important role in human complex diseases. Identification of lncRNA-disease associations will gain insight into disease-related lncRNAs and benefit disease diagnoses and treatment. However, using experiments to explore the lncRNA-disease associations is expensive and time consuming.'),
(98853, 2019, 'bmc', 'ElemCor: accurate data analysis and enrichment calculation for high-resolution LC-MS stable isotope labeling experiments', 'Du, Di|Tan, Lin|Wang, Yumeng|Peng, Bo|Weinstein, John N|Wondisford, Fredric E|Su, Xiaoyang|Lorenzi, Philip L', 'Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.|Department of Medicine, Division of Endocrinology, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, USA.|Department of Medicine, Division of Endocrinology, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, USA. xs137@rwjms.rutgers.edu.|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. pllorenzi@mdanderson.org.', 'United States|United States|United States|United States|United States|United States|United States|United States', 6, 'Chromatography, Liquid|Computer Simulation|Data Analysis|Isotope Labeling|Isotopes|Mass Spectrometry|Saccharomyces cerevisiae|Software', 'Journal Article', '10.1186/s12859-019-2669-9', '30782135', 'The investigation of intracellular metabolism is the mainstay in the biotechnology and physiology settings. Intracellular metabolic rates are commonly evaluated using labeling pattern of the identified metabolites obtained from stable isotope labeling experiments. The labeling pattern or mass distribution vector describes the fractional abundances of all isotopologs with different masses as a result of isotopic labeling, which are typically resolved using mass spectrometry. Because naturally occurring isotopes and isotopic impurity also contribute to measured signals, the measured patterns must be corrected to obtain the labeling patterns. Since contaminant isotopologs with the same nominal mass can be resolved using modern mass spectrometers with high mass resolution, the correction process should be resolution dependent.'),
(98854, 2019, 'bmc', 'SEQprocess: a modularized and customizable pipeline framework for NGS processing in R package', 'Joo, Taewoon|Choi, Ji-Hye|Lee, Ji-Hye|Park, So Eun|Jeon, Youngsic|Jung, Sae Hoon|Woo, Hyun Goo', 'Department of Physiology, Ajou University School of Medicine, 164 Worldcup-ro, Yeongtong-gu, Suwon, 16499, Republic of Korea.|Department of Physiology, Ajou University School of Medicine, 164 Worldcup-ro, Yeongtong-gu, Suwon, 16499, Republic of Korea.|Department of Physiology, Ajou University School of Medicine, 164 Worldcup-ro, Yeongtong-gu, Suwon, 16499, Republic of Korea.|Department of Physiology, Ajou University School of Medicine, 164 Worldcup-ro, Yeongtong-gu, Suwon, 16499, Republic of Korea.|Department of Pathology, Yonsei University College of Medicine, Seoul, Republic of Korea.|Ajou University School of Medicine, Suwon, Republic of Korea.|Department of Physiology, Ajou University School of Medicine, 164 Worldcup-ro, Yeongtong-gu, Suwon, 16499, Republic of Korea. hg@ajou.ac.kr.', '-|-|-|-|-|-|-', 0, 'Data Analysis|High-Throughput Nucleotide Sequencing|Humans|Reproducibility of Results|Software|Workflow', 'Journal Article', '10.1186/s12859-019-2676-x', '30786880', 'Next-Generation Sequencing (NGS) is now widely used in biomedical research for various applications. Processing of NGS data requires multiple programs and customization of the processing pipelines according to the data platforms. However, rapid progress of the NGS applications and processing methods urgently require prompt update of the pipelines. Recent clinical applications of NGS technology such as cell-free DNA, cancer panel, or exosomal RNA sequencing data also require appropriate customization of the processing pipelines. Here, we developed SEQprocess, a highly extendable framework that can provide standard as well as customized pipelines for NGS data processing.'),
(98855, 2019, 'bmc', 'Predicting protein-ligand binding residues with deep convolutional neural networks', 'Cui, Yifeng|Dong, Qiwen|Hong, Daocheng|Wang, Xikun', 'Faculty of Education, East China Normal University, 3663 N. Zhongshan Rd., Shanghai, 200062, China.|Faculty of Education, East China Normal University, 3663 N. Zhongshan Rd., Shanghai, 200062, China. qwdong@dase.ecnu.edu.cn.|School of Data Science & Engineering, East China Normal University, Shanghai, 3663 N. Zhongshan Rd., Shanghai, 200062, China.|The High School Affiliated of Liaoning Normal University, Dalian, China.', 'China|China|China|China', 0, 'Algorithms|Amino Acid Sequence|Databases, Protein|Deep Learning|Ligands|Neural Networks (Computer)|Protein Binding|Proteins', 'Journal Article', '10.1186/s12859-019-2672-1', '30808287', 'Ligand-binding proteins play key roles in many biological processes. Identification of protein-ligand binding residues is important in understanding the biological functions of proteins. Existing computational methods can be roughly categorized as sequence-based or 3D-structure-based methods. All these methods are based on traditional machine learning. In a series of binding residue prediction tasks, 3D-structure-based methods are widely superior to sequence-based methods. However, due to the great number of proteins with known amino acid sequences, sequence-based methods have considerable room for improvement with the development of deep learning. Therefore, prediction of protein-ligand binding residues with deep learning requires study.'),
(98856, 2019, 'bmc', 'FoPA: identifying perturbed signaling pathways in clinical conditions using formal methods', 'Mansoori, Fatemeh|Rahgozar, Maseud|Kavousi, Kaveh', 'Database Research Group, Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran.|Database Research Group, Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran. rahgozar@ut.ac.ir.|Complex Biological Systems and Bioinformatics Lab (CBB), Bioinformatics department, University of Tehran, Tehran, Iran. kkavousi@ut.ac.ir.', '-|-|-', 0, 'Autistic Disorder|Bias|Colorectal Neoplasms|Databases as Topic|False Positive Reactions|Humans|Models, Theoretical|Signal Transduction|Software', 'Journal Article', '10.1186/s12859-019-2635-6', '30808299', 'Accurate identification of perturbed signaling pathways based on differentially expressed genes between sample groups is one of the key factors in the understanding of diseases and druggable targets. Most pathway analysis methods prioritize impacted signaling pathways by incorporating pathway topology using simple graph-based models. Despite their relative success, these models are limited in describing all types of dependencies and interactions that exist in biological pathways.'),
(98857, 2019, 'bmc', 'Gsslasso Cox: a Bayesian hierarchical model for predicting survival and detecting associated genes by incorporating pathway information', 'Tang, Zaixiang|Lei, Shufeng|Zhang, Xinyan|Yi, Zixuan|Guo, Boyi|Chen, Jake Y|Shen, Yueping|Yi, Nengjun', 'Department of Biostatistics, School of Public Health, Medical College of Soochow University, University of Alabama at Birmingham, Suzhou, 215123, China.|Department of Biostatistics, School of Public Health, Medical College of Soochow University, University of Alabama at Birmingham, Suzhou, 215123, China.|Department of Biostatistics, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA, 30458, USA.|Eastern Virginia Medical School, Norfork, VA, 23507, USA.|Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, 35294-0022, USA.|Informatics Institute, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, 35294, USA.|Department of Biostatistics, School of Public Health, Medical College of Soochow University, University of Alabama at Birmingham, Suzhou, 215123, China. shenyueping@suda.edu.cn.|Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, 35294-0022, USA. nyi@uab.edu.', 'China|China|Georgia|United States|United States|United States|China|United States', 2, 'Algorithms|Bayes Theorem|Computer Simulation|Female|Genetic Association Studies|Genetic Predisposition to Disease|Humans|Models, Theoretical|Neoplasms|Prognosis|Proportional Hazards Models|Survival Analysis', 'Journal Article', '10.1186/s12859-019-2656-1', '30813883', 'Group structures among genes encoded in functional relationships or biological pathways are valuable and unique features in large-scale molecular data for survival analysis. However, most of previous approaches for molecular data analysis ignore such group structures. It is desirable to develop powerful analytic methods for incorporating valuable pathway information for predicting disease survival outcomes and detecting associated genes.'),
(98858, 2019, 'bmc', 'apparent: a simple and flexible R package for accurate SNP-based parentage analysis in the absence of guiding information', 'Melo, Arthur T O|Hale, Iago', 'Department of Agriculture, Nutrition, and Food Systems, University of New Hampshire, Durham, NH, USA.|Department of Agriculture, Nutrition, and Food Systems, University of New Hampshire, Durham, NH, USA. iago.hale@unh.edu.', 'United States|United States', 0, 'Actinidia|Genetic Loci|Genotype|Plant Breeding|Plants|Polymorphism, Single Nucleotide|Software|Time Factors', 'Journal Article', '10.1186/s12859-019-2662-3', '30819089', 'The accurate determination of parent-progeny relationships within both in situ natural populations and ex situ genetic resource collections can greatly enhance plant breeding/domestication efforts and support plant genetic resource conservation strategies. Although a range of parentage analysis tools are available, none are designed to infer such relationships using genome-wide single nucleotide polymorphism (SNP) data in the complete absence of guiding information, such as generational groups, partial pedigrees, or genders. The R package (apparent) developed and presented here addresses this gap.'),
(98859, 2019, 'bmc', 'Predicting blood pressure from physiological index data using the SVR algorithm', 'Zhang, Bing|Ren, Huihui|Huang, Guoyan|Cheng, Yongqiang|Hu, Changzhen', 'School of Information Science and Engineering, Yanshan University, Hebei Avenue, Qinhuangdao, 066004, China.|School of Information Science and Engineering, Yanshan University, Hebei Avenue, Qinhuangdao, 066004, China.|School of Information Science and Engineering, Yanshan University, Hebei Avenue, Qinhuangdao, 066004, China. hgy@ysu.edu.cn.|Department of Computer Science and Technology, University of Hull, Hull, HU6 7RX, UK.|Beijing Key Laboratory of Software Security Engineering Technique, Beijing Institute of Technology, Beijing, 100081, China.', 'China|China|China|United Kingdom|China', 3, 'Adult|Blood Pressure|Female|Humans|Linear Models|Machine Learning|Male|Neural Networks (Computer)|Support Vector Machine', 'Clinical Trial', '10.1186/s12859-019-2667-y', '30819090', 'Blood pressure diseases have increasingly been identified as among the main factors threatening human health. How to accurately and conveniently measure blood pressure is the key to the implementation of effective prevention and control measures for blood pressure diseases. Traditional blood pressure measurement methods exhibit many inherent disadvantages, for example, the time needed for each measurement is difficult to determine, continuous measurement causes discomfort, and the measurement process is relatively cumbersome. Wearable devices that enable continuous measurement of blood pressure provide new opportunities and hopes. Although machine learning methods for blood pressure prediction have been studied, the accuracy of the results does not satisfy the needs of practical applications.'),
(98860, 2019, 'bmc', 'isma: an R package for the integrative analysis of mutations detected by multiple pipelines', 'Di Nanni, Noemi|Moscatelli, Marco|Gnocchi, Matteo|Milanesi, Luciano|Mosca, Ettore', 'Institute of Biomedical Technologies, Italian National Research Council, Via Fratelli Cervi 93, 20090, Segrate, MI, Italy.|Institute of Biomedical Technologies, Italian National Research Council, Via Fratelli Cervi 93, 20090, Segrate, MI, Italy.|Institute of Biomedical Technologies, Italian National Research Council, Via Fratelli Cervi 93, 20090, Segrate, MI, Italy.|Institute of Biomedical Technologies, Italian National Research Council, Via Fratelli Cervi 93, 20090, Segrate, MI, Italy.|Institute of Biomedical Technologies, Italian National Research Council, Via Fratelli Cervi 93, 20090, Segrate, MI, Italy. ettore.mosca@itb.cnr.it.', 'Italy|Italy|Italy|Italy|Italy', 0, 'Computational Biology|DNA Mutational Analysis|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Mutation|Neoplasms|Software|User-Computer Interface', 'Journal Article', '10.1186/s12859-019-2701-0', '30819096', 'Recent comparative studies have brought to our attention how somatic mutation detection from next-generation sequencing data is still an open issue in bioinformatics, because different pipelines result in a low consensus. In this context, it is suggested to integrate results from multiple calling tools, but this operation is not trivial and the burden of merging, comparing, filtering and explaining the results demands appropriate software.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(98861, 2019, 'bmc', 'MaGIC: a machine learning tool set and web application for monoallelic gene inference from chromatin', 'Vinogradova, Svetlana|Saksena, Sachit D|Ward, Henry N|Vigneau, Sébastien|Gimelbrant, Alexander A', 'Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02115, USA.|Computational and Systems Biology, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.|Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02115, USA.|Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02115, USA. sebastien_vigneau@dfci.harvard.edu.|Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02115, USA. gimelbrant@mail.dfci.harvard.edu.', 'United States|United States|United States|United States|United States', 0, 'Alleles|Animals|Chromatin|Genes|Humans|Internet|Machine Learning|Mice|Reproducibility of Results|Software', 'Journal Article', '10.1186/s12859-019-2679-7', '30819107', 'A large fraction of human and mouse autosomal genes are subject to random monoallelic expression (MAE), an epigenetic mechanism characterized by allele-specific gene expression that varies between clonal cell lineages. MAE is highly cell-type specific and mapping it in a large number of cell and tissue types can provide insight into its biological function. Its detection, however, remains challenging.'),
(98862, 2019, 'bmc', 'DEvis: an R package for aggregation and visualization of differential expression data', 'Price, Adam|Caciula, Adrian|Guo, Cheng|Lee, Bohyun|Morrison, Juliet|Rasmussen, Angela|Lipkin, W Ian|Jain, Komal', 'Center for Infection and Immunity, Mailman School of Public Health of Columbia University, 722 West 168th St., New York, NY, 10032, USA. ap3637@cumc.columbia.edu.|Center for Infection and Immunity, Mailman School of Public Health of Columbia University, 722 West 168th St., New York, NY, 10032, USA.|Center for Infection and Immunity, Mailman School of Public Health of Columbia University, 722 West 168th St., New York, NY, 10032, USA.|Center for Infection and Immunity, Mailman School of Public Health of Columbia University, 722 West 168th St., New York, NY, 10032, USA.|Center for Infection and Immunity, Mailman School of Public Health of Columbia University, 722 West 168th St., New York, NY, 10032, USA.|Center for Infection and Immunity, Mailman School of Public Health of Columbia University, 722 West 168th St., New York, NY, 10032, USA.|Center for Infection and Immunity, Mailman School of Public Health of Columbia University, 722 West 168th St., New York, NY, 10032, USA.|Center for Infection and Immunity, Mailman School of Public Health of Columbia University, 722 West 168th St., New York, NY, 10032, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Data Aggregation|Gene Expression Profiling|Humans|Reproducibility of Results|Sequence Analysis, RNA|Software|Transcriptome', 'Journal Article', '10.1186/s12859-019-2702-z', '30832568', 'Existing tools for the aggregation and visualization of differential expression data have discrete functionality and require that end-users rely on multiple software packages with complex dependencies or manually manipulate data for analysis and interpretation. Furthermore, at present, data aggregation and visualization are laborious, time consuming, and subject to human error. This is a serious limitation on the current state of differential transcriptomic analysis, which makes it necessary to expend extensive time and resources to reach the point where biological meaning can be interpreted. Such an approach for analysis also leads to scattered and non-standardized code, unsystematic project management and non-reproducible result sets.'),
(98863, 2019, 'bmc', 'CellSim: a novel software to calculate cell similarity and identify their co-regulation networks', 'Li, Leijie|Che, Dongxue|Wang, Xiaodan|Zhang, Peng|Rahman, Siddiq Ur|Zhao, Jianbang|Yu, Jiantao|Tao, Shiheng|Lu, Hui|Liao, Mingzhi', 'College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China.|College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China.|College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China.|College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China.|College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China.|College of Information Engineering, Northwest A&F University, Yangling, Shaanxi, China.|College of Information Engineering, Northwest A&F University, Yangling, Shaanxi, China.|College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China.|Department of Bioinformatics and Biostatistics, SJTU Yale Joint Center Biostatistics, Shanghai Jiao Tong University, Shanghai, China.|College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China. liaomingzhi83@163.com.', 'China|China|China|China|China|China|China|China|China|China', 2, 'Cell Aggregation|Computational Biology|Gene Expression Regulation|Gene Regulatory Networks|Humans|Software|Stem Cells|Transcription Factors', 'Journal Article', '10.1186/s12859-019-2699-3', '30832570', 'Cell direct reprogramming technology has been rapidly developed with its low risk of tumor risk and avoidance of ethical issues caused by stem cells, but it is still limited to specific cell types. Direct reprogramming from an original cell to target cell type needs the cell similarity and cell specific regulatory network. The position and function of cells in vivo, can provide some hints about the cell similarity. However, it still needs further clarification based on molecular level studies.'),
(98864, 2019, 'bmc', 'Positive-unlabelled learning of glycosylation sites in the human proteome', 'Li, Fuyi|Zhang, Yang|Purcell, Anthony W|Webb, Geoffrey I|Chou, Kuo-Chen|Lithgow, Trevor|Li, Chen|Song, Jiangning', 'Infection and Immunity Program, Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC, 3800, Australia.|College of Information Engineering, Northwest A and F University, Yangling, 712100, Shaanxi, China.|Infection and Immunity Program, Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC, 3800, Australia.|Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC, 3800, Australia.|Gordon Life Science Institute, Boston, MA, 02478, USA.|Infection and Immunity Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, Melbourne, VIC, 3800, Australia.|Infection and Immunity Program, Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC, 3800, Australia. Chen.Li@monash.edu.|Infection and Immunity Program, Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC, 3800, Australia. Jiangning.Song@monash.edu.', 'Australia|China|Australia|Australia|United States|Australia|Australia|Australia', 0, 'Computational Biology|Databases, Protein|Glycosylation|Humans|Protein Processing, Post-Translational|Proteome|ROC Curve|Staining and Labeling|Support Vector Machine', 'Journal Article', '10.1186/s12859-019-2700-1', '30841845', 'As an important type of post-translational modification (PTM), protein glycosylation plays a crucial role in protein stability and protein function. The abundance and ubiquity of protein glycosylation across three domains of life involving Eukarya, Bacteria and Archaea demonstrate its roles in regulating a variety of signalling and metabolic pathways. Mutations on and in the proximity of glycosylation sites are highly associated with human diseases. Accordingly, accurate prediction of glycosylation can complement laboratory-based methods and greatly benefit experimental efforts for characterization and understanding of functional roles of glycosylation. For this purpose, a number of supervised-learning approaches have been proposed to identify glycosylation sites, demonstrating a promising predictive performance. To train a conventional supervised-learning model, both reliable positive and negative samples are required. However, in practice, a large portion of negative samples (i.e. non-glycosylation sites) are mislabelled due to the limitation of current experimental technologies. Moreover, supervised algorithms often fail to take advantage of large volumes of unlabelled data, which can aid in model learning in conjunction with positive samples (i.e. experimentally verified glycosylation sites).'),
(98865, 2019, 'bmc', 'GSEPD: a Bioconductor package for RNA-seq gene set enrichment and projection display', 'Stamm, Karl|Tomita-Mitchell, Aoy|Bozdag, Serdar', 'Department of Mathematics, Statistics and Computer Science, Marquette University, Milwaukee, WI, USA.|Department of Surgery, Medical College of Wisconsin, Milwaukee, WI, USA.|Department of Mathematics, Statistics and Computer Science, Marquette University, Milwaukee, WI, USA. serdar.bozdag@marquette.edu.', 'United States|United States|United States', 1, 'Gene Ontology|Heart Atria|Humans|RNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/s12859-019-2697-5', '30841846', 'RNA-seq, wherein RNA transcripts expressed in a sample are sequenced and quantified, has become a widely used technique to study disease and development. With RNA-seq, transcription abundance can be measured, differential expression genes between groups and functional enrichment of those genes can be computed. However, biological insights from RNA-seq are often limited by computational analysis and the enormous volume of resulting data, preventing facile and meaningful review and interpretation of gene expression profiles. Particularly, in cases where the samples under study exhibit uncontrolled variation, deeper analysis of functional enrichment would be necessary to visualize samples gene expression activity under each biological function.'),
(98866, 2019, 'bmc', 'svist4get: a simple visualization tool for genomic tracks from sequencing experiments', 'Egorov, Artyom A|Sakharova, Ekaterina A|Anisimova, Aleksandra S|Dmitriev, Sergey E|Gladyshev, Vadim N|Kulakovskiy, Ivan V', 'Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Leninskiye gory 1, Moscow, 119234, Russia. artyom.egorov@hotmail.com.|Vavilov Institute of General Genetics, Russian Academy of Sciences, Gubkina 3, Moscow, 119991, Russia.|Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Leninskiye gory 1, Moscow, 119234, Russia.|Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Leninskiye gory 1, Moscow, 119234, Russia.|Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Leninskiye gory 1, Moscow, 119234, Russia.|Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Leninskiye gory 1, Moscow, 119234, Russia. ivan.kulakovskiy@gmail.com.', '-|-|-|-|-|-', 4, 'Genome|Genomics|High-Throughput Nucleotide Sequencing|RNA, Messenger|Ribosomes|Software', 'Journal Article', '10.1186/s12859-019-2706-8', '30841857', 'High-throughput sequencing often provides a foundation for experimental analyses in the life sciences. For many such methods, an intermediate layer of bioinformatics data analysis is the genomic signal track constructed by short read mapping to a particular genome assembly. There are many software tools to visualize genomic tracks in a web browser or with a stand-alone graphical user interface. However, there are only few command-line applications suitable for automated usage or production of publication-ready visualizations.'),
(98867, 2019, 'bmc', 'The influence of different types of translational inaccuracies on the genetic code structure', 'Bxc5x82axc5xbbej, Pawexc5x82|Wnetrzak, Maxc5x82gorzata|Mackiewicz, Dorota|Mackiewicz, Pawexc5x82', 'Department of Genomics, University of Wrocxc5x82aw, ul. Joliot-Curie 14a, Wrocxc5x82aw, 50-383, Poland. pawel.blazej@uwr.edu.pl.|Department of Genomics, University of Wrocxc5x82aw, ul. Joliot-Curie 14a, Wrocxc5x82aw, 50-383, Poland.|Department of Genomics, University of Wrocxc5x82aw, ul. Joliot-Curie 14a, Wrocxc5x82aw, 50-383, Poland.|Department of Genomics, University of Wrocxc5x82aw, ul. Joliot-Curie 14a, Wrocxc5x82aw, 50-383, Poland.', 'Poland|Poland|Poland|Poland', 5, 'Algorithms|Codon|Computer Simulation|Entropy|Genetic Code|Models, Genetic|Protein Biosynthesis|Uncertainty', 'Journal Article', '10.1186/s12859-019-2661-4', '30841864', 'The standard genetic code is a recipe for assigning unambiguously 21 labels, i.e. amino acids and stop translation signal, to 64 codons. However, at early stages of the translational machinery development, the codons did not have to be read unambiguously and the early genetic codes could have contained some ambiguous assignments of codons to amino acids. Therefore, the goal of this work was to obtain the genetic code structures which could have evolved assuming different types of inaccuracy of the translational machinery starting from unambiguous assignments of codons to amino acids.'),
(98868, 2019, 'bmc', 'RhoTermPredict: an algorithm for predicting Rho-dependent transcription terminators based on Escherichia coli, Bacillus subtilis and Salmonella enterica databases', 'Di Salvo, Marco|Puccio, Simone|Peano, Clelia|Lacour, Stephan|Alifano, Pietro', 'Department of Biological and Environmental Sciences and Technologies, University of Salento, Lecce, Italy.|Humanitas Clinical and Research Center, Rozzano, Milan, Italy.|Humanitas Clinical and Research Center, Rozzano, Milan, Italy.|Univ. Grenoble Alpes, CNRS, Inria, LIPhy (UMR5588), 38000, Grenoble, France.|Department of Biological and Environmental Sciences and Technologies, University of Salento, Lecce, Italy. pietro.alifano@unisalento.it.', 'Italy|Italy|Italy|France|Italy', 0, 'Algorithms|Bacillus subtilis|Base Sequence|Databases, Genetic|Escherichia coli|Escherichia coli K12|Genome, Bacterial|RNA, Bacterial|Rho Factor|Salmonella enterica|Sequence Analysis, RNA|Terminator Regions, Genetic', 'Journal Article', '10.1186/s12859-019-2704-x', '30845912', 'In bacterial genomes, there are two mechanisms to terminate the DNA transcription: the intrinsic or Rho-independent termination and the Rho-dependent termination. Intrinsic terminators are characterized by a RNA hairpin followed by a run of 6-8xe2x80x89U residues relatively easy to identify using one of the numerous available prediction programs. In contrast, Rho-dependent termination is mediated by the Rho protein factor that, firstly, binds to ribosome-free mRNA in a site characterized by a Cxe2x80x89>xe2x80x89G content and then reaches the RNA polymerase to induce its release. Conversely on intrinsic terminators, the computational prediction of Rho-dependent terminators in prokaryotes is a very difficult problem because the sequence features required for the function of Rho are complex and poorly defined. This is the reason why it still does not exist an exhaustive Rho-dependent terminators prediction program.'),
(98869, 2019, 'bmc', 'Probabilistic ancestry maps: a method to assess and visualize population substructures in genetics', 'Gaspar, Héléna A|Breen, Gerome', 'Kings College London, Institute of Psychiatry, Psychology and Neuroscience, Social, Genetic and Developmental Psychiatry (SGDP) Centre, 16 De Crespigny Park, London, SE5 8AF, UK. helena.gaspar@kcl.ac.uk.|Kings College London, Institute of Psychiatry, Psychology and Neuroscience, Social, Genetic and Developmental Psychiatry (SGDP) Centre, 16 De Crespigny Park, London, SE5 8AF, UK.', 'United Kingdom|United Kingdom', 0, 'African Continental Ancestry Group|Arabidopsis|Asian Continental Ancestry Group|Cluster Analysis|Genetics, Population|Genome-Wide Association Study|Humans|Models, Statistical|Principal Component Analysis|Stochastic Processes', 'Journal Article', '10.1186/s12859-019-2680-1', '30845922', 'Principal component analysis (PCA) is a standard method to correct for population stratification in ancestry-specific genome-wide association studies (GWASs) and is used to cluster individuals by ancestry. Using the 1000 genomes project data, we examine how non-linear dimensionality reduction methods such as t-distributed stochastic neighbor embedding (t-SNE) or generative topographic mapping (GTM) can be used to provide improved ancestry maps by accounting for a higher percentage of explained variance in ancestry, and how they can help to estimate the number of principal components necessary to account for population stratification. GTM generates posterior probabilities of class membership which can be used to assess the probability of an individual to belong to a given population - as opposed to t-SNE, GTM can be used for both clustering and classification.'),
(98870, 2019, 'bmc', 'JOA: Joint Overlap Analysis of multiple genomic interval sets', 'Otlu, Burçak|Can, Tolga', 'Department of Computer Engineering, Middle East Technical University, Ankara, 06800, Turkey. burcak@ceng.metu.edu.tr.|Department of Computer Engineering, Middle East Technical University, Ankara, 06800, Turkey.', 'Turkey|Turkey', 1, 'Algorithms|Databases, Genetic|Genomics|Humans|Sequence Analysis, DNA|Software|Time Factors', 'Journal Article', '10.1186/s12859-019-2698-4', '30849961', 'Next-generation sequencing (NGS) technologies have produced large volumes of genomic data. One common operation on heterogeneous genomic data is genomic interval intersection. Most of the existing tools impose restrictions such as not allowing nested intervals or requiring intervals to be sorted when finding overlaps in two or more interval sets.'),
(98871, 2019, 'bmc', 'SV-Pop: population-based structural variant analysis and visualization', 'Ravenhall, Matt|Campino, Susana|Clark, Taane G', 'Department of Pathogen Molecular Biology, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK. matt.ravenhall@lshtm.ac.uk.|Department of Pathogen Molecular Biology, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK.|Department of Pathogen Molecular Biology, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK.', 'United Kingdom|United Kingdom|United Kingdom', 2, 'Computational Biology|GTP Cyclohydrolase|Genetic Association Studies|Genetic Structures|Genetics, Population|Genomics|High-Throughput Nucleotide Sequencing|Phenotype|Plasmodium falciparum|Polymorphism, Single Nucleotide|Promoter Regions, Genetic|Protozoan Proteins', 'Journal Article', '10.1186/s12859-019-2718-4', '30866793', 'Genetic structural variation underpins a multitude of phenotypes, with significant implications for a range of biological outcomes. Despite their crucial role, structural variants (SVs) are often neglected and overshadowed by single nucleotide polymorphisms (SNPs), which are used in large-scale analysis such as genome-wide association and population genetic studies.'),
(98872, 2019, 'bmc', 'GRIBCG: a software for selection of sgRNAs in the design of balancer chromosomes', 'Merritt, Brian B|Cheung, Lily S', 'School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA.|School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, USA. lily.cheung@gatech.edu.', 'Georgia|Georgia', 0, 'Animals|CRISPR-Associated Protein 9|Chromosome Inversion|Chromosomes, Insect|Drosophila|Gene Editing|RNA, Guide|Software|Streptococcus pyogenes', 'Journal Article', '10.1186/s12859-019-2712-x', '30866794', 'Balancer chromosomes are tools used by fruit fly geneticists to prevent meiotic recombination. Recently, CRISPR/Cas9 genome editing has been shown capable of generating inversions similar to the chromosomal rearrangements present in balancer chromosomes. Extending the benefits of balancer chromosomes to other multicellular organisms could significantly accelerate biomedical and plant genetics research.'),
(98873, 2019, 'bmc', 'PanRV: Pangenome-reverse vaccinology approach for identifications of potential vaccine candidates in microbial pangenome', 'Naz, Kanwal|Naz, Anam|Ashraf, Shifa Tariq|Rizwan, Muhammad|Ahmad, Jamil|Baumbach, Jan|Ali, Amjad', 'Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), H-12, Islamabad, 44000, Pakistan.|Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), H-12, Islamabad, 44000, Pakistan.|Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), H-12, Islamabad, 44000, Pakistan.|Research Center for Modeling and Simulation (RCMS), National University of Sciences and Technology (NUST), H-12, Islamabad, Pakistan.|Research Center for Modeling and Simulation (RCMS), National University of Sciences and Technology (NUST), H-12, Islamabad, Pakistan.|Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munchen, Germany.|Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), H-12, Islamabad, 44000, Pakistan. amjaduni@gmail.com.', 'Pakistan|Pakistan|Pakistan|Pakistan|Pakistan|Germany|Pakistan', 11, 'Bacterial Vaccines|Genomics|Humans|Proteomics|Vaccinology', 'Journal Article', '10.1186/s12859-019-2713-9', '30871454', 'A revolutionary diversion from classical vaccinology to reverse vaccinology approach has been observed in the last decade. The ever-increasing genomic and proteomic data has greatly facilitated the vaccine designing and development process. Reverse vaccinology is considered as a cost-effective and proficient approach to screen the entire pathogen genome. To look for broad-spectrum immunogenic targets and analysis of closely-related bacterial species, the assimilation of pangenome concept into reverse vaccinology approach is essential. The categories of species pangenome such as core, accessory, and unique genes sets can be analyzed for the identification of vaccine candidates through reverse vaccinology.'),
(98874, 2019, 'bmc', 'Multispecies genome-wide analysis defines the MAP3K gene family in Gossypium hirsutum and reveals conserved family expansions', 'Bokros, Norbert|Popescu, Sorina C|Popescu, George V', 'Department of Biochemistry, Molecular Biology, Plant Pathology and Entomology, Mississippi State University, Mississippi State, MS, 39762, USA.|Department of Biochemistry, Molecular Biology, Plant Pathology and Entomology, Mississippi State University, Mississippi State, MS, 39762, USA.|Institute for Genomics, Biocomputing and Bioengineering, Mississippi State University, Mississippi State, MS, 39762, USA. popescu@igbb.msstate.edu.', 'United States|United States|United States', 2, 'Gene Expression Regulation, Plant|Genes, Plant|Genome-Wide Association Study|Gossypium|MAP Kinase Kinase Kinase 1', 'Journal Article', '10.1186/s12859-019-2624-9', '30871456', 'Gene families are sets of structurally and evolutionarily related genes - in one or multiple species - that typically share a conserved biological function. As such, the identification and subsequent analyses of entire gene families are widely employed in the fields of evolutionary and functional genomics of both well established and newly sequenced plant genomes. Currently, plant gene families are typically identified using one of two major ways: 1) HMM-profile based searches using models built on Arabidopsis thaliana genes or 2) coding sequence homology searches using curated databases. Integrated databases containing functionally annotated genes and gene families have been developed for model organisms and several important crops, however, a comprehensive methodology for gene family annotation is currently lacking, preventing automated annotation of newly sequenced genomes.'),
(98875, 2019, 'bmc', 'Evaluation of Sirtuin-3 probe quality and co-expressed genes using literature cohesion', 'Roy, Sujoy|Zaman, Kazi I|Williams, Robert W|Homayouni, Ramin', 'Bioinformatics Program, University of Memphis, Memphis, 38152, USA.|Bioinformatics Program, University of Memphis, Memphis, 38152, USA.|Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, 38163, USA.|Bioinformatics Program, University of Memphis, Memphis, 38152, USA. rhomayon@memphis.edu.', 'United States|United States|United States|United States', 1, 'Data Mining|Gene Expression Profiling|Humans|Proteomics|Sirtuin 3', 'Journal Article', '10.1186/s12859-019-2621-z', '30871457', 'Gene co-expression studies can provide important insights into molecular and cellular signaling pathways. The GeneNetwork database is a unique resource for co-expression analysis using data from a variety of tissues across genetically distinct inbred mice. However, extraction of biologically meaningful co-expressed gene sets is challenging due to variability in microarray platforms, probe quality, normalization methods, and confounding biological factors. In this study, we tested whether literature derived functional cohesion could be used as an objective metric in lieu of ground truth to evaluate the quality of probes and microarray datasets.'),
(98876, 2019, 'bmc', 'Study of serious adverse drug reactions using FDA-approved drug labeling and MedDRA', 'Wu, Leihong|Ingle, Taylor|Liu, Zhichao|Zhao-Wong, Anna|Harris, Stephen|Thakkar, Shraddha|Zhou, Guangxu|Yang, Junshuang|Xu, Joshua|Mehta, Darshan|Ge, Weigong|Tong, Weida|Fang, Hong', 'National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA.|National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA.|National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA.|MedDRA Maintenance and Support Services Organization, 7575 Colshire Dr., McLean, VA, 22102, USA.|National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA.|National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA.|National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA.|National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA.|National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA.|National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA.|National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA.|National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA. Weida.Tong@fda.hhs.gov.|National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA. Hong.Fang@fda.hhs.gov.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Adverse Drug Reaction Reporting Systems|Data Mining|Drug Labeling|Drug-Related Side Effects and Adverse Reactions|Humans', 'Journal Article', '10.1186/s12859-019-2628-5', '30871458', 'Adverse Drug Reactions (ADRs) are of great public health concern. FDA-approved drug labeling summarizes ADRs of a drug product mainly in three sections, i.e., Boxed Warning (BW), Warnings and Precautions (WP), and Adverse Reactions (AR), where the severity of ADRs are intended to decrease in the order of BWxe2x80x89>xe2x80x89WPxe2x80x89>xe2x80x89AR. Several reported studies have extracted ADRs from labeling documents, but most, if not all, did not discriminate the severity of the ADRs by the different labeling sections. Such a practice could overstate or underestimate the impact of certain ADRs to the public health. In this study, we applied the Medical Dictionary for Regulatory Activities (MedDRA) to drug labeling and systematically analyzed and compared the ADRs from the three labeling sections with a specific emphasis on analyzing serious ADRs presented in BW, which is of most drug safety concern.'),
(98877, 2019, 'bmc', 'Keanu: a novel visualization tool to explore biodiversity in metagenomes', 'Thrash, Adam|Arick, Mark|Barbato, Robyn A|Jones, Robert M|Douglas, Thomas A|Esdale, Julie|Perkins, Edward J|Garcia-Reyero, Natàlia', 'Institute for Genomics, Biocomputing & Biotechnology, Mississippi State University, Starkville, MS, USA.|Institute for Genomics, Biocomputing & Biotechnology, Mississippi State University, Starkville, MS, USA.|US Army Engineer Research and Development Center, Cold Regions Research and Engineering Laboratory, Hanover, NH, USA.|US Army Engineer Research and Development Center, Cold Regions Research and Engineering Laboratory, Hanover, NH, USA.|US Army Engineer Research and Development Center, Cold Regions Research and Engineering Laboratory, Fort Wainwright, AK, USA.|Center for the Environmental Management of Military Lands, Colorado State University, Fort Collins, CO, USA.|US Army Engineer Research and Development Center, Environmental Laboratory, Vicksburg, MS, USA.|Institute for Genomics, Biocomputing & Biotechnology, Mississippi State University, Starkville, MS, USA. Natalia.G.Vinas@erdc.dren.mil.', 'United States|United States|United States|United States|United States|United States|United States|United States', 1, 'Biodiversity|Metagenomics', 'Journal Article', '10.1186/s12859-019-2629-4', '30871459', 'One of the main challenges when analyzing complex metagenomics data is the fact that large amounts of information need to be presented in a comprehensive and easy-to-navigate way. In the process of analyzing FASTQ sequencing data, visualizing which organisms are present in the data can be useful, especially with metagenomics data or data suspected to be contaminated. Here, we describe the development and application of a command-line tool, Keanu, for visualizing and exploring sample content in metagenomics data. We developed Keanu as an interactive tool to make viewing complex data easier.'),
(98878, 2019, 'bmc', 'Partition-based optimization model for generative anatomy modeling language (POM-GAML)', 'Demirel, Doga|Cetinsaya, Berk|Halic, Tansel|Kockara, Sinan|Ahmadi, Shahryar', 'Department of Computer Science, University of Arkansas at Little Rock, Little Rock, AR, USA.|Department of Computer Science, University of Arkansas at Little Rock, Little Rock, AR, USA.|Department of Computer Science, University of Central Arkansas, Conway, AR, USA. thalic@uca.edu.|Department of Computer Science, University of Central Arkansas, Conway, AR, USA.|Department of Orthopedic Surgery, University of Arkansas for Medical Sciences, Little Rock, AR, USA.', 'United States|United States|United States|United States|United States', 0, 'Computer Simulation|Humans|Language|Models, Anatomic', 'Journal Article', '10.1186/s12859-019-2626-7', '30871460', 'This paper presents a novel approach for Generative Anatomy Modeling Language (GAML). This approach automatically detects the geometric partitions in 3D anatomy that in turn speeds up integrated non-linear optimization model in GAML for 3D anatomy modeling with constraints (e.g. joints). This integrated non-linear optimization model requires the exponential execution time. However, our approach effectively computes the solution for non-linear optimization model and reduces computation time from exponential to linear time. This is achieved by grouping the 3D geometric constraints into communities.'),
(98879, 2019, 'bmc', 'Similarities and differences between variants called with human reference genome HG19 or HG38', 'Pan, Bohu|Kusko, Rebecca|Xiao, Wenming|Zheng, Yuanting|Liu, Zhichao|Xiao, Chunlin|Sakkiah, Sugunadevi|Guo, Wenjing|Gong, Ping|Zhang, Chaoyang|Ge, Weigong|Shi, Leming|Tong, Weida|Hong, Huixiao', 'Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA.|Immuneering Corporation, Cambridge, MA, 02142, USA.|Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA.|Center for Pharmacogenomics, Fudan University, Shanghai, China.|Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA.|National Center for Biotechnological Information, National Institutes of Health, Bethesda, MD, 20894, USA.|Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA.|Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA.|Environmental Laboratory, US Army Engineer Research and Development Center, Vicksburg, MS, 39180, USA.|School of Computing, The University of Southern Mississippi, Hattiesburg, MS, 39406, USA.|Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA.|Center for Pharmacogenomics, Fudan University, Shanghai, China.|Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA.|Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA. huixiao.hong@fda.hhs.gov.', 'United States|United States|United States|China|United States|United States|United States|United States|United States|United States|United States|China|United States|United States', 0, 'Genome, Human|High-Throughput Nucleotide Sequencing|Humans', 'Journal Article', '10.1186/s12859-019-2620-0', '30871461', 'Reference genome selection is a prerequisite for successful analysis of next generation sequencing (NGS) data. Current practice employs one of the two most recent human reference genome versions: HG19 or HG38. To date, the impact of genome version on SNV identification has not been rigorously assessed.'),
(98880, 2019, 'bmc', 'DSab-origin: a novel IGHD sensitive VDJ mapping method and its application on antibody response after influenza vaccination', 'Zhang, Qingchen|Zhang, Lu|Zhou, Chen|Yang, Yiyan|Yin, Zuojing|Wu, Dingfeng|Tang, Kailin|Cao, Zhiwei', 'Shanghai 10th peoples hospital, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, Peoples Republic of China.|Shanghai 10th peoples hospital, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, Peoples Republic of China.|Shanghai 10th peoples hospital, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, Peoples Republic of China.|Shanghai 10th peoples hospital, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, Peoples Republic of China.|Shanghai 10th peoples hospital, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, Peoples Republic of China.|Shanghai 10th peoples hospital, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, Peoples Republic of China.|Shanghai 10th peoples hospital, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, Peoples Republic of China.|Shanghai 10th peoples hospital, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, Peoples Republic of China. zwcao@tongji.edu.cn.', 'China|China|China|China|China|China|China|China', 0, 'Animals|Antibodies, Viral|Chromosome Mapping|Computational Biology|Humans|Influenza Vaccines|Influenza, Human|Mice|V(D)J Recombination', 'Journal Article', '10.1186/s12859-019-2715-7', '30871465', 'Functional antibody genes are often assembled by VDJ recombination and then diversified by somatic hypermutation. Identifying the combination of sourcing germline genes is critical to understand the process of antibody maturation, which may facilitate the diagnostics and rapid generation of human monoclonal antibodies in therapeutics. Despite of successful efforts in V and J fragment assignment, method in D segment tracing remains weak for immunoglobulin heavy diversity (IGHD).'),
(98881, 2019, 'bmc', 'MNEMONIC: MetageNomic Experiment Mining to create an OTU Network of Inhabitant Correlations', 'Perz, Aleksandra I|Giles, Cory B|Brown, Chase A|Porter, Hunter|Roopnarinesingh, Xiavan|Wren, Jonathan D', 'Arthritis and Clinical Immunology Program, Division of Genomics and Data Sciences, Oklahoma Medical Research Foundation, Oklahoma City, OK, 73104-5005, USA. jdwren@gmail.com.|Arthritis and Clinical Immunology Program, Division of Genomics and Data Sciences, Oklahoma Medical Research Foundation, Oklahoma City, OK, 73104-5005, USA.|Arthritis and Clinical Immunology Program, Division of Genomics and Data Sciences, Oklahoma Medical Research Foundation, Oklahoma City, OK, 73104-5005, USA.|Arthritis and Clinical Immunology Program, Division of Genomics and Data Sciences, Oklahoma Medical Research Foundation, Oklahoma City, OK, 73104-5005, USA.|Arthritis and Clinical Immunology Program, Division of Genomics and Data Sciences, Oklahoma Medical Research Foundation, Oklahoma City, OK, 73104-5005, USA.|Arthritis and Clinical Immunology Program, Division of Genomics and Data Sciences, Oklahoma Medical Research Foundation, Oklahoma City, OK, 73104-5005, USA. jonathan-wren@omrf.org.', 'United States|United States|United States|United States|United States|United States', 0, 'Humans|Metagenomics|Microbiota', 'Journal Article', '10.1186/s12859-019-2623-x', '30871469', 'The number of publicly available metagenomic experiments in various environments has been rapidly growing, empowering the potential to identify similar shifts in species abundance between different experiments. This could be a potentially powerful way to interpret new experiments, by identifying common themes and causes behind changes in species abundance.'),
(98882, 2019, 'bmc', 'Proceedings of the 2018 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) conference', 'Wren, Jonathan D|Doerkson, Robert J|Toby, Inimary T|Nanduri, Bindu|Homayouni, Ramin|Manda, Prashanti|Thakkar, Shraddha', 'Arthritis and Clinical Immunology Research Program, Division of Genomics and Data Sciences, Oklahoma Medical Research Foundation, 825 N.E. 13th Street, Oklahoma City, OK, 73104-5005, USA. jonathan-wren@omrf.org.|Department of BioMolecular Sciences, Division of Medicinal Chemistry, School of Pharmacy, The University of Mississippi, University, Jackson, MS, 38677, USA.|Department of Biology, University of Dallas, 1845 E Northgate Dr, Irving, TX, 75062-4736, USA.|Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University, Mississippi State, MS, 39762, USA.|Foundational Medical Sciences, University of Oakland William Beaumont School of Medicine, Rochester, MI, 48309-4482, USA.|Department of Computer Science, University of North Carolina at Greensboro, Greensboro, USA.|Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, 72079, USA.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Computational Biology|History, 21st Century|Humans|Systems Biology', 'Clinical Conference', '10.1186/s12859-019-2618-7', '30871470', '-'),
(98883, 2019, 'bmc', 'Local edge-enhanced active contour for accurate skin lesion border detection', 'Bayraktar, Mustafa|Kockara, Sinan|Halic, Tansel|Mete, Mutlu|Wong, Henry K|Iqbal, Kamran', 'Bioinformatics, University of Arkansas Little Rock, 2804 S. University, Little Rock, 72204, AR, USA.|Computer Science, University of Central Arkansas, 201 Donaghey Avenue, Conway, 72035, AR, USA. skockara@uca.edu.|Computer Science, University of Central Arkansas, 201 Donaghey Avenue, Conway, 72035, AR, USA.|Computer Science, Texas A&M University-Commerce, 2200 Campbell, Commerce, 75428, TX, USA.|Dermatology, University of Arkansas for Medical Sciences, 324 Campus Dr., Little Rock, 72205, AR, USA.|Systems Engineering, University of Arkansas for Medical Sciences, 2804 S. University, Little Rock, 72204, AR, USA.', 'United States|United States|United States|United States|United States|United States', 3, 'Dermoscopy|Humans|Image Interpretation, Computer-Assisted|Skin Neoplasms', 'Journal Article', '10.1186/s12859-019-2625-8', '30871471', 'Dermoscopy is one of the common and effective imaging techniques in diagnosis of skin cancer, especially for pigmented lesions. Accurate skin lesion border detection is the key to extract important dermoscopic features of the skin lesion. In current clinical settings, border delineation is performed manually by dermatologists. Operator based assessments lead to intra- and inter-observer variations due to its subjective nature. Moreover it is a tedious process. Because of aforementioned hurdles, the automation of lesion boundary detection in dermoscopic images is necessary. In this study, we address this problem by developing a novel skin lesion border detection method with a robust edge indicator function, which is based on a meshless method.'),
(98884, 2019, 'bmc', 'Genome-wide tracts of homozygosity and exome analyses reveal repetitive elements with Barrets esophagus/esophageal adenocarcinoma risk', 'Wanchai, Visanu|Jin, Jing|Bircan, Emine|Eng, Charis|Orloff, Mohammed', 'Arkansas Center for Genomic Epidemiology & Medicine and The Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA.|The Department of Epidemiology, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA.|The Department of Epidemiology, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA.|Genomic Medicine Institute, Cleveland Clinic, Cleveland, OH, 44195, USA.|The Department of Epidemiology, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA. msorloff@uams.edu.', 'United States|United States|United States|United States|United States', 1, 'Adenocarcinoma|Barrett Esophagus|Esophageal Neoplasms|Exome|Genome|Homozygote|Humans|Risk Factors|Whole Exome Sequencing', 'Journal Article', '10.1186/s12859-019-2622-y', '30871476', 'Barretts esophagus (BE) is most commonly seen as the condition in which the normal squamous epithelium lining of the esophagus is replaced by goblet cells. Many studies show that BE is a predisposing factor for the development of esophageal adenocarcinoma (EAC), a particularly lethal cancer. The use of single nucleotide polymorphisms (SNPs) to map BE/EAC genes has previously provided insufficient genetic information to fully characterize the heterogeneous nature of the disease. We therefore hypothesize that rigorous interrogation of other types of genomic changes, e.g. tracts of homozygosity (TOH), repetitive elements, and insertion/deletions, may provide a comprehensive understanding of the development of BE/EAC.'),
(98885, 2019, 'bmc', 'Predicting protein residue-residue contacts using random forests and deep networks', 'Luttrell, Joseph|Liu, Tong|Zhang, Chaoyang|Wang, Zheng', 'School of Computing Sciences and Computer Engineering, University of Southern Mississippi, 118 College Drive, Hattiesburg, MS, 39406, USA.|Department of Computer Science, University of Miami, 1365 Memorial Drive, Coral Gables, FL, 33124, USA.|School of Computing Sciences and Computer Engineering, University of Southern Mississippi, 118 College Drive, Hattiesburg, MS, 39406, USA.|Department of Computer Science, University of Miami, 1365 Memorial Drive, Coral Gables, FL, 33124, USA. zheng.wang@miami.edu.', 'United States|United States|United States|United States', 3, 'Amino Acid Sequence|Computational Biology|Machine Learning|Proteins', 'Journal Article', '10.1186/s12859-019-2627-6', '30871477', 'The ability to predict which pairs of amino acid residues in a protein are in contact with each other offers many advantages for various areas of research that focus on proteins. For example, contact prediction can be used to reduce the computational complexity of predicting the structure of proteins and even to help identify functionally important regions of proteins. These predictions are becoming especially important given the relatively low number of experimentally determined protein structures compared to the amount of available protein sequence data.'),
(98886, 2019, 'bmc', 'Evaluation of linear models and missing value imputation for the analysis of peptide-centric proteomics', 'Berg, Philip|McConnell, Evan W|Hicks, Leslie M|Popescu, Sorina C|Popescu, George V', 'Department of Biochemistry, Molecular Biology, Entomology, and Plant Pathology, Mississippi State University, Mississippi State, MS, USA.|Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.|Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.|Department of Biochemistry, Molecular Biology, Entomology, and Plant Pathology, Mississippi State University, Mississippi State, MS, USA.|Institute for Genomics, Biocomputing and Biotechnology, Mississippi State University, Mississippi State, MS, USA. popescu@igbb.msstate.edu.', 'United States|United States|United States|United States|United States', 5, 'Humans|Linear Models|Peptides|Proteomics', 'Journal Article', '10.1186/s12859-019-2619-6', '30871482', 'Several methods to handle data generated from bottom-up proteomics via liquid chromatography-mass spectrometry, particularly for peptide-centric quantification dealing with post-translational modification (PTM) analysis like reversible cysteine oxidation are evaluated. The paper proposes a pipeline based on the R programming language to analyze PTMs from peptide-centric label-free quantitative proteomics data.'),
(98887, 2019, 'bmc', 'Network meta-analysis correlates with analysis of merged independent transcriptome expression data', 'Winter, Christine|Kosch, Robin|Ludlow, Martin|Osterhaus, Albert D M E|Jung, Klaus', 'Institute for Animal Breeding and Genetics, University of Veterinary Medicine Hannover, Bünteweg 17p, Hannover, 30559, Germany.|Institute for Animal Breeding and Genetics, University of Veterinary Medicine Hannover, Bünteweg 17p, Hannover, 30559, Germany.|Research Center for Emerging Infections and Zoonoses, University of Veterinary Medicine Hannover, Bünteweg 17p, Hannover, 30559, Germany.|Research Center for Emerging Infections and Zoonoses, University of Veterinary Medicine Hannover, Bünteweg 17p, Hannover, 30559, Germany.|Institute for Animal Breeding and Genetics, University of Veterinary Medicine Hannover, Bünteweg 17p, Hannover, 30559, Germany. klaus.jung@tiho-hannover.de.', 'Germany|Germany|Germany|Germany|Germany', 2, 'Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Gene Regulatory Networks|Humans|Network Meta-Analysis|Transcriptome', 'Journal Article', '10.1186/s12859-019-2705-9', '30876387', 'Using meta-analysis, high-dimensional transcriptome expression data from public repositories can be merged to make group comparisons that have not been considered in the original studies. Merging of high-dimensional expression data can, however, implicate batch effects that are sometimes difficult to be removed. Removing batch effects becomes even more difficult when expression data was taken using different technologies in the individual studies (e.g. merging of microarray and RNA-seq data). Network meta-analysis has so far not been considered to make indirect comparisons in transcriptome expression data, when data merging appears to yield biased results.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(98888, 2019, 'bmc', 'Identifying miRNA-mRNA regulatory relationships in breast cancer with invariant causal prediction', 'Pham, Vu Vh|Zhang, Junpeng|Liu, Lin|Truong, Buu|Xu, Taosheng|Nguyen, Trung T|Li, Jiuyong|Le, Thuc D', 'School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, Australia.|School of Engineering, Dali University, Dali, Yunnan, China.|School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, Australia.|Pham Ngoc Thach University of Medicine, Ho Chi Minh, Vietnam.|Institute of Intelligent Machines, Heifei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.|School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, Australia. Thuc.Le@unisa.edu.au.', 'Australia|China|Australia|-|China|Australia|Australia|Australia', 3, 'Breast Neoplasms|Computational Biology|Databases, Genetic|Female|Gene Regulatory Networks|Humans|MicroRNAs|RNA, Messenger|Reproducibility of Results', 'Journal Article', '10.1186/s12859-019-2668-x', '30876399', 'microRNAs (miRNAs) regulate gene expression at the post-transcriptional level and they play an important role in various biological processes in the human body. Therefore, identifying their regulation mechanisms is essential for the diagnostics and therapeutics for a wide range of diseases. There have been a large number of researches which use gene expression profiles to resolve this problem. However, the current methods have their own limitations. Some of them only identify the correlation of miRNA and mRNA expression levels instead of the causal or regulatory relationships while others infer the causality but with a high computational complexity. To overcome these issues, in this study, we propose a method to identify miRNA-mRNA regulatory relationships in breast cancer using the invariant causal prediction. The key idea of invariant causal prediction is that the cause miRNAs of their target mRNAs are the ones which have persistent causal relationships with the target mRNAs across different environments.'),
(98889, 2019, 'bmc', 'FitEllipsoid: a fast supervised ellipsoid segmentation plugin', 'Kovac, Bastien|Fehrenbach, Jérôme|Guillaume, Ludivine|Weiss, Pierre', 'ITAV, CNRS, Université de Toulouse, 1 Pl. Pierre Potier, Toulouse, 31106, France.|ITAV, CNRS, Université de Toulouse, 1 Pl. Pierre Potier, Toulouse, 31106, France. jerome.fehrenbach@math.univ-toulouse.fr.|ITAV, CNRS, Université de Toulouse, 1 Pl. Pierre Potier, Toulouse, 31106, France.|ITAV, CNRS, Université de Toulouse, 1 Pl. Pierre Potier, Toulouse, 31106, France.', 'France|France|France|France', 0, 'Algorithms|Bridged Bicyclo Compounds, Heterocyclic|Cell Nucleus|Humans|Imaging, Three-Dimensional|Spheroids, Cellular|Thiazolidines|Tumor Cells, Cultured', 'Journal Article', '10.1186/s12859-019-2673-0', '30876406', 'The segmentation of a 3D image is a task that can hardly be automatized in certain situations, notably when the contrast is low and/or the distance between elements is small. The existing supervised methods require a high amount of user input, e.g. delineating the domain in all planar sections.'),
(98890, 2019, 'bmc', 'GMASS: a novel measure for genome assembly structural similarity', 'Kwon, Daehong|Lee, Jongin|Kim, Jaebum', 'Department of Biomedical Science and Engineering, Konkuk University, Seoul, 05029, South Korea.|Department of Biomedical Science and Engineering, Konkuk University, Seoul, 05029, South Korea.|Department of Biomedical Science and Engineering, Konkuk University, Seoul, 05029, South Korea. jbkim@konkuk.ac.kr.', 'United Kingdom|United Kingdom|United Kingdom', 1, 'Databases, Genetic|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Models, Theoretical|Molecular Structure|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-019-2710-z', '30885117', 'Thanks to the recent advancements in next-generation sequencing (NGS) technologies, large amount of genomic data, which are short DNA sequences known as reads, has been accumulating. Diverse assemblers have been developed to generate high quality de novo assemblies using the NGS reads, but their output is very different because of algorithmic differences. However, there are not properly structured measures to show the similarity or difference in assemblies.'),
(98891, 2019, 'bmc', 'MGSEA - a multivariate Gene set enrichment analysis', 'Tiong, Khong-Loon|Yeang, Chen-Hsiang', 'Institute of Statistical Science, Academia Sinica, Taipei, Taiwan.|Institute of Statistical Science, Academia Sinica, Taipei, Taiwan. chyeang@stat.sinica.edu.tw.', '-|-', 2, 'Biomarkers, Tumor|Brain Neoplasms|Breast Neoplasms|Cell Proliferation|DNA Methylation|Databases, Genetic|Epithelial-Mesenchymal Transition|Gene Expression Regulation, Neoplastic|Glioblastoma|Humans|Models, Theoretical|Multivariate Analysis', 'Journal Article', '10.1186/s12859-019-2716-6', '30885118', 'Gene Set Enrichment Analysis (GSEA) is a powerful tool to identify enriched functional categories of informative biomarkers. Canonical GSEA takes one-dimensional feature scores derived from the data of one platform as inputs. Numerous extensions of GSEA handling multimodal OMIC data are proposed, yet none of them explicitly captures combinatorial relations of feature scores from multiple platforms.'),
(98892, 2019, 'bmc', 'Replicability analysis in genome-wide association studies via Cartesian hidden Markov models', 'Wang, Pengfei|Zhu, Wensheng', 'Key Laboratory for Applied Statistics of MOE, School of Mathematics and Statistics, Northeast Normal University, 5268 Renmin Street, Changchun, 130024, China.|Key Laboratory for Applied Statistics of MOE, School of Mathematics and Statistics, Northeast Normal University, 5268 Renmin Street, Changchun, 130024, China. wszhu@nenu.edu.cn.', 'China|China', 0, 'Bayes Theorem|Genome-Wide Association Study|Genomics|Humans|Markov Chains|Models, Theoretical|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/s12859-019-2707-7', '30885122', 'Replicability analysis which aims to detect replicated signals attracts more and more attentions in modern scientific applications. For example, in genome-wide association studies (GWAS), it would be of convincing to detect an association which can be replicated in more than one study. Since the neighboring single nucleotide polymorphisms (SNPs) often exhibit high correlation, it is desirable to exploit the dependency information among adjacent SNPs properly in replicability analysis. In this paper, we propose a novel multiple testing procedure based on the Cartesian hidden Markov model (CHMM), called repLIS procedure, for replicability analysis across two studies, which can characterize the local dependence structure among adjacent SNPs via a four-state Markov chain.'),
(98893, 2019, 'bmc', 'A likelihood ratio test for changes in homeolog expression bias', 'Smith, Ronald D|Kinser, Taliesin J|Smith, Gregory D Conradi|Puzey, Joshua R', 'Department of Applied Science, The College of William & Mary, Williamsburg, 23187, VA, USA.|Department of Biology, The College of William & Mary, Williamsburg, 23187, VA, USA.|Department of Applied Science, The College of William & Mary, Williamsburg, 23187, VA, USA.|Department of Biology, The College of William & Mary, Williamsburg, 23187, VA, USA. jrpuzey@wm.edu.', 'United States|United States|United States|United States', 1, 'Evolution, Molecular|Gene Duplication|Gene Expression Profiling|Gene Expression Regulation, Plant|Genes, Plant|Genome, Plant|Mimulus|Polyploidy|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-019-2709-5', '30894122', 'Gene duplications are a major source of raw material for evolution and a likely contributor to the diversity of life on earth. Duplicate genes (i.e., homeologs, in the case of a whole genome duplication) may retain their ancestral function, sub- or neofunctionalize, or be lost entirely. A primary way that duplicate genes evolve new functions is by altering their expression patterns. Comparing the expression patterns of duplicate genes gives clues as to whether any of these evolutionary processes have occurred.'),
(98894, 2019, 'bmc', 'FitTetra 20 - improved genotype calling for tetraploids with multiple population and parental data support', 'Zych, Konrad|Gort, Gerrit|Maliepaard, Chris A|Jansen, Ritsert C|Voorrips, Roeland E', 'Groningen Bioinformatics Centre, University of Groningen, Groningen, The Netherlands.|Wageningen University and Research - Biometris, Wageningen, The Netherlands.|Wageningen University and Research - Plant Breeding, Wageningen, The Netherlands.|Groningen Bioinformatics Centre, University of Groningen, Groningen, The Netherlands.|Wageningen University and Research - Plant Breeding, Wageningen, The Netherlands. roeland.voorrips@wur.nl.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 2, 'Algorithms|Alleles|Genetics, Population|Genotype|Humans|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide|Software|Tetraploidy', 'Journal Article', '10.1186/s12859-019-2703-y', '30894135', 'Genetic studies in tetraploids are lagging behind in comparison with studies of diploids as the complex genetics of tetraploids require much more elaborated computational methodologies. Recent advancements in development of molecular techniques and computational tools facilitate new methods for automated, high-throughput genotype calling in tetraploid species. We report on the upgrade of the widely-used fitTetra software aiming to improve its accuracy, which to date is hampered by technical artefacts in the data.'),
(98895, 2019, 'bmc', 'SSS-test: a novel test for detecting positive selection on RNA secondary structure', 'Walter Costa, Maria Beatriz|Hxc3xb6ner Zu Siederdissen, Christian|Dunjixc4x87, Marko|Stadler, Peter F|Nowick, Katja', 'Embrapa Agroenergia, Parque Estaxc3xa7xc3xa3o Biolxc3xb3gica (PqEB), Asa Norte, Brasxc3xadlia, DF, 70770-901, Brazil. bia.walter@gmail.com.|Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, Universitxc3xa4t Leipzig, Hxc3xa4rtelstraxc3x9fe 16-18, Leipzig, 04107, Germany.|Human Biology Group, Institute for Biology, Department of Biology, Chemistry, Pharmacy, Freie Universitaet Berlin, Kxc3xb6nigin-Luise-Straxc3x9fe 1-3, Berlin, 14195, Germany.|Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, Universitxc3xa4t Leipzig, Hxc3xa4rtelstraxc3x9fe 16-18, Leipzig, 04107, Germany.|Human Biology Group, Institute for Biology, Department of Biology, Chemistry, Pharmacy, Freie Universitaet Berlin, Kxc3xb6nigin-Luise-Straxc3x9fe 1-3, Berlin, 14195, Germany. katja.nowick@fu-berlin.de.', 'Brazil|Germany|Germany|Germany|Germany', 1, 'Humans|Protein Structure, Secondary|RNA', 'Journal Article', '10.1186/s12859-019-2711-y', '30898084', 'Long non-coding RNAs (lncRNAs) play an important role in regulating gene expression and are thus important for determining phenotypes. Most attempts to measure selection in lncRNAs have focused on the primary sequence. The majority of small RNAs and at least some parts of lncRNAs must fold into specific structures to perform their biological function. Comprehensive assessments of selection acting on RNAs therefore must also encompass structure. Selection pressures acting on the structure of non-coding genes can be detected within multiple sequence alignments. Approaches of this type, however, have so far focused on negative selection. Thus, a computational method for identifying ncRNAs under positive selection is needed.'),
(98896, 2019, 'bmc', 'UTAP: User-friendly Transcriptome Analysis Pipeline', 'Kohen, Refael|Barlev, Jonathan|Hornung, Gil|Stelzer, Gil|Feldmesser, Ester|Kogan, Kiril|Safran, Marilyn|Leshkowitz, Dena', 'Bioinformatics Unit, Department of Life Sciences Core Facilities, Weizmann Institute of Science, 76100, Rehovot, Israel. refael.kohen@weizmann.ac.il.|The Mantoux Bioinformatics institute of the Nancy and Stephen Grand Israel National Center for Personalized Medicine, Department of Life Sciences Core Facilities, Weizmann Institute of Science, 76100, Rehovot, Israel.|The Mantoux Bioinformatics institute of the Nancy and Stephen Grand Israel National Center for Personalized Medicine, Department of Life Sciences Core Facilities, Weizmann Institute of Science, 76100, Rehovot, Israel.|Bioinformatics Unit, Department of Life Sciences Core Facilities, Weizmann Institute of Science, 76100, Rehovot, Israel.|Bioinformatics Unit, Department of Life Sciences Core Facilities, Weizmann Institute of Science, 76100, Rehovot, Israel.|Bioinformatics Unit, Department of Life Sciences Core Facilities, Weizmann Institute of Science, 76100, Rehovot, Israel.|Bioinformatics Unit, Department of Life Sciences Core Facilities, Weizmann Institute of Science, 76100, Rehovot, Israel.|Bioinformatics Unit, Department of Life Sciences Core Facilities, Weizmann Institute of Science, 76100, Rehovot, Israel. dena.leshkowitz@weizmann.ac.il.', 'Israel|Israel|Israel|Israel|Israel|Israel|Israel|Israel', 12, 'Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/s12859-019-2728-2', '30909881', 'RNA-Seq technology is routinely used to characterize the transcriptome, and to detect gene expression differences among cell types, genotypes and conditions. Advances in short-read sequencing instruments such as Illumina Next-Seq have yielded easy-to-operate machines, with high throughput, at a lower price per base. However, processing this data requires bioinformatics expertise to tailor and execute specific solutions for each type of library preparation.'),
(98897, 2019, 'bmc', 'A new massively parallel nanoball sequencing platform for whole exome research', 'Xu, Yu|Lin, Zhe|Tang, Chong|Tang, Yujing|Cai, Yue|Zhong, Hongbin|Wang, Xuebin|Zhang, Wenwei|Xu, Chongjun|Wang, Jingjing|Wang, Jian|Yang, Huanming|Yang, Linfeng|Gao, Qiang', 'BGI-Genomics, BGI-Shenzhen, Shenzhen, 518083, China.|BGI-Genomics, BGI-Shenzhen, Shenzhen, 518083, China.|BGI-Genomics, BGI-Shenzhen, Shenzhen, 518083, China.|BGI-Genomics, BGI-Shenzhen, Shenzhen, 518083, China.|BGI-Genomics, BGI-Shenzhen, Shenzhen, 518083, China.|BGI-Genomics, BGI-Shenzhen, Shenzhen, 518083, China.|BGI-Genomics, BGI-Shenzhen, Shenzhen, 518083, China.|BGI-Shenzhen, Shenzhen, 518083, China.|Complete Genomics, Inc, Mountain View, CA, 94043, USA.|BGI-Shenzhen, Shenzhen, 518083, China.|BGI-Shenzhen, Shenzhen, 518083, China.|BGI-Shenzhen, Shenzhen, 518083, China.|BGI-Genomics, BGI-Shenzhen, Shenzhen, 518083, China. yanglinfeng@genomics.cn.|BGI-Genomics, BGI-Shenzhen, Shenzhen, 518083, China. gaoqiang@genomics.cn.', 'China|China|China|China|China|China|China|China|United States|China|China|China|China|China', 8, 'Genetic Variation|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|INDEL Mutation|Reproducibility of Results|Sensitivity and Specificity|Whole Exome Sequencing', 'Evaluation Studies', '10.1186/s12859-019-2751-3', '30909888', 'Whole exome sequencing (WES) has been widely used in human genetics research. BGISEQ-500 is a recently established next-generation sequencing platform. However, the performance of BGISEQ-500 on WES is not well studied. In this study, we evaluated the performance of BGISEQ-500 on WES by side-to-side comparison with Hiseq4000, on well-characterized human sample NA12878.'),
(98898, 2019, 'bmc', 'Predicting enhancers in mammalian genomes using supervised hidden Markov models', 'Zehnder, Tobias|Benner, Philipp|Vingron, Martin', 'Max Planck Institute for Molecular Genetics, Ihnestraße 63-73, Berlin, 14195, Germany. zehnder@molgen.mpg.de.|Max Planck Institute for Molecular Genetics, Ihnestraße 63-73, Berlin, 14195, Germany.|Max Planck Institute for Molecular Genetics, Ihnestraße 63-73, Berlin, 14195, Germany.', 'Germany|Germany|Germany', 1, 'Animals|Base Sequence|DNA Methylation|Embryonic Stem Cells|Enhancer Elements, Genetic|Genome|Genomics|Histones|Humans|Mammals|Markov Chains|Mice|Models, Genetic|Promoter Regions, Genetic|Reproducibility of Results', 'Journal Article', '10.1186/s12859-019-2708-6', '30917778', 'Eukaryotic gene regulation is a complex process comprising the dynamic interaction of enhancers and promoters in order to activate gene expression. In recent years, research in regulatory genomics has contributed to a better understanding of the characteristics of promoter elements and for most sequenced model organism genomes there exist comprehensive and reliable promoter annotations. For enhancers, however, a reliable description of their characteristics and location has so far proven to be elusive. With the development of high-throughput methods such as ChIP-seq, large amounts of data about epigenetic conditions have become available, and many existing methods use the information on chromatin accessibility or histone modifications to train classifiers in order to segment the genome into functional groups such as enhancers and promoters. However, these methods often do not consider prior biological knowledge about enhancers such as their diverse lengths or molecular structure.'),
(98899, 2019, 'bmc', 'GO functional similarity clustering depends on similarity measure, clustering method, and annotation completeness', 'Liu, Meng|Thomas, Paul D', 'Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, USA.|Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, USA. pdthomas@usc.edu.', 'United States|United States', 6, 'Algorithms|Area Under Curve|Cluster Analysis|Computational Biology|Gene Ontology|Humans|Molecular Sequence Annotation|Semantics', 'Journal Article', '10.1186/s12859-019-2752-2', '30917779', 'Biological knowledge, and therefore Gene Ontology annotation sets, for human genes is incomplete. Recent studies have reported that biases in available GO annotations result in biased estimates of functional similarities of genes, but it is still unclear what the effect of incompleteness itself may be, even in the absence of bias. Pairwise gene similarities are used in a number of contexts, including gene functional similarity clustering and the related problem of functional ontology structure inference, but it is not known how different similarity measures or clustering methods perform on this task, and how the clusters are affected by annotation completeness.'),
(98900, 2019, 'bmc', 'Linking entities through an ontology using word embeddings and syntactic re-ranking', 'Karadeniz, xc4xb0lknur|xc3x96zgxc3xbcr, Arzucan', 'Department of Computer Engineering, Boxc4x9fazixc3xa7i University, xc4xb0stanbul, 34342, Turkey.|Department of Computer Engineering, Boxc4x9fazixc3xa7i University, xc4xb0stanbul, 34342, Turkey. arzucan.ozgur@boun.edu.tr.', 'Turkey|Turkey', 4, 'Algorithms|Bacteria|Data Mining|Drug-Related Side Effects and Adverse Reactions|Reference Standards|Semantics|Software', 'Journal Article', '10.1186/s12859-019-2678-8', '30917789', 'Although there is an enormous number of textual resources in the biomedical domain, currently, manually curated resources cover only a small part of the existing knowledge. The vast majority of these information is in unstructured form which contain nonstandard naming conventions. The task of named entity recognition, which is the identification of entity names from text, is not adequate without a standardization step. Linking each identified entity mention in text to an ontology/dictionary concept is an essential task to make sense of the identified entities. This paper presents an unsupervised approach for the linking of named entities to concepts in an ontology/dictionary. We propose an approach for the normalization of biomedical entities through an ontology/dictionary by using word embeddings to represent semantic spaces, and a syntactic parser to give higher weight to the most informative word in the named entity mentions.'),
(98901, 2019, 'bmc', 'High throughput automatic muscle image segmentation using parallel framework', 'Cui, Lei|Feng, Jun|Zhang, Zizhao|Yang, Lin', 'Department of Information Science and Technology, Northwest University, Xian, China.|Department of Information Science and Technology, Northwest University, Xian, China. fengjun@nwu.edu.cn.|Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, USA.|Department of Information Science and Technology, Northwest University, Xian, China.', 'China|China|United States|China', 3, 'Algorithms|Image Processing, Computer-Assisted|Muscle, Skeletal', 'Journal Article', '10.1186/s12859-019-2719-3', '30922212', 'Fast and accurate automatic segmentation of skeletal muscle cell image is crucial for the diagnosis of muscle related diseases, which extremely reduces the labor-intensive manual annotation. Recently, several methods have been presented for automatic muscle cell segmentation. However, most methods exhibit high model complexity and time cost, and they are not adaptive to large-scale images such as whole-slide scanned specimens.'),
(98902, 2019, 'bmc', 'PyCellBase, an efficient python package for easy retrieval of biological data from heterogeneous sources', 'Perez-Gil, Daniel|Lopez, Francisco J|Dopazo, Joaquin|Marin-Garcia, Pablo|Rendon, Augusto|Medina, Ignacio', 'Genomics England, London, UK.|Genomics England, London, UK.|Clinical Bioinformatics Area, Fundacion Progreso y Salud, Seville, Spain.|Department of Bioinformatics, Universidad Católica de Valencia, Valencia, Spain.|Genomics England, London, UK.|HPC Service, UIS, University of Cambridge, Cambridge, UK. im411@cam.ac.uk.', 'United Kingdom|United Kingdom|Spain|Spain|United Kingdom|United Kingdom', 0, 'Computational Biology|Databases, Factual|Software|User-Computer Interface', 'Journal Article', '10.1186/s12859-019-2726-4', '30922213', 'Biological databases and repositories are incrementing in diversity and complexity over the years. This rapid expansion of current and new sources of biological knowledge raises serious problems of data accessibility and integration. To handle the growing necessity of unification, CellBase was created as an integrative solution. CellBase provides a centralized NoSQL database containing biological information from different and heterogeneous sources. Access to this information is done through a RESTful web service API, which provides an efficient interface to the data.'),
(98903, 2019, 'bmc', 'A (fire)cloud-based DNA methylation data preprocessing and quality control platform', 'Kangeyan, Divy|Dunford, Andrew|Iyer, Sowmya|Stewart, Chip|Hanna, Megan|Getz, Gad|Aryee, Martin J', 'Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA.|Broad Institute of MIT & Harvard, Cambridge, MA, USA.|Department of Pathology, Massachusetts General Hospital, Boston, MA, USA.|Broad Institute of MIT & Harvard, Cambridge, MA, USA.|Broad Institute of MIT & Harvard, Cambridge, MA, USA.|Broad Institute of MIT & Harvard, Cambridge, MA, USA.|Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA. aryee.martin@mgh.harvard.edu.', 'United States|United States|United States|United States|United States|United States|United States', 2, 'Cloud Computing|DNA Methylation|Databases, Nucleic Acid|Genome, Human|Genomics|Humans|Quality Control|Reproducibility of Results|Sequence Analysis, DNA|Software|Whole Genome Sequencing|Workflow', 'Journal Article', '10.1186/s12859-019-2750-4', '30922215', 'Bisulfite sequencing allows base-pair resolution profiling of DNA methylation and has recently been adapted for use in single-cells. Analyzing these data, including making comparisons with existing data, remains challenging due to the scale of the data and differences in preprocessing methods between published datasets.'),
(98904, 2019, 'bmc', 'A new class of constitutively active super-enhancers is associated with fast recovery of 3D chromatin loops', 'Ryu, Jayoung|Kim, Hyunwoong|Yang, Dongchan|Lee, Andrew J|Jung, Inkyung', 'Department of Biological Sciences, KAIST, Daejeon, 34141, South Korea.|Department of Biological Sciences, KAIST, Daejeon, 34141, South Korea.|Department of Bio and Brain Engineering, KAIST, Daejeon, 34141, South Korea.|Department of Biological Sciences, KAIST, Daejeon, 34141, South Korea.|Department of Biological Sciences, KAIST, Daejeon, 34141, South Korea. ijung@kaist.ac.kr.', '-|-|-|-|-', 2, 'Cell Line|Chromatin|Enhancer Elements, Genetic|Gene Ontology|Genomics|Humans|Nucleic Acid Conformation', 'Journal Article', '10.1186/s12859-019-2646-3', '30925856', 'Super-enhancers or stretch enhancers are clusters of active enhancers that often coordinate cell-type specific gene regulation during development and differentiation. In addition, the enrichment of disease-associated single nucleotide polymorphism in super-enhancers indicates their critical function in disease-specific gene regulation. However, little is known about the function of super-enhancers beyond gene regulation.'),
(98905, 2019, 'bmc', 'reactIDR: evaluation of the statistical reproducibility of high-throughput structural analyses towards a robust RNA structure prediction', 'Kawaguchi, Risa|Kiryu, Hisanori|Iwakiri, Junichi|Sese, Jun', 'Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Aomi, Koto-ku, Tokyo, Japan. kawaguchi-rs@aist.go.jp.|Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, the University of Tokyo, Kashiwanoha, Kashiwa-shi, Chiba, Japan.|Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, the University of Tokyo, Kashiwanoha, Kashiwa-shi, Chiba, Japan.|Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Aomi, Koto-ku, Tokyo, Japan.', 'Japan|Japan|Japan|Japan', 0, 'Algorithms|Area Under Curve|High-Throughput Nucleotide Sequencing|Machine Learning|Markov Chains|Nucleic Acid Conformation|Nucleotides|RNA|RNA, Ribosomal|ROC Curve|Reproducibility of Results|Statistics as Topic', 'Journal Article', '10.1186/s12859-019-2645-4', '30925857', 'Recently, next-generation sequencing techniques have been applied for the detection of RNA secondary structures, which is referred to as high-throughput RNA structural (HTS) analyses, and many different protocols have been used to detect comprehensive RNA structures at single-nucleotide resolution. However, the existing computational analyses heavily depend on the experimental methodology to generate data, which results in difficulties associated with statistically sound comparisons or combining the results obtained using different HTS methods.'),
(98906, 2019, 'bmc', 'Prediction of drug-disease associations based on ensemble meta paths and singular value decomposition', 'Wu, Guangsheng|Liu, Juan|Yue, Xiang', 'School of Computer Science, Wuhan University, Wuhan, 430072, Peoples Republic of China.|School of Computer Science, Wuhan University, Wuhan, 430072, Peoples Republic of China. liujuan@whu.edu.cn.|School of Computer Science, Wuhan University, Wuhan, 430072, Peoples Republic of China.', 'China|China|China', 0, 'Algorithms|Computational Biology|Disease|Humans|Pharmaceutical Preparations|Proteins|ROC Curve', 'Journal Article', '10.1186/s12859-019-2644-5', '30925858', 'In the field of drug repositioning, it is assumed that similar drugs may treat similar diseases, therefore many existing computational methods need to compute the similarities of drugs and diseases. However, the calculation of similarity depends on the adopted measure and the available features, which may lead that the similarity scores vary dramatically from one to another, and it will not work when facing the incomplete data. Besides, supervised learning based methods usually need both positive and negative samples to train the prediction models, whereas in drug-disease pairs data there are only some verified interactions (positive samples) and a lot of unlabeled pairs. To train the models, many methods simply treat the unlabeled samples as negative ones, which may introduce artificial noises. Herein, we propose a method to predict drug-disease associations without the need of similarity information, and select more likely negative samples.'),
(98907, 2019, 'bmc', 'SplicedFamAlign: CDS-to-gene spliced alignment and identification of transcript orthology groups', 'Jammali, Safa|Aguilar, Jean-David|Kuitche, Esaie|Ouangraoua, Aïda', 'Department of Computer science, Faculty of Science, Université de Sherbrooke, Sherbrooke, Quebec, Canada. safa.jammali@usherbrooke.ca.|Department of Computer science, Faculty of Science, Université de Sherbrooke, Sherbrooke, Quebec, Canada.|Department of Computer science, Faculty of Science, Université de Sherbrooke, Sherbrooke, Quebec, Canada.|Department of Computer science, Faculty of Science, Université de Sherbrooke, Sherbrooke, Quebec, Canada.', 'Canada|Canada|Canada|Canada', 0, 'Algorithms|Alternative Splicing|Base Sequence|Computer Simulation|Exons|Introns|Molecular Sequence Annotation|Open Reading Frames|RNA, Messenger|Sequence Alignment', 'Journal Article', '10.1186/s12859-019-2647-2', '30925859', 'The inference of splicing orthology relationships between gene transcripts is a basic step for the prediction of transcripts and the annotation of gene structures in genomes. The splicing structure of a sequence refers to the exon extremity information in a CDS or the exon-intron extremity information in a gene sequence. Splicing orthologous CDS are pairs of CDS with similar sequences and conserved splicing structures from orthologous genes. Spliced alignment that consists in aligning a spliced cDNA sequence against an unspliced genomic sequence, constitutes a promising, yet unexplored approach for the identification of splicing orthology relationships. Existing spliced alignment algorithms do not exploit the information on the splicing structure of the input sequences, namely the exon structure of the cDNA sequence and the exon-intron structure of the genomic sequences. Yet, this information is often available for coding DNA sequences (CDS) and gene sequences annotated in databases, and it can help improve the accuracy of the computed spliced alignments. To address this issue, we introduce a new spliced alignment problem and a method called SplicedFamAlign (SFA) for computing the alignment of a spliced CDS against a gene sequence while accounting for the splicing structures of the input sequences, and then the inference of transcript splicing orthology groups in a gene family based on spliced alignments.'),
(98908, 2019, 'bmc', 'Automatic localization and identification of mitochondria in cellular electron cryo-tomography using faster-RCNN', 'Li, Ran|Zeng, Xiangrui|Sigmund, Stephanie E|Lin, Ruogu|Zhou, Bo|Liu, Chang|Wang, Kaiwen|Jiang, Rui|Freyberg, Zachary|Lv, Hairong|Xu, Min', 'Department of Automation, Tsinghua University, Beijing, China.|Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA.|Department of Cellular, Molecular and Biophysical Studies, Columbia University Medical Center, New York, NY, USA.|Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA.|Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA.|Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.|Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.|Department of Automation, Tsinghua University, Beijing, China.|Departments of Psychiatry and Cell Biology, University of Pittsburgh, Pittsburgh, PA, USA. freybergzz@upmc.edu.|Department of Automation, Tsinghua University, Beijing, China. lihua@ict.ac.cn.|Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA. mxu1@cs.cmu.edu.', 'China|United States|United States|United States|United States|United States|United States|China|United States|China|United States', 8, 'Algorithms|Animals|Automation|Cell Line|Cryoelectron Microscopy|Databases as Topic|Electron Microscope Tomography|Image Processing, Computer-Assisted|Mitochondria|Models, Theoretical|Neural Networks (Computer)|Rats|Signal-To-Noise Ratio', 'Journal Article', '10.1186/s12859-019-2650-7', '30925860', 'Cryo-electron tomography (cryo-ET) enables the 3D visualization of cellular organization in near-native state which plays important roles in the field of structural cell biology. However, due to the low signal-to-noise ratio (SNR), large volume and high content complexity within cells, it remains difficult and time-consuming to localize and identify different components in cellular cryo-ET. To automatically localize and recognize in situ cellular structures of interest captured by cryo-ET, we proposed a simple yet effective automatic image analysis approach based on Faster-RCNN.'),
(98909, 2019, 'bmc', 'Identification of trans-eQTLs using mediation analysis with multiple mediators', 'Shan, Nayang|Wang, Zuoheng|Hou, Lin', 'Center for Statistical Science, Tsinghua University, Beijing, 100084, China.|Department of Biostatistics, Yale School of Public Health, New Haven, CT, 06510, USA. zuoheng.wang@yale.edu.|Center for Statistical Science, Tsinghua University, Beijing, 100084, China. houl@tsinghua.edu.cn.', 'China|United States|China', 2, 'Chromosome Mapping|Computer Simulation|Databases, Genetic|Gene Expression Regulation|Haplotypes|Humans|Polymorphism, Single Nucleotide|Quantitative Trait Loci', 'Journal Article', '10.1186/s12859-019-2651-6', '30925861', 'Mapping expression quantitative trait loci (eQTLs) has provided insight into gene regulation. Compared to cis-eQTLs, the regulatory mechanisms of trans-eQTLs are less known. Previous studies suggest that trans-eQTLs may regulate expression of remote genes by altering the expression of nearby genes. Trans-association has been studied in the mediation analysis with a single mediator. However, prior applications with one mediator are prone to model misspecification due to correlations between genes. Motivated from the observation that trans-eQTLs are more likely to associate with more than one cis-gene than randomly selected SNPs in the GTEx dataset, we developed a computational method to identify trans-eQTLs that are mediated by multiple mediators.'),
(98910, 2019, 'bmc', 'Detecting virus-specific effects on post-infection temporal gene expression', 'Chen, Quan|Zhu, Jun', 'Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, 10029, NY, USA.|Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, 10029, NY, USA. jun.zhu@mssm.edu.', 'United States|United States', 0, 'Animals|Female|Gene Expression Profiling|Gene Expression Regulation|Gene Ontology|Influenza A Virus, H3N2 Subtype|Lung|Mice, Inbred C57BL|Multivariate Analysis|Orthomyxoviridae Infections|Time Factors|Transcriptome', 'Journal Article', '10.1186/s12859-019-2653-4', '30925863', 'Different types of viruses have different envelope proteins, and may have their shared or distinctive host-virus interactions which result in various post-infection effects in humans and animals. These effects often do not appear at once but take time to unfold. To characterize the virus-specific effects, we applied a Multivariate Polynomial Time-dependent Genetic Association (MPTGA) method, previously proposed for detecting differences in temporal gene expression traits, to test for the differences in mouse lung transcriptome response to infection of different subtypes of influenza A viruses.'),
(98911, 2019, 'bmc', 'RCPred: RNA complex prediction as a constrained maximum weight clique problem', 'Legendre, Audrey|Angel, Eric|Tahi, Fariza', 'IBISC, Univ Evry, Université Paris-Saclay, Evry, 91025, France.|IBISC, Univ Evry, Université Paris-Saclay, Evry, 91025, France.|IBISC, Univ Evry, Université Paris-Saclay, Evry, 91025, France. fariza.tahi@univ-evry.fr.', 'France|France|France', 1, 'Algorithms|Databases, Nucleic Acid|Nucleic Acid Conformation|RNA|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-019-2648-1', '30925864', 'RNAs can interact and form complexes, which have various biological roles. The secondary structure prediction of those complexes is a first step towards the identification of their 3D structure. We propose an original approach that takes advantage of the high number of RNA secondary structure and RNA-RNA interaction prediction tools. We formulate the problem of RNA complex prediction as the determination of the best combination (according to the free energy) of predicted RNA secondary structures and RNA-RNA interactions.'),
(98912, 2019, 'bmc', 'Protein complex detection based on flower pollination mechanism in multi-relation reconstructed dynamic protein networks', 'Lei, Xiujuan|Fang, Ming|Guo, Ling|Wu, Fang-Xiang', 'School of Computer Science, Shaanxi Normal University, 710119, Xian, China. xjlei@snnu.edu.cn.|School of Computer Science, Shaanxi Normal University, 710119, Xian, China.|College of Life Sciences, Shaanxi Normal University, 710119, Xian, China.|Department of Mechanical Engineering and Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK, S7N 5A9, Canada.', 'China|China|China|Canada', 0, 'Algorithms|Computational Biology|Databases, Protein|Flowers|Golgi Apparatus|Plant Proteins|Pollination|Protein Interaction Mapping|Protein Interaction Maps', 'Journal Article', '10.1186/s12859-019-2649-0', '30925866', 'Detecting protein complex in protein-protein interaction (PPI) networks plays a significant part in bioinformatics field. It enables us to obtain the better understanding for the structures and characteristics of biological systems.'),
(98913, 2019, 'bmc', 'Constructing effective energy functions for protein structure prediction through broadening attraction-basin and reverse Monte Carlo sampling', 'Wang, Chao|Wei, Yi|Zhang, Haicang|Kong, Lupeng|Sun, Shiwei|Zheng, Wei-Mou|Bu, Dongbo', 'Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, 6, Kexueyuan South Road, Zhongguancun, Beijing, 100190, China.|Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, 6, Kexueyuan South Road, Zhongguancun, Beijing, 100190, China.|Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, 6, Kexueyuan South Road, Zhongguancun, Beijing, 100190, China.|Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, 6, Kexueyuan South Road, Zhongguancun, Beijing, 100190, China.|Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, 6, Kexueyuan South Road, Zhongguancun, Beijing, 100190, China.|University of Chinese Academy of Sciences, 19-1, Yuquan Road, Shijingshan, Beijing, 100049, China. zheng@itp.ac.cn.|Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, 6, Kexueyuan South Road, Zhongguancun, Beijing, 100190, China. dbu@ict.ac.cn.', 'China|China|China|China|China|China|China', 0, 'Algorithms|Computational Biology|Databases, Protein|Monte Carlo Method|Protein Conformation|Proteins|Thermodynamics', 'Journal Article', '10.1186/s12859-019-2652-5', '30925867', 'The ab initio approaches to protein structure prediction usually employ the Monte Carlo technique to search the structural conformation that has the lowest energy. However, the widely-used energy functions are usually ineffective for conformation search. How to construct an effective energy function remains a challenging task.'),
(98914, 2019, 'bmc', 'A statistical normalization method and differential expression analysis for RNA-seq data between different species', 'Zhou, Yan|Zhu, Jiadi|Tong, Tiejun|Wang, Junhui|Lin, Bingqing|Zhang, Jun', 'College of Mathematics and Statistics, Institute of Statistical Sciences, Shenzhen University, Shenzhen, 518060, China.|College of Mathematics and Statistics, Institute of Statistical Sciences, Shenzhen University, Shenzhen, 518060, China.|Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong.|School of Data Science, City University of Hong Kong, Kowloon Tong, Hong Kong.|College of Mathematics and Statistics, Institute of Statistical Sciences, Shenzhen University, Shenzhen, 518060, China.|College of Mathematics and Statistics, Institute of Statistical Sciences, Shenzhen University, Shenzhen, 518060, China. zhangjunstat@gmail.com.', 'China|China|Hong Kong|Hong Kong|China|China', 0, 'Animals|Computer Simulation|Gene Expression Profiling|Humans|Mice|Sequence Analysis, RNA|Species Specificity|Statistics as Topic', 'Journal Article', '10.1186/s12859-019-2745-1', '30925894', 'High-throughput techniques bring novel tools and also statistical challenges to genomic research. Identifying genes with differential expression between different species is an effective way to discover evolutionarily conserved transcriptional responses. To remove systematic variation between different species for a fair comparison, normalization serves as a crucial pre-processing step that adjusts for the varying sample sequencing depths and other confounding technical effects.'),
(98915, 2019, 'bmc', 'Data and knowledge management in translational research: implementation of the eTRIKS platform for the IMI OncoTrack consortium', 'Gu, Wei|Yildirimman, Reha|Van der Stuyft, Emmanuel|Verbeeck, Denny|Herzinger, Sascha|Satagopam, Venkata|Barbosa-Silva, Adriano|Schneider, Reinhard|Lange, Bodo|Lehrach, Hans|Guo, Yike|Henderson, David|Rowe, Anthony|IMI OncoTrack and the IMI eTRIKS consortia', 'Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg.|Alacris Theranostics GmbH, Berlin, Germany.|Janssen Pharmaceutica NV, Beerse, Belgium.|Janssen Pharmaceutica NV, Beerse, Belgium.|Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg.|Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg.|Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg.|Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg.|Alacris Theranostics GmbH, Berlin, Germany.|Alacris Theranostics GmbH, Berlin, Germany.|Data Science Institute, Imperial College London, London, UK.|Bayer AG, Berlin, Germany. david.henderson@bayer.com.|Janssen Research and Development Ltd, High Wycombe, UK. arowe4@its.jnj.com.|Janssen Research and Development Ltd, High Wycombe, UK. arowe4@its.jnj.com.', 'Luxembourg|Germany|Belgium|Belgium|Luxembourg|Luxembourg|Luxembourg|Luxembourg|Germany|Germany|United Kingdom|Ger', 1, 'Animals|Cells, Cultured|Computer Simulation|Databases, Factual|Disease Models, Animal|Humans|Knowledge Management|Models, Biological|Proteomics|Software|Systems Biology|Translational Medical Research|Whole Genome Sequencing|Xenograft Model Antitumor Assays', 'Journal Article', '10.1186/s12859-019-2748-y', '30935364', 'For large international research consortia, such as those funded by the European Unions Horizon 2020 programme or the Innovative Medicines Initiative, good data coordination practices and tools are essential for the successful collection, organization and analysis of the resulting data. Research consortia are attempting ever more ambitious science to better understand disease, by leveraging technologies such as whole genome sequencing, proteomics, patient-derived biological models and computer-based systems biology simulations.'),
(98916, 2019, 'bmc', 'AUTALASSO: an automatic adaptive LASSO for genome-wide prediction', 'Waldmann, Patrik|Ferenxc4x8dakovixc4x87, Maja|Mxc3xa9szxc3xa1ros, Gxc3xa1bor|Khayatzadeh, Negar|Curik, Ino|Sxc3xb6lkner, Johann', 'Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Box 7023, Uppsala, 750 07, Sweden. Patrik.Waldmann@slu.se.|Department of Animal Science, Faculty of Agriculture, University of Zagreb, Svetosimunska 25, Zagreb, 10000, Croatia.|Division of Livestock Sciences,Department of Sustainable Agricultural Systems,University of Natural Resources and Life Sciences Vienna, Gregor Mendel Str. 33, Vienna, A-1180, Austria.|Division of Livestock Sciences,Department of Sustainable Agricultural Systems,University of Natural Resources and Life Sciences Vienna, Gregor Mendel Str. 33, Vienna, A-1180, Austria.|Department of Animal Science, Faculty of Agriculture, University of Zagreb, Svetosimunska 25, Zagreb, 10000, Croatia.|Division of Livestock Sciences,Department of Sustainable Agricultural Systems,University of Natural Resources and Life Sciences Vienna, Gregor Mendel Str. 33, Vienna, A-1180, Austria.', 'Sweden|Croatia|Austria|Austria|Croatia|Austria', 4, 'Algorithms|Animals|Breeding|Cattle|Genome|Genomics|Software', 'Journal Article', '10.1186/s12859-019-2743-3', '30940067', 'Genome-wide prediction has become the method of choice in animal and plant breeding. Prediction of breeding values and phenotypes are routinely performed using large genomic data sets with number of markers on the order of several thousands to millions. The number of evaluated individuals is usually smaller which results in problems where model sparsity is of major concern. The LASSO technique has proven to be very well-suited for sparse problems often providing excellent prediction accuracy. Several computationally efficient LASSO algorithms have been developed, but optimization of hyper-parameters can be demanding.'),
(98917, 2019, 'bmc', 'NITPicker: selecting time points for follow-up experiments', 'Ezer, Daphne|Keir, Joseph', 'Department of Statistics, University of Warwick, Coventry, CV4 7AL, UK. dezer@turing.ac.uk.|Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, CB3 0WA, UK.', 'United Kingdom|United Kingdom', 1, 'Follow-Up Studies|Research Design|Software', 'Journal Article', '10.1186/s12859-019-2717-5', '30940082', 'The design of an experiment influences both what a researcher can measure, as well as how much confidence can be placed in the results. As such, it is vitally important that experimental design decisions do not systematically bias research outcomes. At the same time, making optimal design decisions can produce results leading to statistically stronger conclusions. Deciding where and when to sample are among the most critical aspects of many experimental designs, for example, we might have to choose the time points at which to measure some quantity in a time series experiment. Choosing times which are too far apart could result in missing short bursts of activity. On the other hand, there may be time points which provide very little information regarding the overall behaviour of the quantity in question.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(98918, 2019, 'bmc', 'Haplin power analysis: a software module for power and sample size calculations in genetic association analyses of family triads and unrelated controls', 'Gjerdevik, Miriam|Jugessur, Astanand|Haaland, Øystein A|Romanowska, Julia|Lie, Rolv T|Cordell, Heather J|Gjessing, Håkon K', 'Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway. miriam.gjerdevik@uib.no.|Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway.|Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway.|Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway.|Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway.|Institute of Genetic Medicine, Newcastle University, International Centre for Life, Central Parkway, Newcastle upon Tyne, UK.|Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway.', 'Norway|Norway|Norway|Norway|Norway|United Kingdom|Norway', 6, 'Child|Genetic Association Studies|Genotyping Techniques|Haplotypes|Humans|Polymorphism, Single Nucleotide|Sample Size|Software', 'Journal Article', '10.1186/s12859-019-2727-3', '30940094', 'Log-linear and multinomial modeling offer a flexible framework for genetic association analyses of offspring (child), parent-of-origin and maternal effects, based on genotype data from a variety of child-parent configurations. Although the calculation of statistical power or sample size is an important first step in the planning of any scientific study, there is currently a lack of software for genetic power calculations in family-based study designs. Here, we address this shortcoming through new implementations of power calculations in the R package Haplin, which is a flexible and robust software for genetic epidemiological analyses. Power calculations in Haplin can be performed analytically using the asymptotic variance-covariance structure of the parameter estimator, or else by a straightforward simulation approach. Haplin performs power calculations for child, parent-of-origin and maternal effects, as well as for gene-environment interactions. The power can be calculated for both single SNPs and haplotypes, either autosomal or X-linked. Moreover, Haplin enables power calculations for different child-parent configurations, including (but not limited to) case-parent triads, case-mother dyads, and case-parent triads in combination with unrelated control-parent triads.'),
(98919, 2019, 'bmc', 'FeatureSelect: a software for feature selection based on machine learning approaches', 'Masoudi-Sobhanzadeh, Yosef|Motieghader, Habib|Masoudi-Nejad, Ali', 'Laboratory of system Biology and Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.|Laboratory of system Biology and Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.|Laboratory of system Biology and Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran. amasoudin@ut.ac.ir.', '-|-|-', 22, 'Algorithms|Machine Learning|Sensitivity and Specificity|Software', 'Evaluation Studies', '10.1186/s12859-019-2754-0', '30943889', 'Feature selection, as a preprocessing stage, is a challenging problem in various sciences such as biology, engineering, computer science, and other fields. For this purpose, some studies have introduced tools and softwares such as WEKA. Meanwhile, these tools or softwares are based on filter methods which have lower performance relative to wrapper methods. In this paper, we address this limitation and introduce a software application called FeatureSelect. In addition to filter methods, FeatureSelect consists of optimisation algorithms and three types of learners. It provides a user-friendly and straightforward method of feature selection for use in any kind of research, and can easily be applied to any type of balanced and unbalanced data based on several score functions like accuracy, sensitivity, specificity, etc. RESULTS: In addition to our previously introduced optimisation algorithm (WCC), a total of 10 efficient, well-known and recently developed algorithms have been implemented in FeatureSelect. We applied our software to a range of different datasets and evaluated the performance of its algorithms. Acquired results show that the performances of algorithms are varying on different datasets, but WCC, LCA, FOA, and LA are suitable than others in the overall state. The results also show that wrapper methods are better than filter methods.'),
(98920, 2019, 'bmc', 'Robustness of signal detection in cryo-electron microscopy via a bi-objective-function approach', 'Wang, Wei Li|Yu, Zhou|Castillo-Menendez, Luis R|Sodroski, Joseph|Mao, Youdong', 'Intelxc2xae Parallel Computing Center for Structural Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.|Graduate School of Arts and Sciences, Department of Cellular and Molecular Biology, Harvard University, Cambridge, MA, 02138, USA.|Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Department of Microbiology, Harvard Medical School, Boston, MA, 02115, USA.|Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Department of Microbiology, Harvard Medical School, Boston, MA, 02115, USA.|Intelxc2xae Parallel Computing Center for Structural Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA. youdong_mao@dfci.harvard.edu.', 'United States|United States|United States|United States|United States', 2, 'Cryoelectron Microscopy|Image Processing, Computer-Assisted|Likelihood Functions|Signal-To-Noise Ratio', 'Journal Article', '10.1186/s12859-019-2714-8', '30943890', 'The detection of weak signals and selection of single particles from low-contrast micrographs of frozen hydrated biomolecules by cryo-electron microscopy (cryo-EM) represents a major practical bottleneck in cryo-EM data analysis. Template-based particle picking by an objective function using fast local correlation (FLC) allows computational extraction of a large number of candidate particles from micrographs. Another independent objective function based on maximum likelihood estimates (MLE) can be used to align the images and verify the presence of a signal in the selected particles. Despite the widespread applications of the two objective functions, an optimal combination of their utilities has not been exploited. Here we propose a bi-objective function (BOF) approach that combines both FLC and MLE and explore the potential advantages and limitations of BOF in signal detection from cryo-EM data.'),
(98921, 2019, 'bmc', 'SNP2SIM: a modular workflow for standardizing molecular simulation and functional analysis of protein variants', 'McCoy, Matthew D|Shivakumar, Vikram|Nimmagadda, Sridhar|Jafri, Mohsin Saleet|Madhavan, Subha', 'Innovation Center for Biomedical Informatics, Georgetown University Medical Center, 2115 Wisconsin Avenue, NW, Suite 110, Washington, D.C., 20007, USA. Matthew.McCoy@georgetown.edu.|Innovation Center for Biomedical Informatics, Georgetown University Medical Center, 2115 Wisconsin Avenue, NW, Suite 110, Washington, D.C., 20007, USA.|Radiology and Radiological Science, Johns Hopkins University, 1550 Orleans St, #492, Cancer Research Building II, Baltimore, MD, 21287, USA.|School of Systems Biology, George Mason University, 4461 Rockfish Creek Lane, MS 2A1, Fairfax, VA, 22030, USA.|Innovation Center for Biomedical Informatics, Georgetown University Medical Center, 2115 Wisconsin Avenue, NW, Suite 110, Washington, D.C., 20007, USA.', 'United States|United States|United States|United States|United States', 1, 'Humans|Ligands|Molecular Docking Simulation|Molecular Dynamics Simulation|Mutant Proteins|Mutation, Missense|Protein Conformation|Software|Workflow', 'Journal Article', '10.1186/s12859-019-2774-9', '30943891', 'Molecular simulations are used to provide insight into protein structure and dynamics, and have the potential to provide important context when predicting the impact of sequence variation on protein function. In addition to understanding molecular mechanisms and interactions on the atomic scale, translational applications of those approaches include drug screening, development of novel molecular therapies, and targeted treatment planning. Supporting the continued development of these applications, we have developed the SNP2SIM workflow that generates reproducible molecular dynamics and molecular docking simulations for downstream functional variant analysis. The Python workflow utilizes molecular dynamics software (NAMD (Phillips et al., J Comput Chem 26(16):1781-802, 2005), VMD (Humphrey et al., J Mol Graph 14(1):33-8, 27-8, 1996)) to generate variant specific scaffolds for simulated small molecule docking (AutoDock Vina (Trott and Olson, J Comput Chem 31(2):455-61, 2010)).'),
(98922, 2019, 'bmc', 'Computational enhancer prediction: evaluation and improvements', 'Asma, Hasiba|Halfon, Marc S', 'Program in Genetics, Genomics, and Bioinformatics, University at Buffalo-State University of New York, 701 Ellicott St, Buffalo, NY, 14203, USA.|Program in Genetics, Genomics, and Bioinformatics, University at Buffalo-State University of New York, 701 Ellicott St, Buffalo, NY, 14203, USA. mshalfon@buffalo.edu.', 'United States|United States', 1, 'Animals|Base Sequence|Chromosome Mapping|Computational Biology|Drosophila melanogaster|Enhancer Elements, Genetic|Female|Genome, Insect|Male|Species Specificity|Transcription Factors', 'Journal Article', '10.1186/s12859-019-2781-x', '30953451', 'Identifying transcriptional enhancers and other cis-regulatory modules (CRMs) is an important goal of post-sequencing genome annotation. Computational approaches provide a useful complement to empirical methods for CRM discovery, but it is critical that we develop effective means to evaluate their performance in terms of estimating their sensitivity and specificity.'),
(98923, 2019, 'bmc', 'Ranking genomic features using an information-theoretic measure of epigenetic discordance', 'Jenkinson, Garrett|Abante, Jordi|Koldobskiy, Michael A|Feinberg, Andrew P|Goutsias, John', 'Whitaker Biomedical Engineering Institute, Johns Hopkins University, Baltimore, MD, USA.|Whitaker Biomedical Engineering Institute, Johns Hopkins University, Baltimore, MD, USA.|Center for Epigenetics, Johns Hopkins School of Medicine, Baltimore, MD, USA.|Center for Epigenetics, Johns Hopkins School of Medicine, Baltimore, MD, USA.|Whitaker Biomedical Engineering Institute, Johns Hopkins University, Baltimore, MD, USA. goutsias@jhu.edu.', 'United States|United States|United States|United States|United States', 1, 'DNA Methylation|Epigenesis, Genetic|Epigenomics|Genome, Human|Genomics|Humans|Neoplasms|Probability', 'Journal Article', '10.1186/s12859-019-2777-6', '30961526', 'Establishment and maintenance of DNA methylation throughout the genome is an important epigenetic mechanism that regulates gene expression whose disruption has been implicated in human diseases like cancer. It is therefore crucial to know which genes, or other genomic features of interest, exhibit significant discordance in DNA methylation between two phenotypes. We have previously proposed an approach for ranking genes based on methylation discordance within their promoter regions, determined by centering a window of fixed size at their transcription start sites. However, we cannot use this method to identify statistically significant genomic features and handle features of variable length and with missing data.'),
(98924, 2019, 'bmc', '3DMMS: robust 3D Membrane Morphological Segmentation of C elegans embryo', 'Cao, Jianfeng|Wong, Ming-Kin|Zhao, Zhongying|Yan, Hong', 'Department of Electronic Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong. jfcao3-c@my.cityu.edu.hk.|Department of Biology, Hong Kong Baptist University, Kowloon Tong, Hong Kong.|Department of Biology, Hong Kong Baptist University, Kowloon Tong, Hong Kong.|Department of Electronic Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong.', 'Hong Kong|Hong Kong|Hong Kong|Hong Kong', 1, 'Algorithms|Animals|Caenorhabditis elegans|Cell Division|Cell Nucleus|Embryo, Nonmammalian|Embryonic Development|Imaging, Three-Dimensional|Models, Theoretical', 'Journal Article', '10.1186/s12859-019-2720-x', '30961566', 'Understanding the cellular architecture is a fundamental problem in various biological studies. C. elegans is widely used as a model organism in these studies because of its unique fate determinations. In recent years, researchers have worked extensively on C. elegans to excavate the regulations of genes and proteins on cell mobility and communication. Although various algorithms have been proposed to analyze nucleus, cell shape features are not yet well recorded. This paper proposes a method to systematically analyze three-dimensional morphological cellular features.'),
(98925, 2019, 'bmc', 'Identification of monotonically differentially expressed genes for non-small cell lung cancer', 'Tian, Suyan', 'Division of Clinical Research, The First Hospital of Jilin University, 71 Xinmin Street, Changchun, 130021, Jilin, China. windytian@hotmail.com.', 'China', 5, 'Algorithms|Carcinoma, Non-Small-Cell Lung|Computational Biology|Gene Expression Profiling|Humans|Lung Neoplasms|Prognosis', 'Journal Article', '10.1186/s12859-019-2775-8', '30971213', 'Monotonically expressed genes (MEGs) are genes whose expression values increase or decrease monotonically as a disease advances or time proceeds. Non-small cell lung cancer (NSCLC) is a multistage progression process resulting from genetic sequences mutations, the identification of MEGs for NSCLC is important.'),
(98926, 2019, 'bmc', 'Neural sentence embedding models for semantic similarity estimation in the biomedical domain', 'Blagec, Kathrin|Xu, Hong|Agibetov, Asan|Samwald, Matthias', 'Section for Artificial Intelligence and Decision Support, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Wxc3xa4hringer Straxc3x9fe 25a, 1090, Vienna, Austria.|Section for Artificial Intelligence and Decision Support, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Wxc3xa4hringer Straxc3x9fe 25a, 1090, Vienna, Austria.|Section for Artificial Intelligence and Decision Support, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Wxc3xa4hringer Straxc3x9fe 25a, 1090, Vienna, Austria.|Section for Artificial Intelligence and Decision Support, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Wxc3xa4hringer Straxc3x9fe 25a, 1090, Vienna, Austria. matthias.samwald@meduniwien.ac.at.', 'Austria|Austria|Austria|Austria', 6, 'Algorithms|Biomedical Research|Humans|Models, Theoretical|PubMed|Semantics', 'Journal Article', '10.1186/s12859-019-2789-2', '30975071', 'Neuralxc2xa0network based embedding models are receiving significant attention in the field of natural language processing due to their capability to effectively capture semantic information representing words, sentences or even larger text elements in low-dimensional vector space. While current state-of-the-art models for assessing the semantic similarity of textual statements from biomedical publications depend on the availability of laboriously curated ontologies, unsupervised neural embedding models only require large text corpora as input and do not need manual curation. In this study, we investigated the efficacy of current state-of-the-art neural sentence embedding models for semantic similarity estimation of sentences from biomedical literature. We trained different neural embedding models on 1.7 millionxc2xa0articles from the PubMed Open Access dataset, and evaluated them based on a biomedical benchmark set containing 100 sentence pairs annotated by human experts and a smaller contradiction subset derived from the original benchmark set.'),
(98927, 2019, 'bmc', 'Leveraging the effects of chloroquine on resistant malaria parasites for combination therapies', 'Untaroiu, Ana M|Carey, Maureen A|Guler, Jennifer L|Papin, Jason A', 'Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.|Department of Microbiology, Immunology, and Cancer Biology, University of Virginia, Charlottesville, VA, USA.|Department of Biology, University of Virginia, Charlottesville, VA, USA. jlg5fw@virginia.edu.|Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA. papin@virginia.edu.', 'United States|United States|United States|United States', 1, 'Animals|Antimalarials|Chloroquine|Down-Regulation|Drug Resistance|Drug Therapy, Combination|Folic Acid|Humans|Malaria, Falciparum|Parasites|Plasmodium falciparum|Terpenes', 'Journal Article', '10.1186/s12859-019-2756-y', '30987583', 'Malaria is a major global health problem, with the Plasmodium falciparum protozoan parasite causing the most severe form of the disease. Prevalence of drug-resistant P. falciparum highlights the need to understand the biology of resistance and to identify novel combination therapies that are effective against resistant parasites. Resistance has compromised the therapeutic use of many antimalarial drugs, including chloroquine, and limited our ability to treat malaria across the world. Fortunately, chloroquine resistance comes at a fitness cost to the parasite, this can be leveraged in developing combination therapies or to reinstate use of chloroquine.'),
(98928, 2019, 'bmc', 'FGMP: assessing fungal genome completeness', 'Cissé, Ousmane H|Stajich, Jason E', 'Department of Microbiology & Plant Pathology and Institute for Integrative Genome Biology, University of California-Riverside, Riverside, CA, 92521, USA. ousmanecis@gmail.com.|Department of Microbiology & Plant Pathology and Institute for Integrative Genome Biology, University of California-Riverside, Riverside, CA, 92521, USA. jason.stajich@ucr.edu.', 'United States|United States', 5, 'Base Sequence|Biomarkers|Computer Simulation|Fungal Proteins|Fungi|Genome, Fungal|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-019-2782-9', '30987585', 'Inexpensive high-throughput DNA sequencing has democratized access to genetic information for most organisms so that research utilizing a genome or transcriptome of an organism is not limited to model systems. However, the quality of the assemblies of sampled genomes can vary greatly which hampers utility for comparisons and meaningful interpretation. The uncertainty of the completeness of a given genome sequence can limit feasibility of asserting patterns of high rates of gene loss reported in many lineages.'),
(98929, 2019, 'bmc', 'Highly efficient hypothesis testing methods for regression-type tests with correlated observations and heterogeneous variance structure', 'Zhang, Yun|Bandyopadhyay, Gautam|Topham, David J|Falsey, Ann R|Qiu, Xing', 'J Craig Venter Institute, 4120 Capricorn Lane, La Jolla 92037, CA, USA.|Department of Surgery, University of Rochester, 601 Elmwood Ave, Rochester, Rochester 14642, NY, USA.|Department of Microbiology and Immunology, University of Rochester, 601 Elmwood Ave, Rochester, Rochester 14642, NY, USA.|Department of Medicine, University of Rochester, 601 Elmwood Ave, Rochester, Rochester 14642, NY, USA.|Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Ave, Rochester, Rochester 14642, NY, USA. Xing_Qiu@urmc.rochester.edu.', 'United States|United States|United States|United States|United States', 0, 'Breast Neoplasms|Computer Simulation|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Models, Genetic|ROC Curve|Regression Analysis', 'Journal Article', '10.1186/s12859-019-2783-8', '30987598', 'For many practical hypothesis testing (H-T) applications, the data are correlated and/or with heterogeneous variance structure. The regression t-test for weighted linear mixed-effects regression (LMER) is a legitimate choice because it accounts for complex covariance structure, however, high computational costs and occasional convergence issues make it impractical for analyzing high-throughput data. In this paper, we propose computationally efficient parametric and semiparametric tests based on a set of specialized matrix techniques dubbed as the PB-transformation. The PB-transformation has two advantages: 1. The PB-transformed data will have a scalar variance-covariance matrix. 2. The original H-T problem will be reduced to an equivalent one-sample H-T problem. The transformed problem can then be approached by either the one-sample Students t-test or Wilcoxon signed rank test.'),
(98930, 2019, 'bmc', 'Ryxc5xabtxc5x8d: network-flow based transcriptome reconstruction', 'Gatter, Thomas|Stadler, Peter F', 'Bioinformatics Group, Department of Computer Science & Interdisciplinary Center for Bioinformatics, Universitxc3xa4t Leipzig, Hxc3xa4rtelstraxc3x9fe 16-18, Leipzig, 04107, Germany. thomas@bioinf.uni-leipzig.de.|Bioinformatics Group, Department of Computer Science & Interdisciplinary Center for Bioinformatics, Universitxc3xa4t Leipzig, Hxc3xa4rtelstraxc3x9fe 16-18, Leipzig, 04107, Germany.', 'Germany|Germany', 2, 'Algorithms|Computational Biology|Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Humans|RNA, Messenger|Sequence Analysis, RNA|Transcriptome', 'Journal Article', '10.1186/s12859-019-2786-5', '30991937', 'The rapid increase in High-throughput sequencing of RNA (RNA-seq) has led to tremendous improvements in the detection and reconstruction of both expressed coding and non-coding RNA transcripts. Yet, the complete and accurate annotation of the complex transcriptional output of not only the human genome has remained elusive. One of the critical bottlenecks in this endeavor is the computational reconstruction of transcript structures, due to high noise levels, technological limits, and other biases in the raw data.'),
(98931, 2019, 'bmc', 'Effect of stochasticity on coinfection dynamics of respiratory viruses', 'Pinky, Lubna|Gonzalez-Parra, Gilberto|Dobrovolny, Hana M', 'Department of Physics & Astronomy, Texas Christian University, Fort Worth, TX, USA.|Department of Mathematics, New Mexico Tech, Socorro, NM, USA.|Department of Physics & Astronomy, Texas Christian University, Fort Worth, TX, USA. h.dobrovolny@tcu.edu.', 'United States|Mexico|United States', 1, 'Coinfection|Computational Biology|Computer Simulation|Humans|Models, Biological|Models, Statistical|Respiratory Tract Infections|Stochastic Processes|Virus Diseases|Viruses', 'Journal Article', '10.1186/s12859-019-2793-6', '30991939', 'Respiratory viral infections are a leading cause of mortality worldwide. As many as 40% of patients hospitalized with influenza-like illness are reported to be infected with more than one type of virus. However, it is not clear whether these infections are more severe than single viral infections. Mathematical models can be used to help us understand the dynamics of respiratory viral coinfections and their impact on the severity of the illness. Most models of viral infections use ordinary differential equations (ODE) that reproduce the average behavior of the infection, however, they might be inaccurate in predicting certain events because of the stochastic nature of viral replication cycle. Stochastic simulations of single virus infections have shown that there is an extinction probability that depends on the size of the initial viral inoculum and parameters that describe virus-cell interactions. Thus the coinfection dynamics predicted by the ODE might be difficult to observe in reality.'),
(98932, 2019, 'bmc', 'metamicrobiomeR: an R package for analysis of microbiome relative abundance data using zero-inflated beta GAMLSS and meta-analysis across studies using random effects models', 'Ho, Nhan Thi|Li, Fan|Wang, Shuang|Kuhn, Louise', 'Gertrude H. Sergievsky Center, Columbia University, New York City, NY, USA. nhanhocumc@gmail.com.|Department of Pediatrics, University of California, Los Angeles, CA, USA.|Department of Biostatistics, Mailman School of Public Health, Columbia University, New York City, NY, USA.|Gertrude H. Sergievsky Center, Columbia University, New York City, NY, USA.', 'United States|United States|United States|United States', 5, 'Computational Biology|DNA, Bacterial|Female|Gastrointestinal Microbiome|High-Throughput Nucleotide Sequencing|Humans|Infant|Male|Models, Statistical|RNA, Ribosomal, 16S|Software', 'Journal Article', '10.1186/s12859-019-2744-2', '30991942', 'The rapid growth of high-throughput sequencing-based microbiome profiling has yielded tremendous insights into human health and physiology. Data generated from high-throughput sequencing of 16S rRNA gene amplicons are often preprocessed into composition or relative abundance. However, reproducibility has been lacking due to the myriad of different experimental and computational approaches taken in these studies. Microbiome studies may report varying results on the same topic, therefore, meta-analyses examining different microbiome studies to provide consistent and robust results are important. So far, there is still a lack of implemented methods to properly examine differential relative abundances of microbial taxonomies and to perform meta-analysis examining the heterogeneity and overall effects across microbiome studies.'),
(98933, 2019, 'bmc', 'eNetXplorer: an R package for the quantitative exploration of elastic net families for generalized linear models', 'Candia, Julixc3xa1n|Tsang, John S', 'Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. julian.candia@nih.gov.|Trans-NIH Center for Human Immunology (CHI), National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA. john.tsang@nih.gov.', 'United States|United States', 5, 'Computational Biology|Gene Expression Profiling|Linear Models|Proteomics|Software', 'Journal Article', '10.1186/s12859-019-2778-5', '30991955', 'Regularized generalized linear models (GLMs) are popular regression methods in bioinformatics, particularly useful in scenarios with fewer observations than parameters/features or when many of the features are correlated. In both ridge and lasso regularization, feature shrinkage is controlled by a penalty parameter xcexbb. The elastic net introduces a mixing parameter xcexb1 to tune the shrinkage continuously from ridge to lasso. Selecting xcexb1 objectively and determining which features contributed significantly to prediction after model fitting remain a practical challenge given the paucity of available software to evaluate performance and statistical significance.'),
(98934, 2019, 'bmc', 'Metabopolis: scalable network layout for biological pathway diagrams in urban map style', 'Wu, Hsiang-Yun|Nöllenburg, Martin|Sousa, Filipa L|Viola, Ivan', 'Research Division of Computer Graphics, Institute of Visual Computing and Human- Centered Technology, TU Wien, Vienna, Austria. hsiang.yun.wu@acm.org.|Algorithms and Complexity Group, Institute of Logic and Computation, TU Wien, Vienna, Austria.|Archaea Biology and Ecogenomics Division, Department of Ecogenomics and Systems Biology, University of Vienna, Vienna, Austria.|Research Division of Computer Graphics, Institute of Visual Computing and Human- Centered Technology, TU Wien, Vienna, Austria.', 'Austria|Austria|Austria|Austria', 2, 'Algorithms|Computational Biology|Humans|Maps as Topic|Metabolic Networks and Pathways|Models, Biological', 'Journal Article', '10.1186/s12859-019-2779-4', '30991966', 'Biological pathways represent chains of molecular interactions in biological systems that jointly form complex dynamic networks. The network structure changes from the significance of biological experiments and layout algorithms often sacrifice low-level details to maintain high-level information, which complicates the entire image to large biochemical systems such as human metabolic pathways.'),
(98935, 2019, 'bmc', 'Metabolic and signalling network maps integration: application to cross-talk studies and omics data analysis in cancer', 'Sompairac, Nicolas|Modamio, Jennifer|Barillot, Emmanuel|Fleming, Ronan M T|Zinovyev, Andrei|Kuperstein, Inna', 'Institut Curie, 26 rue dUlm, F-75005, Paris, France.|Centre for Systems Biomedicine, University of Luxembourg, L-4367, Belvaux, Luxembourg.|Institut Curie, 26 rue dUlm, F-75005, Paris, France.|Centre for Systems Biomedicine, University of Luxembourg, L-4367, Belvaux, Luxembourg.|Institut Curie, 26 rue dUlm, F-75005, Paris, France.|Institut Curie, 26 rue dUlm, F-75005, Paris, France. inna.kuperstein@curie.fr.', 'France|Luxembourg|France|Luxembourg|France|France', 1, 'Data Analysis|Female|Genomics|Humans|Metabolic Networks and Pathways|Neoplasms|Signal Transduction|Software|Systems Biology', 'Journal Article', '10.1186/s12859-019-2682-z', '30999838', 'The interplay between metabolic processes and signalling pathways remains poorly understood. Global, detailed and comprehensive reconstructions of human metabolism and signalling pathways exist in the form of molecular maps, but they have never been integrated together. We aim at filling in this gap by integrating of both signalling and metabolic pathways allowing a visual exploration of multi-level omics data and study of cross-regulatory circuits between these processes in health and in disease.'),
(98936, 2019, 'bmc', 'From trash to treasure: detecting unexpected contamination in unmapped NGS data', 'Sangiovanni, Mara|Granata, Ilaria|Thind, Amarinder Singh|Guarracino, Mario Rosario', 'Stazione Zoologica Anton Dohrn, Villa Comunale, Napoli, 80121, Italy.|High Performance Computing and Networking Institute, National Research Council of Italy, Via P. Castellino, 111, Napoli, 80131, Italy. ilaria.granata@icar.cnr.it.|High Performance Computing and Networking Institute, National Research Council of Italy, Via P. Castellino, 111, Napoli, 80131, Italy.|High Performance Computing and Networking Institute, National Research Council of Italy, Via P. Castellino, 111, Napoli, 80131, Italy.', 'Italy|Italy|Italy|Italy', 2, 'Bacteria|DNA Contamination|Fungi|High-Throughput Nucleotide Sequencing|Humans|Software|Viruses', 'Journal Article', '10.1186/s12859-019-2684-x', '30999839', 'Next Generation Sequencing (NGS) experiments produce millions of short sequences that, mapped to a reference genome, provide biological insights at genomic, transcriptomic and epigenomic level. Typically the amount of reads that correctly maps to the reference genome ranges between 70% and 90%, leaving in some cases a consistent fraction of unmapped sequences. This misalignment can be ascribed to low quality bases or sequence differences between the sample reads and the reference genome. Investigating the source of the unmapped reads is definitely important to better assess the quality of the whole experiment and to check for possible downstream or upstream contamination from exogenous nucleic acids.'),
(98937, 2019, 'bmc', 'Spatial quorum sensing modelling using coloured hybrid Petri nets and simulative model checking', 'Gilbert, David|Heiner, Monika|Ghanbar, Leila|Chodak, Jacek', 'Department of Computer Science, Brunel University London, Uxbridge, UB8 3PH, UK. david.gilbert@brunel.ac.uk.|Department of Computer Science, Brunel University London, Uxbridge, UB8 3PH, UK.|Department of Computer Science, Brunel University London, Uxbridge, UB8 3PH, UK.|Computer Science Department, Brandenburg University of Technology Cottbus-Senftenberg, Cottbus, D-03046, Germany.', 'United Kingdom|United Kingdom|United Kingdom|Germany', 4, 'Biofilms|Color|Computer Simulation|Escherichia coli|Homoserine|Lactones|Models, Biological|Quorum Sensing', 'Journal Article', '10.1186/s12859-019-2690-z', '30999841', 'Quorum sensing drives biofilm formation in bacteria in order to ensure that biofilm formation only occurs when colonies are of a sufficient size and density. This spatial behaviour is achieved by the broadcast communication of an autoinducer in a diffusion scenario. This is of interest, for example, when considering the role of gut microbiota in gut health. This behaviour occurs within the context of the four phases of bacterial growth, specifically in the exponential stage (phase 2) for autoinducer production and the stationary stage (phase 3) for biofilm formation.'),
(98938, 2019, 'bmc', 'RIP-Chip analysis supports different roles for AGO2 and GW182 proteins in recruiting and processing microRNA targets', 'Perconti, Giovanni|Rubino, Patrizia|Contino, Flavia|Bivona, Serena|Bertolazzi, Giorgio|Tumminello, Michele|Feo, Salvatore|Giallongo, Agata|Coronnello, Claudia', 'Istituto di Biomedicina ed Immunologia Molecolare (IBIM) CNR, via Ugo la Malfa 153, 90146, Palermo, Italy.|Istituto di Biomedicina ed Immunologia Molecolare (IBIM) CNR, via Ugo la Malfa 153, 90146, Palermo, Italy.|Dipartimento di Scienze e Tecnologie Biologiche Chimiche e Farmaceutiche, Università degli Studi di Palermo, 90128, Palermo, Italy.|Dipartimento di Scienze e Tecnologie Biologiche Chimiche e Farmaceutiche, Università degli Studi di Palermo, 90128, Palermo, Italy.|Dipartimento di Scienze Economiche, Aziendali e Statistiche, Università degli Studi di Palermo, 90128, Palermo, Italy.|Dipartimento di Scienze Economiche, Aziendali e Statistiche, Università degli Studi di Palermo, 90128, Palermo, Italy.|Dipartimento di Scienze e Tecnologie Biologiche Chimiche e Farmaceutiche, Università degli Studi di Palermo, 90128, Palermo, Italy.|Istituto di Biomedicina ed Immunologia Molecolare (IBIM) CNR, via Ugo la Malfa 153, 90146, Palermo, Italy. agata.giallongo@ibim.cnr.it.|Istituto di Biomedicina ed Immunologia Molecolare (IBIM) CNR, via Ugo la Malfa 153, 90146, Palermo, Italy. ccoronnello@fondazionerimed.com.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 3, 'Argonaute Proteins|Autoantigens|Binding Sites|Chromatin Immunoprecipitation|Gene Expression Profiling|Gene Expression Regulation|Humans|MCF-7 Cells|MicroRNAs|Open Reading Frames|RNA, Messenger|RNA-Binding Proteins|Support Vector Machine', 'Journal Article', '10.1186/s12859-019-2683-y', '30999843', 'MicroRNAs (miRNAs) are small non-coding RNA molecules mediating the translational repression and degradation of target mRNAs in the cell. Mature miRNAs are used as a template by the RNA-induced silencing complex (RISC) to recognize the complementary mRNAs to be regulated. To discern further RISC functions, we analyzed the activities of two RISC proteins, AGO2 and GW182, in the MCF-7 human breast cancer cell line.'),
(98939, 2019, 'bmc', 'GenHap: a novel computational method based on genetic algorithms for haplotype assembly', 'Tangherloni, Andrea|Spolaor, Simone|Rundo, Leonardo|Nobile, Marco S|Cazzaniga, Paolo|Mauri, Giancarlo|Liò, Pietro|Merelli, Ivan|Besozzi, Daniela', 'Department of Informatics, Systems and Communication (DISCo), University of Milano-Bicocca, Viale Sarca 336, U14 Building, Milan, 20126, Italy. andrea.tangherloni@disco.unimib.it.|Department of Informatics, Systems and Communication (DISCo), University of Milano-Bicocca, Viale Sarca 336, U14 Building, Milan, 20126, Italy.|Department of Informatics, Systems and Communication (DISCo), University of Milano-Bicocca, Viale Sarca 336, U14 Building, Milan, 20126, Italy.|Department of Informatics, Systems and Communication (DISCo), University of Milano-Bicocca, Viale Sarca 336, U14 Building, Milan, 20126, Italy.|Department of Human and Social Sciences, University of Bergamo, Piazzale SantAgostino 2, Bergamo, 24129, Italy.|Department of Informatics, Systems and Communication (DISCo), University of Milano-Bicocca, Viale Sarca 336, U14 Building, Milan, 20126, Italy.|Computer Laboratory, University of Cambridge, 15 JJ Thomson Avenue, Cambridge, CB3 0FD, UK.|Institute of Biomedical Technologies, Italian National Research Council, Via Fratelli Cervi 93, Segrate (MI), 20090, Italy.|Department of Informatics, Systems and Communication (DISCo), University of Milano-Bicocca, Viale Sarca 336, U14 Building, Milan, 20126, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|United Kingdom|Italy|Italy', 0, 'Algorithms|Computational Biology|Databases, Genetic|Haplotypes|Humans|Time Factors', 'Journal Article', '10.1186/s12859-019-2691-y', '30999845', 'In order to fully characterize the genome of an individual, the reconstruction of the two distinct copies of each chromosome, called haplotypes, is essential. The computational problem of inferring the full haplotype of a cell starting from read sequencing data is known as haplotype assembly, and consists in assigning all heterozygous Single Nucleotide Polymorphisms (SNPs) to exactly one of the two chromosomes. Indeed, the knowledge of complete haplotypes is generally more informative than analyzing single SNPs and plays a fundamental role in many medical applications.'),
(98940, 2019, 'bmc', 'VIGLA-M: visual gene expression data analytics', 'Navas-Delgado, Ismael|García-Nieto, José|López-Camacho, Esteban|Rybinski, Maciej|Lavado, Rocio|Berciano Guerrero, Miguel Ángel|Aldana-Montes, José F', 'Khaos Research, Universidad de Málaga, Málaga, Spain, Arquitecto Francisco Peñalosa 18, Málaga, 29071, Spain. ismael@lcc.uma.es.|Khaos Research, Universidad de Málaga, Málaga, Spain, Arquitecto Francisco Peñalosa 18, Málaga, 29071, Spain.|Khaos Research, Universidad de Málaga, Málaga, Spain, Arquitecto Francisco Peñalosa 18, Málaga, 29071, Spain.|Khaos Research, Universidad de Málaga, Málaga, Spain, Arquitecto Francisco Peñalosa 18, Málaga, 29071, Spain.|Unidad de Oncología Intercentros, Hospitales Univesitarios Regional y Virgen de la Victoria de Málaga, Instituto de Investigaciones Biomédicas (IBIMA), Málaga, Spain, Málaga, Spain.|Unidad de Oncología Intercentros, Hospitales Univesitarios Regional y Virgen de la Victoria de Málaga, Instituto de Investigaciones Biomédicas (IBIMA), Málaga, Spain, Málaga, Spain.|Khaos Research, Universidad de Málaga, Málaga, Spain, Arquitecto Francisco Peñalosa 18, Málaga, 29071, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain', 1, 'Algorithms|Cluster Analysis|Data Science|Databases, Factual|Gene Expression Profiling|Gene Expression Regulation|Gene Regulatory Networks|Humans|Melanoma', 'Journal Article', '10.1186/s12859-019-2695-7', '30999846', 'The analysis of gene expression levels is used in many clinical studies to know how patients evolve or to find new genetic biomarkers that could help in clinical decision making. However, the techniques and software available for these analyses are not intended for physicians, but for geneticists. However, enabling physicians to make initial discoveries on these data would benefit in the clinical assay development.'),
(98941, 2019, 'bmc', 'FEDRO: a software tool for the automatic discovery of candidate ORFs in plants with c xe2x86x92u RNA editing', 'Fassetti, Fabio|Giallombardo, Claudia|Leone, Ofelia|Palopoli, Luigi|Rombo, Simona E|Saiardi, Adolfo', 'DIMES, Universitxc3xa0 della Calabria, Via Pietro Bucci 41 C, Cosenza, Italy.|Department of Mathematics and Computer Science, Universitxc3xa0 degli Studi di Palermo, Via Archirafi 34, Palermo, Italy.|DIMES, Universitxc3xa0 della Calabria, Via Pietro Bucci 41 C, Cosenza, Italy.|DIMES, Universitxc3xa0 della Calabria, Via Pietro Bucci 41 C, Cosenza, Italy.|Department of Mathematics and Computer Science, Universitxc3xa0 degli Studi di Palermo, Via Archirafi 34, Palermo, Italy. simona.rombo@unipa.it.|LMCB, MRC, Cell Biology Unit and Department of Developmental Biology, University College, London, UK.', 'Italy|Italy|Italy|Italy|Italy|United Kingdom', 1, 'Base Sequence|DNA, Mitochondrial|Genome, Mitochondrial|Open Reading Frames|Oryza|RNA Editing|Software', 'Journal Article', '10.1186/s12859-019-2696-6', '30999847', 'RNA editing is an important mechanism for gene expression in plants organelles. It alters the direct transfer of genetic information from DNA to proteins, due to the introduction of differences between RNAs and the corresponding coding DNA sequences. Software tools successful for the search of genes in other organisms not always are able to correctly perform this task in plants organellar genomes. Moreover, the available software tools predicting RNA editing events utilise algorithms that do not account for events which may generate a novel start codon.'),
(98942, 2019, 'bmc', 'Integration of transcriptomic data in a genome-scale metabolic model to investigate the link between obesity and breast cancer', 'Granata, Ilaria|Troiano, Enrico|Sangiovanni, Mara|Guarracino, Mario Rosario', 'High Performance Computing and Networking Institute, National Research Council of Italy, Via P. Castellino, 111, Napoli, 80131, Italy. ilaria.granata@icar.cnr.it.|High Performance Computing and Networking Institute, National Research Council of Italy, Via P. Castellino, 111, Napoli, 80131, Italy.|Stazione Zoologica Anton Dohrn, Villa Comunale, Napoli, 80121, Italy.|High Performance Computing and Networking Institute, National Research Council of Italy, Via P. Castellino, 111, Napoli, 80131, Italy.', 'Italy|Italy|Italy|Italy', 0, 'Acyl Carrier Protein|Breast Neoplasms|Fatty Acids|Female|Gene Expression Profiling|Gene Regulatory Networks|Genome, Human|Humans|Lipid Droplets|Metabolic Networks and Pathways|Models, Genetic|Obesity|Reproducibility of Results|Thinness|Transcriptome', 'Journal Article', '10.1186/s12859-019-2685-9', '30999849', 'Obesity is a complex disorder associated with an increased risk of developing several comorbid chronic diseases, including postmenopausal breast cancer. Although many studies have investigated this issue, the link between body weight and either risk or poor outcome of breast cancer is still to characterize. Systems biology approaches, based on the integration of multiscale models and data from a wide variety of sources, are particularly suitable for investigating the underlying molecular mechanisms of complex diseases. In this scenario, GEnome-scale metabolic Models (GEMs) are a valuable tool, since they represent the metabolic structure of cells and provide a functional scaffold for simulating and quantifying metabolic fluxes in living organisms through constraint-based mathematical methods. The integration of omics data into the structural information described by GEMs allows to build more accurate descriptions of metabolic states.'),
(98943, 2019, 'bmc', 'Greedy de novo motif discovery to construct motif repositories for bacterial proteomes', 'Khakzad, Hamed|Malmström, Johan|Malmström, Lars', 'Faculty of Science, Institute for Computational Science, University of Zurich, 429 Winterthurerstrasse, 190, Zurich, CH-8057, Switzerland.|Division of Infection Medicine, Department of Clinical 432 Sciences, Lund University, Tornavagen, 10, Lund, SE-22184, Sweden.|Faculty of Science, Institute for Computational Science, University of Zurich, 429 Winterthurerstrasse, 190, Zurich, CH-8057, Switzerland. lars.malmstroem@uzh.ch.', 'Switzerland|Sweden|Switzerland', 0, 'Algorithms|Amino Acid Motifs|Bacterial Proteins|Computational Biology|Conserved Sequence|Genome, Bacterial|Proteome|Streptococcus pyogenes', 'Journal Article', '10.1186/s12859-019-2686-8', '30999854', 'Bacterial surfaces are complex systems, constructed from membranes, peptidoglycan and, importantly, proteins. The proteins play crucial roles as critical regulators of how the bacterium interacts with and survive in its environment. A full catalog of the motifs in protein families and their relative conservation grade is a prerequisite to target the protein-protein interaction that bacterial surface protein makes to host proteins.'),
(98944, 2019, 'bmc', 'The 2017 Network Tools and Applications in Biology (NETTAB) workshop: aims, topics and outcomes', 'Romano, Paolo|Céol, Arnaud|Dräger, Andreas|Fiannaca, Antonino|Giugno, Rosalba|La Rosa, Massimo|Milanesi, Luciano|Pfeffer, Ulrich|Rizzo, Riccardo|Shin, Soo-Yong|Xia, Junfeng|Urso, Alfonso', 'IRCCS Ospedale Policlinico San Martino, Largo Rosanna Benzi 10, Genova, I-16132, Italy. paolo.romano@hsanmartino.it.|European Institute of Oncology IRCCS, Milan, 20141, Italy.|Computational Systems Biology of Infection and Antimicrobial-Resistant Pathogens, Center for Bioinformatics Tübingen (ZBIT), Tübingen, 72074, Germany.|ICAR-CNR, Institute for high performance computing and networking, National Research Council of Italy, Palermo, 90146, Italy.|Department of Computer Science, University of Verona, Verona, 37134, Italy.|ICAR-CNR, Institute for high performance computing and networking, National Research Council of Italy, Palermo, 90146, Italy.|ITB-CNR, Institute of biomedical technologies, National Research Council of Italy, Segrate (MI), 20090, Italy.|IRCCS Ospedale Policlinico San Martino, Largo Rosanna Benzi 10, Genova, I-16132, Italy.|ICAR-CNR, Institute for high performance computing and networking, National Research Council of Italy, Palermo, 90146, Italy.|Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, 03063, South Korea.|Institutes of Physical Science and Information Technology, Anhui University, Hefei, 230601, China.|ICAR-CNR, Institute for high performance computing and networking, National Research Council of Italy, Palermo, 90146, Italy.', 'Italy|Italy|Germany|Italy|Italy|Italy|Italy|Italy|Italy|-|China|Italy', 0, 'Computational Biology|Delivery of Health Care|Genomics|Humans|Italy|Neoplasms|Precision Medicine', 'Congress', '10.1186/s12859-019-2681-0', '30999855', 'The 17th International NETTAB workshop was held in Palermo, Italy, on October 16-18, 2017. The special topic for the meeting was Methods, tools and platforms for Personalised Medicine in the Big Data Era, but the traditional topics of the meeting series were also included in the event. About 40 scientific contributions were presented, including four keynote lectures, five guest lectures, and many oral communications and posters. Also, three tutorials were organised before and after the workshop. Full papers from some of the best works presented in Palermo were submitted for this Supplement of BMC Bioinformatics. Here, we provide an overview of meeting aims and scope. We also shortly introduce selected papers that have been accepted for publication in this Supplement, for a complete presentation of the outcomes of the meeting.'),
(98945, 2019, 'bmc', 'Walking pathways with positive feedback loops reveal DNA methylation biomarkers of colorectal cancer', 'Kel, Alexander|Boyarskikh, Ulyana|Stegmaier, Philip|Leskov, Leonid S|Sokolov, Andrey V|Yevshin, Ivan|Mandrik, Nikita|Stelmashenko, Daria|Koschmann, Jeannette|Kel-Margoulis, Olga|Krull, Mathias|Martxc3xadnez-Cardxc3xbas, Anna|Moran, Sebastian|Esteller, Manel|Kolpakov, Fedor|Filipenko, Maxim|Wingender, Edgar', 'Institute of Chemical Biology and Fundamental Medicine, SBRAN, Novosibirsk, 630090, Russia. alexander.kel@genexplain.com.|Institute of Chemical Biology and Fundamental Medicine, SBRAN, Novosibirsk, 630090, Russia.|geneXplain GmbH, 38302, Wolfenbxc3xbcttel, Germany.|City Clinical Hospital xe2x84x961, Novosibirsk, 630090, Russia.|City Clinical Hospital xe2x84x961, Novosibirsk, 630090, Russia.|Biosoft.ru, Ltd, Novosibirsk, 630090, Russia.|Biosoft.ru, Ltd, Novosibirsk, 630090, Russia.|Biosoft.ru, Ltd, Novosibirsk, 630090, Russia.|geneXplain GmbH, 38302, Wolfenbxc3xbcttel, Germany.|geneXplain GmbH, 38302, Wolfenbxc3xbcttel, Germany.|geneXplain GmbH, 38302, Wolfenbxc3xbcttel, Germany.|Cancer Epigenetics and Biology Program (PEBC), Bellvitge Biomedical Research Institute (IDIBELL), 08908, Barcelona, Spain.|Cancer Epigenetics and Biology Program (PEBC), Bellvitge Biomedical Research Institute (IDIBELL), 08908, Barcelona, Spain.|Cancer Epigenetics and Biology Program (PEBC), Bellvitge Biomedical Research Institute (IDIBELL), 08908, Barcelona, Spain.|Biosoft.ru, Ltd, Novosibirsk, 630090, Russia.|Institute of Chemical Biology and Fundamental Medicine, SBRAN, Novosibirsk, 630090, Russia.|geneXplain GmbH, 38302, Wolfenbxc3xbcttel, Germany.', '-|-|Germany|-|-|-|-|-|Germany|Germany|Germany|Spain|Spain|Spain|-|-|Germany', 4, 'Binding Sites|Biomarkers, Tumor|Colorectal Neoplasms|CpG Islands|DNA Methylation|Epigenesis, Genetic|Feedback, Physiological|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Male|Middle Aged|Neoplasm Staging|Signal Transduction|Transcription Factors', 'Journal Article', '10.1186/s12859-019-2687-7', '30999858', 'The search for molecular biomarkers of early-onset colorectal cancer (CRC) is an important but still quite challenging and unsolved task. Detection of CpG methylation in human DNA obtained from blood or stool has been proposed as a promising approach to a noninvasive early diagnosis of CRC. Thousands of abnormally methylated CpG positions in CRC genomes are often located in non-coding parts of genes. Novel bioinformatic methods are thus urgently needed for multi-omics data analysis to reveal causative biomarkers with a potential driver role in early stages of cancer.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(98946, 2019, 'bmc', 'Analyzing big datasets of genomic sequences: fast and scalable collection of k-mer statistics', 'Ferraro Petrillo, Umberto|Sorella, Mara|Cattaneo, Giuseppe|Giancarlo, Raffaele|Rombo, Simona E', 'Dipartimento di Scienze Statistiche, Università di Roma - La Sapienza, Rome, 00185, Italy.|Dipartimento di Ingegneria Informatica, Automatica e Gestionale, Università di Roma - La Sapienza, Rome, 00185, Italy.|Dipartimento di Informatica, Università di Salerno, Fisciano (SA), 84084, Italy.|Dipartimento di Matematica ed Informatica, Università di Palermo, Palermo, 90133, Italy. raffaele.giancarlo@unipa.it.|Dipartimento di Matematica ed Informatica, Università di Palermo, Palermo, 90133, Italy.', 'Italy|Italy|Italy|Italy|Italy', 3, 'Algorithms|Base Sequence|Data Analysis|Databases, Nucleic Acid|Genome|Software|Statistics as Topic|Time Factors', 'Journal Article', '10.1186/s12859-019-2694-8', '30999863', 'Distributed approaches based on the MapReduce programming paradigm have started to be proposed in the Bioinformatics domain, due to the large amount of data produced by the next-generation sequencing techniques. However, the use of MapReduce and related Big Data technologies and frameworks (e.g., Apache Hadoop and Spark) does not necessarily produce satisfactory results, in terms of both efficiency and effectiveness. We discuss how the development of distributed and Big Data management technologies has affected the analysis of large datasets of biological sequences. Moreover, we show how the choice of different parameter configurations and the careful engineering of the software with respect to the specific framework under consideration may be crucial in order to achieve good performance, especially on very large amounts of data. We choose k-mers counting as a case study for our analysis, and Spark as the framework to implement FastKmer, a novel approach for the extraction of k-mer statistics from large collection of biological sequences, with arbitrary values of k.'),
(98947, 2019, 'bmc', 'An algebraic language for RNA pseudoknots comparison', 'Quadrini, Michela|Tesei, Luca|Merelli, Emanuela', 'School of Science and Technology, University of Camerino, Via Madonna della Carceri 9, Camerino, 62032, Italy.|School of Science and Technology, University of Camerino, Via Madonna della Carceri 9, Camerino, 62032, Italy.|School of Science and Technology, University of Camerino, Via Madonna della Carceri 9, Camerino, 62032, Italy. emanuela.merelli@unicam.it.', 'Italy|Italy|Italy', 4, 'Algorithms|Base Sequence|Nucleic Acid Conformation|RNA|Sequence Alignment', 'Journal Article', '10.1186/s12859-019-2689-5', '30999864', 'RNA secondary structure comparison is a fundamental task for several studies, among which are RNA structure prediction and evolution. The comparison can currently be done efficiently only for pseudoknot-free structures due to their inherent tree representation.'),
(98948, 2019, 'bmc', 'A Dynamic Bayesian Network model for the simulation of Amyotrophic Lateral Sclerosis progression', 'Zandonà, Alessandro|Vasta, Rosario|Chiò, Adriano|Di Camillo, Barbara', 'Department of Information Engineering, University of Padova, Gradenigo 6/b, 35131, Padova, Italy.|Department of Neuroscience, University of Torino, 10124, Torino, Italy.|Department of Neuroscience, University of Torino, 10124, Torino, Italy.|Department of Information Engineering, University of Padova, Gradenigo 6/b, 35131, Padova, Italy. barbara.dicamillo@unipd.it.', 'Italy|Italy|Italy|Italy', 0, 'Amyotrophic Lateral Sclerosis|Area Under Curve|Bayes Theorem|Computer Simulation|Databases as Topic|Disease Progression|Humans|Probability|ROC Curve', 'Journal Article', '10.1186/s12859-019-2692-x', '30999865', 'Amyotrophic lateral sclerosis (ALS) is an adult-onset neurodegenerative disease progressively affecting upper and lower motor neurons in the brain and spinal cord. Mean life expectancy is three to five years, with paralysis of muscles, respiratory failure and loss of vital functions being the common causes of death. Clinical manifestations of ALS are heterogeneous due to the mix of anatomic regions involvement and the variability in disease course, consequently, diagnosis and prognosis at the level of individual patient is really challenging. Prediction of ALS progression and stratification of patients into meaningful subgroups have been long-standing interests to clinical practice, research and drug development.'),
(98949, 2019, 'bmc', 'decompTumor2Sig: identification of mutational signatures active in individual tumors', 'Krüger, Sandra|Piro, Rosario M', 'Institute of Computer Science and Institute of Bioinformatics, Freie Universität Berlin, Berlin, Germany.|Institute of Computer Science and Institute of Bioinformatics, Freie Universität Berlin, Berlin, Germany. r.piro@fu-berlin.de.', 'Germany|Germany', 6, 'Base Sequence|Computer Simulation|Genome, Human|Humans|Models, Genetic|Models, Statistical|Mutation|Mutation Rate|Neoplasms|Software', 'Journal Article', '10.1186/s12859-019-2688-6', '30999866', 'The somatic mutations found in a tumor have in most cases been caused by multiple mutational processes such as those related to extrinsic carcinogens like cigarette smoke, and those related to intrinsic processes like age-related spontaneous deamination of 5-methylcytosine. The effect of such mutational processes can be modeled by mutational signatures, of which two different conceptualizations exist: the model introduced by Alexandrov et al., Nature 500:415-421, 2013, and the model introduced by Shiraishi et al., PLoS Genetics 11(12):e1005657, 2015. The initial identification and definition of mutational signatures requires large sets of tumor samples.'),
(98950, 2019, 'bmc', 'PGxO and PGxLOD: a reconciliation of pharmacogenomic knowledge of various provenances, enabling further comparison', 'Monnin, Pierre|Legrand, Joël|Husson, Graziella|Ringot, Patrice|Tchechmedjiev, Andon|Jonquet, Clément|Napoli, Amedeo|Coulet, Adrien', 'Université de Lorraine, CNRS, Inria, LORIA, Nancy, 54000, France. pierre.monnin@loria.fr.|Université de Lorraine, CNRS, Inria, LORIA, Nancy, 54000, France.|Université de Lorraine, CNRS, Inria, LORIA, Nancy, 54000, France.|Université de Lorraine, CNRS, Inria, LORIA, Nancy, 54000, France.|LIRMM, Université de Montpellier, CNRS, Montpellier, 34095, France.|LIRMM, Université de Montpellier, CNRS, Montpellier, 34095, France.|Université de Lorraine, CNRS, Inria, LORIA, Nancy, 54000, France.|Université de Lorraine, CNRS, Inria, LORIA, Nancy, 54000, France.', 'France|France|France|France|France|France|France|France', 5, 'Data Mining|Databases, Factual|Electronic Health Records|Humans|Knowledge Bases|Pharmacogenetics|Tissue Banks', 'Journal Article', '10.1186/s12859-019-2693-9', '30999867', 'Pharmacogenomics (PGx) studies how genomic variations impact variations in drug response phenotypes. Knowledge in pharmacogenomics is typically composed of units that have the form of ternary relationships gene variant - drug - adverse event. Such a relationship states that an adverse event may occur for patients having the specified gene variant and being exposed to the specified drug. State-of-the-art knowledge in PGx is mainly available in reference databases such as PharmGKB and reported in scientific biomedical literature. But, PGx knowledge can also be discovered from clinical data, such as Electronic Health Records (EHRs), and in this case, may either correspond to new knowledge or confirm state-of-the-art knowledge that lacks clinical counterpart or validation. For this reason, there is a need for automatic comparison of knowledge units from distinct sources.'),
(98951, 2019, 'bmc', 'Tiling Nussinovs RNA folding loop nest with a space-time approach', 'Palkowski, Marek|Bielecki, Wlodzimierz', 'West Pomeranian University of Technology, Faculty of Computer Science, Zolnierska 49, Szczecin, 71-210, Poland. mpalkowski@wi.zut.edu.pl.|West Pomeranian University of Technology, Faculty of Computer Science, Zolnierska 49, Szczecin, 71-210, Poland.', 'Poland|Poland', 0, 'Algorithms|Computational Biology|Nucleic Acid Conformation|RNA|RNA Folding', 'Journal Article', '10.1186/s12859-019-2785-6', '31014228', 'An RNA primary structure, or sequence, is a single strand considered as a chain of nucleotides from the alphabet AUGC (adenine, uracil, guanine, cytosine). The strand can be folded onto itself, i.e., one segment of an RNA sequence might be paired with another segment of the same RNA sequence into a two-dimensional structure composed by a list of complementary base pairs, which are close together with the minimum energy. That list is called RNAs secondary structure and is predicted by an RNA folding algorithm. RNA secondary structure prediction is a computing-intensive task that lies at the core of search applications in bioinformatics.'),
(98952, 2019, 'bmc', 'Detailed prediction of protein sub-nuclear localization', 'Littmann, Maria|Goldberg, Tatyana|Seitz, Sebastian|Bodxc3xa9n, Mikael|Rost, Burkhard', 'Department of Informatics, Bioinformatics & Computational Biology - i12, TUM (Technical University of Munich), Boltzmannstr. 3, 85748, Garching/Munich, Germany. littmann@rostlab.org.|Department of Informatics, Bioinformatics & Computational Biology - i12, TUM (Technical University of Munich), Boltzmannstr. 3, 85748, Garching/Munich, Germany.|Department of Informatics, Bioinformatics & Computational Biology - i12, TUM (Technical University of Munich), Boltzmannstr. 3, 85748, Garching/Munich, Germany.|School of Chemistry and Molecular Biosciences, UQ (University of Queensland), Cooper Rd, Brisbane City, QLD, 4072, Australia.|Department of Informatics, Bioinformatics & Computational Biology - i12, TUM (Technical University of Munich), Boltzmannstr. 3, 85748, Garching/Munich, Germany.', 'Germany|Germany|Germany|Australia|Germany', 1, 'Cell Nucleus|Computational Biology|Nuclear Proteins|Proteins|Sequence Analysis, Protein|Support Vector Machine', 'Journal Article', '10.1186/s12859-019-2790-9', '31014229', 'Sub-nuclear structures or locations are associated with various nuclear processes. Proteins localized in these substructures are important to understand the interior nuclear mechanisms. Despite advances in high-throughput methods, experimental protein annotations remain limited. Predictions of cellular compartments have become very accurate, largely at the expense of leaving out substructures inside the nucleus making a fine-grained analysis impossible.'),
(98953, 2019, 'bmc', 'A network-based approach for identifying suitable biomarkers for oral immunotherapy of food allergy', 'van Bilsen, Jolanda H M|Verschuren, Lars|Wagenaar, Laura|Vonk, Marlotte M|van Esch, Betty C A M|Knippels, Léon M J|Garssen, Johan|Smit, Joost J|Pieters, Raymond H H|van den Broek, Tim J', 'TNO, PO Box 360, 3700, AJ, Zeist, The Netherlands. j.vanbilsen@tno.nl.|TNO, PO Box 360, 3700, AJ, Zeist, The Netherlands.|Institute of Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands.|Utrecht Institute of Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands.|Utrecht Institute of Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands.|Utrecht Institute of Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands.|Utrecht Institute of Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands.|Institute of Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands.|Institute of Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands.|TNO, PO Box 360, 3700, AJ, Zeist, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Neth', 0, 'Animals|Biomarkers|Computational Biology|Desensitization, Immunologic|Food Hypersensitivity|Humans|Mice', 'Journal Article', '10.1186/s12859-019-2802-9', '31014233', 'Oral immunotherapy (OIT) is a promising therapeutic approach to treat food allergic patients. However, concerns with regards to safety and long-term efficacy of OIT remain. There is a need to identify biomarkers that predict, monitor and/or evaluate the effects of OIT. Here we present a method to select candidate biomarkers for efficacy and safety assessment of OIT using the computational approaches Bayesian networks (BN) and Topological Data Analysis (TDA).'),
(98954, 2019, 'bmc', 'Estimates of introgression as a function of pairwise distances', 'Pfeifer, Bastian|Kapan, Durrell D', 'Institute for Medical Informatics, Statistics and Documentation, Medical University, Graz, Austria.|Department of Entomology and Center for Comparative Genomics, Institute for Biodiversity Science and Sustainability, California Academy of Sciences, 55 Music Concourse Dr., San Francisco, USA. dkapan@calacademy.org.', 'Austria|United States', 1, 'Databases, Genetic|Gene Flow|Genomics|Hybridization, Genetic|Models, Genetic|Models, Statistical|Polymorphism, Single Nucleotide|Whole Genome Sequencing', 'Journal Article', '10.1186/s12859-019-2747-z', '31014244', 'Research over the last 10 years highlights the increasing importance of hybridization between species as a major force structuring the evolution of genomes and potentially providing raw material for adaptation by natural and/or sexual selection. Fueled by research in a few model systems where phenotypic hybrids are easily identified, research into hybridization and introgression (the flow of genes between species) has exploded with the advent of whole-genome sequencing and emerging methods to detect the signature of hybridization at the whole-genome or chromosome level. Amongst these are a general class of methods that utilize patterns of single-nucleotide polymorphisms (SNPs) across a tree as markers of hybridization. These methods have been applied to a variety of genomic systems ranging from butterflies to Neanderthals to detect introgression, however, when employed at a fine genomic scale these methods do not perform well to quantify introgression in small sample windows.'),
(98955, 2019, 'bmc', 'Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data', 'Ramazzotti, Daniele|Graudenzi, Alex|De Sano, Luca|Antoniotti, Marco|Caravagna, Giulio', 'Department of Pathology, Stanford University, Palo Alto, 94305, CA, USA.|Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336, Milan, 20126, Italy. alex.graudenzi@unimib.it.|Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336, Milan, 20126, Italy.|Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336, Milan, 20126, Italy.|Centre for Evolution and Cancer, The Institute of Cancer Research, 15 Cotswold Road, London, SM2 5NG, UK.', 'United Kingdom|Italy|Italy|Italy|United Kingdom', 0, 'Algorithms|Computational Biology|Evolution, Molecular|Humans|Mutation|Neoplasms|Sequence Analysis, DNA|Single-Cell Analysis', 'Journal Article', '10.1186/s12859-019-2795-4', '31023236', 'A large number of algorithms is being developed to reconstruct evolutionary models of individual tumours from genome sequencing data. Most methods can analyze multiple samples collected either through bulk multi-region sequencing experiments or the sequencing of individual cancer cells. However, rarely the same method can support both data types.'),
(98956, 2019, 'bmc', 'Balancing multiple objectives in conformation sampling to control decoy diversity in template-free protein structure prediction', 'Zaman, Ahmed Bin|Shehu, Amarda', 'Department of Computer Science, George Mason University, Fairfax, 22030, VA, USA.|Department of Computer Science, George Mason University, Fairfax, 22030, VA, USA.', 'United States|United States', 8, 'Algorithms|Computational Biology|Protein Structure, Tertiary|Proteins|Thermodynamics', 'Journal Article', '10.1186/s12859-019-2794-5', '31023237', 'Computational approaches for the determination of biologically-active/native three-dimensional structures of proteins with novel sequences have to handle several challenges. The (conformation) space of possible three-dimensional spatial arrangements of the chain of amino acids that constitute a protein molecule is vast and high-dimensional. Exploration of the conformation spaces is performed in a sampling-based manner and is biased by the internal energy that sums atomic interactions. Even state-of-the-art energy functions that quantify such interactions are inherently inaccurate and associate with protein conformation spaces overly rugged energy surfaces riddled with artifact local minima. The response to these challenges in template-free protein structure prediction is to generate large numbers of low-energy conformations (also referred to as decoys) as a way of increasing the likelihood of having a diverse decoy dataset that covers a sufficient number of local minima possibly housing near-native conformations.'),
(98957, 2019, 'bmc', 'Fixed-parameter tractable sampling for RNA design with multiple target structures', 'Hammer, Stefan|Wang, Wei|Will, Sebastian|Ponty, Yann', 'Dept. Computer Science, and Interdisciplinary Center for Bioinformatics, Univ. Leipzig, Härtelstr. 16-18, Leipzig, D-04107, Germany.|CNRS UMR 7161 LIX, Ecole Polytechnique, Bat. Alan Turing, Palaiseau, 91120, France.|Dept. Theoretical Chemistry, Univ. Vienna, Währingerstr. 17, Wien, A-1090, Austria. will@tbi.univie.ac.at.|CNRS UMR 7161 LIX, Ecole Polytechnique, Bat. Alan Turing, Palaiseau, 91120, France. yann.ponty@lix.polytechnique.fr.', 'Germany|France|Austria|France', 0, 'Algorithms|Base Composition|Models, Theoretical|Nucleic Acid Conformation|RNA|User-Computer Interface', 'Journal Article', '10.1186/s12859-019-2784-7', '31023239', 'The design of multi-stable RNA molecules has important applications in biology, medicine, and biotechnology. Synthetic design approaches profit strongly from effective in-silico methods, which substantially reduce the need for costly wet-lab experiments.'),
(98958, 2019, 'bmc', 'CSESA: an R package to predict Salmonella enterica serotype based on newly incorporated spacer pairs of CRISPR', 'Yang, Lang|Zhang, Xia|Liu, Yuqi|Li, Hao|Qiu, Shaofu|Li, Peng|Song, Hongbin', 'Academy of Military Medical Sciences, Beijing, China.|School of electronics engineering and computer science, Peking University, Beijing, China.|Academy of Military Medical Sciences, Beijing, China.|Academy of Military Medical Sciences, Beijing, China.|Center for Disease Control and Prevention of PLA, Beijing, China.|Center for Disease Control and Prevention of PLA, Beijing, China. jiekenlee@126.com.|Center for Disease Control and Prevention of PLA, Beijing, China. hongbinsong@263.net.', 'China|China|China|China|China|China|China', 0, 'Clustered Regularly Interspaced Short Palindromic Repeats|Polymerase Chain Reaction|Salmonella enterica|Serogroup|Serotyping|User-Computer Interface', 'Journal Article', '10.1186/s12859-019-2806-5', '31029079', 'Salmonella enterica is a major cause of bacterial food-borne disease worldwide. Immunological serotyping is the most commonly used typing method to characterize S. enterica isolates, but is time-consuming and requires expensive reagents. Here, we developed an R package CSESA (CRISPR-based Salmonella enterica Serotype Analyzer) to predict the serotype based on the CRISPR loci of S. enterica.'),
(98959, 2019, 'bmc', 'DNAscan: personal computer compatible NGS analysis, annotation and visualisation', 'Iacoangeli, A|Al Khleifat, A|Sproviero, W|Shatunov, A|Jones, A R|Morgan, S L|Pittman, A|Dobson, R J|Newhouse, S J|Al-Chalabi, A', 'Department of Biostatistics and Health Informatics, Kings College London, London, UK. alfredo.iacoangeli@kcl.ac.uk.|Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Kings College London, London, UK.|Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Kings College London, London, UK.|Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Kings College London, London, UK.|Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Kings College London, London, UK.|Department of Molecular Neuroscience, UCL, Institute of Neurology, London, UK.|Department of Molecular Neuroscience, UCL, Institute of Neurology, London, UK.|Department of Biostatistics and Health Informatics, Kings College London, London, UK.|Department of Biostatistics and Health Informatics, Kings College London, London, UK.|Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Kings College London, London, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Amyotrophic Lateral Sclerosis|Computational Biology|DNA, Bacterial|Databases, Factual|HIV-1|High-Throughput Nucleotide Sequencing|Humans|INDEL Mutation|Polymorphism, Single Nucleotide|RNA, Viral|User-Computer Interface|Whole Genome Sequencing', 'Journal Article', '10.1186/s12859-019-2791-8', '31029080', 'Next Generation Sequencing (NGS) is a commonly used technology for studying the genetic basis of biological processes and it underpins the aspirations of precision medicine. However, there are significant challenges when dealing with NGS data. Firstly, a huge number of bioinformatics tools for a wide range of uses exist, therefore it is challenging to design an analysis pipeline. Secondly, NGS analysis is computationally intensive, requiring expensive infrastructure, and many medical and research centres do not have adequate high performance computing facilities and cloud computing is not always an option due to privacy and ownership issues. Finally, the interpretation of the results is not trivial and most available pipelines lack the utilities to favour this crucial step.'),
(98960, 2019, 'bmc', 'Topological and functional comparison of community detection algorithms in biological networks', 'Rahiminejad, Sara|Maurya, Mano R|Subramaniam, Shankar', 'Departments of Bioengineering and Mechanical and Aerospace Engineering, University of California, San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA.|Department of Bioengineering and San Diego Supercomputer Center, University of California, San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA. mmaurya@ucsd.edu.|Department of Bioengineering, Departments of Computer Science and Engineering, Cellular and Molecular Medicine, and the Graduate Program in Bioinformatics, University of California, San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA. shankar@ucsd.edu.', 'United States|United States|United States', 3, 'Algorithms|Gene Ontology|Humans|Metabolic Networks and Pathways|Protein Interaction Maps|Proteins|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins', 'Journal Article', '10.1186/s12859-019-2746-0', '31029085', 'Community detection algorithms are fundamental tools to uncover important features in networks. There are several studies focused on social networks but only a few deal with biological networks. Directly or indirectly, most of the methods maximize modularity, a measure of the density of links within communities as compared to links between communities.'),
(98961, 2019, 'bmc', 'Adapting machine-learning algorithms to design gene circuits', 'Hiscock, Tom W', 'Cancer Research UK, Cambridge Institute, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK. twh27@cam.ac.uk.', 'United Kingdom', 0, 'Algorithms|Gene Regulatory Networks|Machine Learning|Software', 'Journal Article', '10.1186/s12859-019-2788-3', '31029103', 'Gene circuits are important in many aspects of biology, and perform a wide variety of different functions. For example, some circuits oscillate (e.g. the cell cycle), some are bistable (e.g. as cells differentiate), some respond sharply to environmental signals (e.g. ultrasensitivity), and some pattern multicellular tissues (e.g. Turings model). Often, one starts from a given circuit, and using simulations, asks what functions it can perform. Here we want to do the opposite: starting from a prescribed function, can we find a circuit that executes this function? Whilst simple in principle, this task is challenging from a computational perspective, since gene circuit models are complex systems with many parameters. In this work, we adapted machine-learning algorithms to significantly accelerate gene circuit discovery.'),
(98962, 2019, 'bmc', 'Software tool for internal standard based normalization of lipids, and effect of data-processing strategies on resulting values', 'Koelmel, Jeremy P|Cochran, Jason A|Ulmer, Candice Z|Levy, Allison J|Patterson, Rainey E|Olsen, Berkley C|Yost, Richard A|Bowden, John A|Garrett, Timothy J', 'Department of Chemistry, University of Florida, 214 Leigh Hall, Gainesville, FL, 32611, USA.|College of Engineering, University of Florida, 412 Newell Dr., Gainesville, FL, 32611, USA.|Hollings Marine Laboratory, National Institute of Standards and Technology, 331 Ft. Johnson Road, Charleston, SC, 29412, USA.|Department of Chemistry, University of Florida, 214 Leigh Hall, Gainesville, FL, 32611, USA.|Department of Chemistry, University of Florida, 214 Leigh Hall, Gainesville, FL, 32611, USA.|College of Public Health & Health Professions, University of Florida, 1225 Center Dr., Gainesville, FL, 32611, USA.|Department of Chemistry, University of Florida, 214 Leigh Hall, Gainesville, FL, 32611, USA.|Hollings Marine Laboratory, National Institute of Standards and Technology, 331 Ft. Johnson Road, Charleston, SC, 29412, USA.|Department of Chemistry, University of Florida, 214 Leigh Hall, Gainesville, FL, 32611, USA. tgarrett@ufl.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 7, 'Algorithms|Chromatography, High Pressure Liquid|Humans|Lipids|Software|Tandem Mass Spectrometry', 'Journal Article', '10.1186/s12859-019-2803-8', '31035918', 'Lipidomics, the comprehensive measurement of lipids within a biological system or substrate, is an emerging field with significant potential for improving clinical diagnosis and our understanding of health and disease. While lipids diverse biological roles contribute to their clinical utility, the diversity of lipid structure and concentrations prove to make lipidomics analytically challenging. Without internal standards to match each lipid species, researchers often apply individual internal standards to a broad range of related lipids. To aid in standardizing and automating this relative quantitation process, we developed LipidMatch Normalizer (LMN) http://secim.ufl.edu/secim-tools/ which can be used in most open source lipidomics workflows.'),
(98963, 2019, 'bmc', 'pwrEWAS: a user-friendly tool for comprehensive power estimation for epigenome wide association studies (EWAS)', 'Graw, Stefan|Henn, Rosalyn|Thompson, Jeffrey A|Koestler, Devin C', 'Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, KS, USA. sgraw@kumc.edu.|Department of Cancer Biology, University of Kansas Medical Center, Kansas City, KS, USA.|Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, KS, USA.|Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, KS, USA.', 'United States|United States|United States|United States', 7, 'CpG Islands|DNA Methylation|Epigenesis, Genetic|Humans|Linear Models|Phenotype|Proportional Hazards Models|User-Computer Interface|Vaping', 'Journal Article', '10.1186/s12859-019-2804-7', '31035919', 'When designing an epigenome-wide association study (EWAS) to investigate the relationship between DNA methylation (DNAm) and some exposure(s) or phenotype(s), it is critically important to assess the sample size needed to detect a hypothesized difference with adequate statistical power. However, the complex and nuanced nature of DNAm data makes direct assessment of statistical power challenging. To circumvent these challenges and to address the outstanding need for a user-friendly interface for EWAS power evaluation, we have developed pwrEWAS.'),
(98964, 2019, 'bmc', 'Automated assessment of biological database assertions using the scientific literature', 'Bouadjenek, Mohamed Reda|Zobel, Justin|Verspoor, Karin', 'Department of Mechanical & Industrial Engineering, University of Toronto, Toronto, M5S 3G8, Canada. mrb@mie.utoronto.ca.|School of Computing and Information Systems, University of Melbourne, Melbourne, 3010, Australia.|School of Computing and Information Systems, University of Melbourne, Melbourne, 3010, Australia.', 'Canada|Australia|Australia', 0, 'Algorithms|Data Mining|Databases, Factual|Databases, Nucleic Acid|Humans|Protein Interaction Maps|Publishing', 'Journal Article', '10.1186/s12859-019-2801-x', '31035936', 'The large biological databases such as GenBank contain vast numbers of records, the content of which is substantively based on external resources, including published literature. Manual curation is used to establish whether the literature and the records are indeed consistent. We explore in this paper an automated method for assessing the consistency of biological assertions, to assist biocurators, which we call BARC, Biocuration tool for Assessment of Relation Consistency. In this method a biological assertion is represented as a relation between two objects (for example, a gene and a disease), we then use our novel set-based relevance algorithm SaBRA to retrieve pertinent literature, and apply a classifier to estimate the likelihood that this relation (assertion) is correct.'),
(98965, 2019, 'bmc', 'Reliable heritability estimation using sparse regularization in ultrahigh dimensional genome-wide association studies', 'Li, Xin|Wu, Dongya|Cui, Yue|Liu, Bing|Walter, Henrik|Schumann, Gunter|Li, Chong|Jiang, Tianzi', 'School of Mathematical Sciences, Zhejiang University, 38 Zheda Road, Hangzhou, 310027, China.|Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 East Zhongguancun Road, Beijing, 100190, China.|Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 East Zhongguancun Road, Beijing, 100190, China.|Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 East Zhongguancun Road, Beijing, 100190, China.|Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité, Universitätsmedizin Berlin, Berlin, Germany.|Centre for Population Neuroscience and Stratified Medicine (PONS) and MRC-SGDP Centre, Institute of Psychiatry, Psychology & Neuroscience, Kings College London, London, United Kingdom.|School of Mathematical Sciences, Zhejiang University, 38 Zheda Road, Hangzhou, 310027, China.|Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 East Zhongguancun Road, Beijing, 100190, China. jiangtz@nlpr.ia.ac.cn.', 'China|China|China|China|Germany|United Kingdom|China|China', 0, 'Genome-Wide Association Study|Genomics|Humans|Models, Genetic', 'Journal Article', '10.1186/s12859-019-2792-7', '31039742', 'Data from genome-wide association studies (GWASs) have been used to estimate the heritability of human complex traits in recent years. Existing methods are based on the linear mixed model, with the assumption that the genetic effects are random variables, which is opposite to the fixed effect assumption embedded in the framework of quantitative genetics theory. Moreover, heritability estimators provided by existing methods may have large standard errors, which calls for the development of reliable and accurate methods to estimate heritability.'),
(98966, 2019, 'bmc', 'Focused multidimensional scaling: interactive visualization for exploration of high-dimensional data', 'Urpa, Lea M|Anders, Simon', 'Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.|Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland. s.anders@zmbh.uni-heidelberg.de.', 'Finland|Finland', 0, 'Animals|Cluster Analysis|Computer Graphics|Mice|Software', 'Journal Article', '10.1186/s12859-019-2780-y', '31046657', 'Visualization is an important tool for generating meaning from scientific data, but the visualization of structures in high-dimensional data (such as from high-throughput assays) presents unique challenges. Dimension reduction methods are key in solving this challenge, but these methods can be misleading- especially when apparent clustering in the dimension-reducing representation is used as the basis for reasoning about relationships within the data.'),
(98967, 2019, 'bmc', 'scruff: an R/Bioconductor package for preprocessing single-cell RNA-sequencing data', 'Wang, Zhe|Hu, Junming|Johnson, W Evan|Campbell, Joshua D', 'Bioinformatics Program, Boston University, Boston, MA, USA.|Bioinformatics Program, Boston University, Boston, MA, USA.|Bioinformatics Program, Boston University, Boston, MA, USA.|Bioinformatics Program, Boston University, Boston, MA, USA. camp@bu.edu.', 'United States|United States|United States|United States', 3, 'Gene Expression Profiling|Genomics|Sequence Alignment|Sequence Analysis, RNA|Single-Cell Analysis|Software', 'Journal Article', '10.1186/s12859-019-2797-2', '31046658', 'Single-cell RNA sequencing (scRNA-seq) enables the high-throughput quantification of transcriptional profiles in single cells. In contrast to bulk RNA-seq, additional preprocessing steps such as cell barcode identification or unique molecular identifier (UMI) deconvolution are necessary for preprocessing of data from single cell protocols. R packages that can easily preprocess data and rapidly visualize quality metrics and read alignments for individual cells across multiple samples or runs are still lacking.'),
(98968, 2019, 'bmc', 'm6Acomet: large-scale functional prediction of individual m6A RNA methylation sites from an RNA co-methylation network', 'Wu, Xiangyu|Wei, Zhen|Chen, Kunqi|Zhang, Qing|Su, Jionglong|Liu, Hui|Zhang, Lin|Meng, Jia', 'Department of Biological Sciences, Xian Jiaotong-Liverpool University, Suzhou, 215123, Jiangsu, China.|Department of Biological Sciences, Xian Jiaotong-Liverpool University, Suzhou, 215123, Jiangsu, China.|Department of Biological Sciences, Xian Jiaotong-Liverpool University, Suzhou, 215123, Jiangsu, China.|Department of Biological Sciences, Xian Jiaotong-Liverpool University, Suzhou, 215123, Jiangsu, China.|Department of Mathematical Sciences, Xian Jiaotong-Liverpool University, Suzhou, 215123, Jiangsu, China.|School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, Jiangsu, China.|School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, Jiangsu, China. lin.zhang@cumt.edu.cn.|Department of Biological Sciences, Xian Jiaotong-Liverpool University, Suzhou, 215123, Jiangsu, China. jia.meng@xjtlu.edu.cn.', 'China|China|China|China|China|China|China|China', 7, 'Adenosine|Epigenesis, Genetic|Humans|Methylation|RNA Processing, Post-Transcriptional|Software', 'Journal Article', '10.1186/s12859-019-2840-3', '31046660', 'Over one hundred different types of post-transcriptional RNA modifications have been identified in human. Researchers discovered that RNA modifications can regulate various biological processes, and RNA methylation, especially N6-methyladenosine, has become one of the most researched topics in epigenetics.'),
(98969, 2019, 'bmc', 'Integrating data and knowledge to identify functional modules of genes: a multilayer approach', 'Liang, Lifan|Chen, Vicky|Zhu, Kunju|Fan, Xiaonan|Lu, Xinghua|Lu, Songjian', 'Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.|Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.|Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.|Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.|Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.|Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA. songjian@pitt.edu.', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Cluster Analysis|Gene Expression Profiling|Genes, Fungal|Humans|Protein Interaction Mapping|Saccharomyces cerevisiae Proteins', 'Journal Article', '10.1186/s12859-019-2800-y', '31046665', 'Characterizing the modular structure of cellular network is an important way to identify novel genes for targeted therapeutics. This is made possible by the rising of high-throughput technology. Unfortunately, computational methods to identify functional modules were limited by the data quality issues of high-throughput techniques. This study aims to integrate knowledge extracted from literature to further improve the accuracy of functional module identification.'),
(98970, 2019, 'bmc', 'Efficient proximal gradient algorithm for inference of differential gene networks', 'Wang, Chen|Gao, Feng|Giannakis, Georgios B|DUrso, Gennaro|Cai, Xiaodong', 'Department of Electrical and Computer Engineering, University of Miami, 1251 Memorial Drive, Coral Gables, 33146, FL, USA.|Department of Electrical and Computer Engineering, University of Miami, 1251 Memorial Drive, Coral Gables, 33146, FL, USA.|Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, 55455, MN, USA.|Department of Molecular and Cellular Pharmacology, University of Miami, Miami, 33136, FL, USA.|Department of Electrical and Computer Engineering, University of Miami, 1251 Memorial Drive, Coral Gables, 33146, FL, USA. x.cai@miami.edu.', 'United States|United States|United States|United States|United States', 3, 'Algorithms|Breast Neoplasms|Computer Simulation|Female|Gene Expression Regulation, Neoplastic|Gene Regulatory Networks|Humans|Kidney Neoplasms', 'Journal Article', '10.1186/s12859-019-2749-x', '31046666', 'Gene networks in living cells can change depending on various conditions such as caused by different environments, tissue types, disease states, and development stages. Identifying the differential changes in gene networks is very important to understand molecular basis of various biological process. While existing algorithms can be used to infer two gene networks separately from gene expression data under two different conditions, and then to identify network changes, such an approach does not exploit the similarity between two gene networks, and it is thus suboptimal. A desirable approach would be clearly to infer two gene networks jointly, which can yield improved estimates of network changes.'),
(98971, 2019, 'bmc', 'WASABI: a dynamic iterative framework for gene regulatory network inference', 'Bonnaffoux, Arnaud|Herbach, Ulysse|Richard, Angélique|Guillemin, Anissa|Gonin-Giraud, Sandrine|Gros, Pierre-Alexis|Gandrillon, Olivier', 'University Lyon, ENS de Lyon, University Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell, Lyon, France. a.bonnaffoux@vidium-solutions.com.|University Lyon, ENS de Lyon, University Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell, Lyon, France.|University Lyon, ENS de Lyon, University Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell, Lyon, France.|University Lyon, ENS de Lyon, University Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell, Lyon, France.|University Lyon, ENS de Lyon, University Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell, Lyon, France.|Cosmotech, Lyon, France.|University Lyon, ENS de Lyon, University Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell, Lyon, France.', 'France|France|France|France|France|France|France', 2, 'Algorithms|Animals|Cell Differentiation|Computer Simulation|Erythroid Cells|Gene Expression Profiling|Gene Regulatory Networks|Markov Chains|Single-Cell Analysis|Systems Biology', 'Journal Article', '10.1186/s12859-019-2798-1', '31046682', 'Inference of gene regulatory networks from gene expression data has been a long-standing and notoriously difficult task in systems biology. Recently, single-cell transcriptomic data have been massively used for gene regulatory network inference, with both successes and limitations.'),
(98972, 2019, 'bmc', 'FastqPuri: high-performance preprocessing of RNA-seq data', 'Pérez-Rubio, Paula|Lottaz, Claudio|Engelmann, Julia C', 'Statistical Bioinformatics, Institute of Functional Genomics, University of Regensburg, Am BioPark 9, Regensburg, 93053, Germany.|Statistical Bioinformatics, Institute of Functional Genomics, University of Regensburg, Am BioPark 9, Regensburg, 93053, Germany.|Department of Marine Microbiology and Biogeochemistry, NIOZ Royal Netherlands Institute for Sea Research and Utrecht University, P.O. Box 59, Den Burg, 1790 AB, The Netherlands. julia.engelmann@nioz.nl.', 'Germany|Germany|Netherlands', 4, 'High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/s12859-019-2799-0', '31053060', 'RNA sequencing (RNA-seq) has become the standard means of analyzing gene and transcript expression in high-throughput. While previously sequence alignment was a time demanding step, fast alignment methods and even more so transcript counting methods which avoid mapping and quantify gene and transcript expression by evaluating whether a read is compatible with a transcript, have led to significant speed-ups in data analysis. Now, the most time demanding step in the analysis of RNA-seq data is preprocessing the raw sequence data, such as running quality control and adapter, contamination and quality filtering before transcript or gene quantification. To do so, many researchers chain different tools, but a comprehensive, flexible and fast software that covers all preprocessing steps is currently missing.'),
(98973, 2019, 'bmc', 'Tree reconciliation combined with subsampling improves large scale inference of orthologous group hierarchies', 'Heller, Davide|Szklarczyk, Damian|Mering, Christian von', 'Institute of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, Zurich, 8057, Switzerland.|Institute of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, Zurich, 8057, Switzerland.|Institute of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, Zurich, 8057, Switzerland. mering@imls.uzh.ch.', 'Switzerland|Switzerland|Switzerland', 0, 'Databases, Protein|Phylogeny', 'Journal Article', '10.1186/s12859-019-2828-z', '31060495', 'An orthologous group (OG) comprises a set of orthologous and paralogous genes that share a last common ancestor (LCA). OGs are defined with respect to a chosen taxonomic level, which delimits the position of the LCA in time to a specified speciation event. A hierarchy of OGs expands on this notion, connecting more general OGs, distant in time, to more recent, fine-grained OGs, thereby spanning multiple levels of the tree of life. Large scale inference of OG hierarchies with independently computed taxonomic levels can suffer from inconsistencies between successive levels, such as the position in time of a duplication event. This can be due to confounding genetic signal or algorithmic limitations. Importantly, inconsistencies limit the potential use of OGs for functional annotation and third-party applications.'),
(98974, 2019, 'bmc', 'GeneSurrounder: network-based identification of disease genes in expression data', 'Shah, Sahil D|Braun, Rosemary', 'Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, USA.|Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, USA. rbraun@northwestern.edu.', 'United States|United States', 3, 'Algorithms|Computational Biology|Gene Expression|Gene Regulatory Networks|Humans', 'Journal Article', '10.1186/s12859-019-2829-y', '31060502', 'A key challenge of identifying disease-associated genes is analyzing transcriptomic data in the context of regulatory networks that control cellular processes in order to capture multi-gene interactions and yield mechanistically interpretable results. One existing category of analysis techniques identifies groups of related genes using interaction networks, but these gene sets often comprise tens or hundreds of genes, making experimental follow-up challenging. A more recent category of methods identifies precise gene targets while incorporating systems-level information, but these techniques do not determine whether a gene is a driving source of changes in its network, an important characteristic when looking for potential drug targets.'),
(98975, 2019, 'bmc', 'Metabolic characterization of the chitinolytic bacterium Serratia marcescens using a genome-scale metabolic model', 'Yan, Qiang|Robert, Seth|Brooks, J Paul|Fong, Stephen S', 'Department of Chemical and Life Science Engineering, School of Engineering, Virginia Commonwealth University, West Hall, Room 422, 601 West Main Street, P.O. Box 843028, Richmond, VA, 23284-3028, USA. yanq2@vcu.edu.|Department of Chemical and Life Science Engineering, School of Engineering, Virginia Commonwealth University, West Hall, Room 422, 601 West Main Street, P.O. Box 843028, Richmond, VA, 23284-3028, USA.|Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, P.O. Box 843083, Richmond, VA, 23284, USA.|Department of Chemical and Life Science Engineering, School of Engineering, Virginia Commonwealth University, West Hall, Room 422, 601 West Main Street, P.O. Box 843028, Richmond, VA, 23284-3028, USA. ssfong@vcu.edu.', 'United States|United States|United States|United States', 1, 'Metabolic Engineering|Metabolic Networks and Pathways|Serratia marcescens', 'Journal Article', '10.1186/s12859-019-2826-1', '31060515', 'Serratia marcescens is a chitinolytic bacterium that can potentially be used for consolidated bioprocessing to convert chitin to value-added chemicals. Currently, S. marcescens is poorly characterized and studies on intracellular metabolic and regulatory mechanisms would expedite development of bioprocessing applications.'),
(98976, 2019, 'bmc', 'In silico modeling of phosphorylation dependent and independent c-Myc degradation', 'Chakravorty, Debangana|Banerjee, Krishnendu|Mapder, Tarunendu|Saha, Sudipto', 'Bioinformatics Centre, Bose Institute, Kolkata, India.|Bioinformatics Centre, Bose Institute, Kolkata, India.|ARC CoE for Mathematical and Statistical Frontiers, School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia. mtarunendu@yahoo.com.|Bioinformatics Centre, Bose Institute, Kolkata, India. ssaha4@jcbose.ac.in.', 'India|India|Australia|India', 1, 'Cell Proliferation|Computer Simulation|Humans|Phosphorylation|Proto-Oncogene Proteins c-myc', 'Journal Article', '10.1186/s12859-019-2846-x', '31068129', 'c-Myc plays an important role in cell proliferation, cell growth and in differentiation, making it a key regulator for carcinogenesis and pluripotency. Tight control of c-myc turnover is required by ubiquitin-mediated degradation. This is achieved in the system by two F-box proteins Skp2 and FBXW7.'),
(98977, 2019, 'bmc', 'SpliceV: analysis and publication quality printing of linear and circular RNA splicing, expression and regulation', 'Ungerleider, Nathan|Flemington, Erik', 'Department of Pathology, Tulane Cancer Center, Tulane University School of Medicine, New Orleans, LA, 70112, USA.|Department of Pathology, Tulane Cancer Center, Tulane University School of Medicine, New Orleans, LA, 70112, USA. erik@tulane.edu.', 'United States|United States', 1, 'Alternative Splicing|Exons|High-Throughput Nucleotide Sequencing|Humans|RNA|RNA Splicing|RNA-Binding Proteins', 'Journal Article', '10.1186/s12859-019-2865-7', '31068132', 'In eukaryotes, most genes code for multiple transcript isoforms that are generated through the complex and tightly regulated process of RNA splicing. Despite arising from identical precursor transcripts, alternatively spliced RNAs can have dramatically different functions. Transcriptome complexity is elevated further by the production of circular RNAs (circRNAs), another class of mature RNA that results from the splicing of a downstream splice donor to an upstream splice acceptor. While there has been a rapid expansion of circRNA catalogs in the last few years through the utilization of next generation sequencing approaches, our understanding of the mechanisms and regulation of circular RNA biogenesis, the impact that circRNA generation has on parental transcript processing, and the functions carried out by circular RNAs remains limited.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(98978, 2019, 'bmc', 'System-level analysis of metabolic trade-offs during anaerobic photoheterotrophic growth in Rhodopseudomonas palustris', 'Navid, Ali|Jiao, Yongqin|Wong, Sergio Ernesto|Pett-Ridge, Jennifer', 'Physics and Life Sciences Directorate, Lawrence Livermore National Laboratory, 7000 East Ave., Livermore, CA, 94550, USA. navid1@llnl.gov.|Physics and Life Sciences Directorate, Lawrence Livermore National Laboratory, 7000 East Ave., Livermore, CA, 94550, USA.|Physics and Life Sciences Directorate, Lawrence Livermore National Laboratory, 7000 East Ave., Livermore, CA, 94550, USA.|Physics and Life Sciences Directorate, Lawrence Livermore National Laboratory, 7000 East Ave., Livermore, CA, 94550, USA.', 'United States|United States|United States|United States', 2, 'Phototrophic Processes|Rhodopseudomonas', 'Journal Article', '10.1186/s12859-019-2844-z', '31072303', 'Living organisms need to allocate their limited resources in a manner that optimizes their overall fitness by simultaneously achieving several different biological objectives. Examination of these biological trade-offs can provide invaluable information regarding the biophysical and biochemical bases behind observed cellular phenotypes. A quantitative knowledge of a cell systems critical objectives is also needed for engineering of cellular metabolism, where there is interest in mitigating the fitness costs that may result from human manipulation.'),
(98979, 2019, 'bmc', 'Resolving repeat families with long reads', 'Bongartz, Philipp', 'Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, Heidelberg, 69118, Germany. Philipp.Bongartz@h-its.org.', 'Germany', 2, 'Algorithms|Databases, Genetic|Genome|Humans|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-019-2807-4', '31072311', 'Draft quality genomes for a multitude of organisms have become common due to the advancement of genome assemblers using long-read technologies with high error rates. Although current assemblies are substantially more contiguous than assemblies based on short reads, complete chromosomal assemblies are still challenging. Interspersed repeat families with multiple copy versions dominate the contig and scaffold ends of current long-read assemblies for complex genomes. These repeat families generally remain unresolved, as existing algorithmic solutions either do not scale to large copy numbers or can not handle the current high read error rates.'),
(98980, 2019, 'bmc', 'NanoDJ: a Dockerized Jupyter notebook for interactive Oxford Nanopore MinION sequence manipulation and genome assembly', 'Rodríguez-Pérez, Héctor|Hernández-Beeftink, Tamara|Lorenzo-Salazar, José M|Roda-García, José L|Pérez-González, Carlos J|Colebrook, Marcos|Flores, Carlos', 'Research Unit, Hospital Universitario Nuestra Señora de Candelaria, Universidad de La Laguna, Santa Cruz de Tenerife, Spain.|Research Unit, Hospital Universitario Nuestra Señora de Candelaria, Universidad de La Laguna, Santa Cruz de Tenerife, Spain.|Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), Santa Cruz de Tenerife, Spain.|Departamento de Ingeniería Informática y de Sistemas, Universidad de La Laguna, Santa Cruz de Tenerife, Spain.|Departamento de Matemáticas, Estadística e Investigación Operativa, Universidad de La Laguna, Santa Cruz de Tenerife, Spain.|Departamento de Ingeniería Informática y de Sistemas, Universidad de La Laguna, Santa Cruz de Tenerife, Spain. mcolesan@ull.edu.es.|Research Unit, Hospital Universitario Nuestra Señora de Candelaria, Universidad de La Laguna, Santa Cruz de Tenerife, Spain. cflores@ull.edu.es.', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain', 1, 'Genomics|High-Throughput Nucleotide Sequencing|Nanopores|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-019-2860-z', '31072312', 'The Oxford Nanopore Technologies (ONT) MinION portable sequencer makes it possible to use cutting-edge genomic technologies in the field and the academic classroom.'),
(98981, 2019, 'bmc', 'A voting mechanism-based linear epitope prediction system for the host-specific Iridoviridae family', 'Shih, Tao-Chuan|Ho, Li-Ping|Wu, Jen-Leih|Chou, Hsin-Yiu|Pai, Tun-Wen', 'Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan.|Center of Excellence for the Oceans, National Taiwan Ocean University, Keelung, Taiwan.|Department of Bioscience and Biotechnology, National Taiwan Ocean University, Keelung, Taiwan.|Center of Excellence for the Oceans, National Taiwan Ocean University, Keelung, Taiwan. hychou@mail.ntou.edu.tw.|Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan. twp@csie.ntut.edu.tw.', '-|-|-|-|-', 0, 'Animals|DNA Virus Infections|Epitopes|Host-Pathogen Interactions|Invertebrates|Iridoviridae|Vertebrates|Viral Proteins', 'Journal Article', '10.1186/s12859-019-2736-2', '31074372', 'The Iridoviridae family is categorized into five genera and clustered into two subfamilies: Alphairidovirinae includes Lymphocystivirus, Ranavirus (GIV), and Megalocystivirus (TGIV), which infect vertebrate hosts and Betairidovirinae includes Iridovirus and Chloriridovirus, which infect invertebrate hosts. Clustered Iridoviridae subfamilies possess host-specific characteristics, which can be considered as exclusive features for in-silico prediction of effective epitopes for vaccine development. A voting mechanism-based linear epitope (LE) prediction system was applied to identify and endorse LE candidates with a minimum length requirement for each clustered subfamily RESULTS: The experimental results showed that four conserved epitopes among the Iridovirideae family, one exclusive epitope for invertebrate subfamily and two exclusive epitopes for vertebrate family were predicted. These predicted LE candidates were further validated by ELISA assays for evaluating the strength of antigenicity and cross antigenicity. The conserved LEs for Iridoviridae family reflected high antigenicity responses for the two subfamilies, while exclusive LEs reflected high antigenicity responses only for the host-specific subfamily CONCLUSIONS: Host-specific characteristics are important features and constraints for effective epitope prediction. Our proposed voting mechanism based system provides a novel approach for in silico LE prediction prior to vaccine development, and it is especially powerful for analyzing antigen sequences with exclusive features between two clustered groups.'),
(98982, 2019, 'bmc', 'MD-SVM: a novel SVM-based algorithm for the motif discovery of transcription factor binding sites', 'Hu, Jialu|Wang, Jingru|Lin, Jianan|Liu, Tianwei|Zhong, Yuanke|Liu, Jie|Zheng, Yan|Gao, Yiqun|He, Junhao|Shang, Xuequn', 'School of Computer Science, Northwestern Polytechnical University, West Youyi Road 127, Xian, 710072, China. jhu@nwpu.edu.cn.|School of Computer Science, Northwestern Polytechnical University, West Youyi Road 127, Xian, 710072, China.|School of Computer Science, Northwestern Polytechnical University, West Youyi Road 127, Xian, 710072, China.|School of Computer Science, Northwestern Polytechnical University, West Youyi Road 127, Xian, 710072, China.|School of Computer Science, Northwestern Polytechnical University, West Youyi Road 127, Xian, 710072, China.|School of Computer Science, Northwestern Polytechnical University, West Youyi Road 127, Xian, 710072, China.|School of Computer Science, Northwestern Polytechnical University, West Youyi Road 127, Xian, 710072, China.|School of Computer Science, Northwestern Polytechnical University, West Youyi Road 127, Xian, 710072, China.|School of Computer Science, Northwestern Polytechnical University, West Youyi Road 127, Xian, 710072, China.|School of Computer Science, Northwestern Polytechnical University, West Youyi Road 127, Xian, 710072, China.', 'China|China|China|China|China|China|China|China|China|China', 4, 'Algorithms|Binding Sites|Humans|Nucleotide Motifs|Protein Binding|Support Vector Machine|Transcription Factors', 'Journal Article', '10.1186/s12859-019-2735-3', '31074373', 'Transcription factors (TFs) play important roles in the regulation of gene expression. They can activate or block transcription of downstream genes in a manner of binding to specific genomic sequences. Therefore, motif discovery of these binding preference patterns is of central significance in the understanding of molecular regulation mechanism. Many algorithms have been proposed for the identification of transcription factor binding sites. However, it remains a challengeable problem.'),
(98983, 2019, 'bmc', 'Investigation of lipid metabolism dysregulation and the effects on immune microenvironments in pan-cancer using multiple omics data', 'Hao, Yang|Li, Daixi|Xu, Yong|Ouyang, Jian|Wang, Yongkun|Zhang, Yuqi|Li, Baoguo|Xie, Lu|Qin, Guangrong', 'School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.|School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China. dxli75@126.com.|School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.|Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai, 201203, China.|School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.|School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.|School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.|Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai, 201203, China. luxiex2017@outlook.com.|Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai, 201203, China. guangrong.qin@systemsbiology.org.', 'China|China|China|China|China|China|China|China|China', 7, 'Biomarkers, Tumor|Computational Biology|DNA Methylation|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Lipid Metabolism|Mutation|Neoplasms|Transcriptome|Tumor Microenvironment', 'Journal Article', '10.1186/s12859-019-2734-4', '31074374', 'Lipid metabolism reprogramming is a hallmark for tumor which contributes to tumorigenesis and progression, but the commonality and difference of lipid metabolism among pan-cancer is not fully investigated. Increasing evidences suggest that the alterations in tumor metabolism, including metabolite abundance and accumulation of metabolic products, lead to local immunosuppression in the tumor microenvironment. An integrated analysis of lipid metabolism in cancers from different tissues using multiple omics data may provide novel insight into the understanding of tumorigenesis and progression.'),
(98984, 2019, 'bmc', 'Systems biology intertwines with single cell and AI', 'Wang, Yong|Zhang, Xiang-Sun|Chen, Luonan', 'CEMS, NCMIS, MDIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China. ywang@amss.ac.cn.|CEMS, NCMIS, MDIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China.|Key Laboratory of Systems Biology, SIBS-Novo Nordisk Translational Research Centre for PreDiabetes, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, China. lnchen@sibs.ac.cn.', 'China|China|China', 0, 'Artificial Intelligence|Computational Biology|Congresses as Topic|Genomics|Humans|Single-Cell Analysis|Systems Biology', 'Editorial', '10.1186/s12859-019-2731-7', '31074375', 'A report of the 12th International Conference on Systems Biology (ISB2018), 18-21 August, Guiyang, China.'),
(98985, 2019, 'bmc', 'Generalized gene co-expression analysis via subspace clustering using low-rank representation', 'Wang, Tongxin|Zhang, Jie|Huang, Kun', 'Department of Computer Science, Indiana University Bloomington, Bloomington, 47408, IN, USA.|Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, 46202, IN, USA.|Department of Medicine, Indiana University School of Medicine, Indianapolis, 46202, IN, USA. kunhuang@iu.edu.', 'India|India|India', 0, 'Algorithms|Cluster Analysis|Computational Biology|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Gene Regulatory Networks|Humans|Neoplasm Proteins|Neoplasms', 'Journal Article', '10.1186/s12859-019-2733-5', '31074376', 'Gene Co-expression Network Analysis (GCNA) helps identify gene modules with potential biological functions and has become a popular method in bioinformatics and biomedical research. However, most current GCNA algorithms use correlation to build gene co-expression networks and identify modules with highly correlated genes. There is a need to look beyond correlation and identify gene modules using other similarity measures for finding novel biologically meaningful modules.'),
(98986, 2019, 'bmc', 'ODAE: Ontology-based systematic representation and analysis of drug adverse events and its usage in study of adverse events given different patient age and disease conditions', 'Yu, Hong|Nysak, Solomiya|Garg, Noemi|Ong, Edison|Ye, Xianwei|Zhang, Xiangyan|He, Yongqun', 'Department of Pulmonary and Critical Care Medicine, Guizhou Provincial Peoples Hospital, Guiyang, 550002, Guizhou, China. yuhong20040416@sina.com.|College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI, 48109, USA.|College of Pharmacy, University of Michigan, Ann Arbor, MI, 48109, USA.|Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.|Department of Pulmonary and Critical Care Medicine, Guizhou Provincial Peoples Hospital, Guiyang, 550002, Guizhou, China.|Department of Pulmonary and Critical Care Medicine, Guizhou Provincial Peoples Hospital, Guiyang, 550002, Guizhou, China.|Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, Center for Computational Medicine and Bioinformatics, and Comprehensive Cancer Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA. yongqunh@med.umich.edu.', 'China|United States|United States|United States|China|China|United States', 0, 'Adult|Adverse Drug Reaction Reporting Systems|Age Factors|Anti-Bacterial Agents|Antibiotics, Antineoplastic|Drug-Related Side Effects and Adverse Reactions|Humans|Linezolid|Nervous System Diseases|Pharmaceutical Preparations|Sirolimus|Software', 'Journal Article', '10.1186/s12859-019-2729-1', '31074377', 'Drug adverse events (AEs), or called adverse drug events (ADEs), are ranked one of the leading causes of mortality. The Ontology of Adverse Events (OAE) has been widely used for adverse event AE representation, standardization, and analysis. OAE-based ADE-specific ontologies, including ODNAE for drug-associated neuropathy-inducing AEs and OCVDAE for cardiovascular drug AEs, have also been developed and used. However, these ADE-specific ontologies do not consider the effects of other factors (e.g., age and drug-treated disease) on the outcomes of ADEs. With more ontological studies of ADEs, it is also critical to develop a general purpose ontology for representing ADEs for various types of drugs.'),
(98987, 2019, 'bmc', 'The cis-trans binding strength defined by motif frequencies facilitates statistical inference of transcriptional regulation', 'Feng, Yance|Zhang, Sheng|Li, Liang|Li, Lei M', 'National Center of Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China.|National Center of Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China.|National Center of Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China.|National Center of Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China. lilei@amss.ac.cn.', 'China|China|China|China', 0, 'Adipogenesis|Angiogenesis Inducing Agents|Animals|Docosahexaenoic Acids|Eicosapentaenoic Acid|Gene Expression Regulation|Hyperlipidemias|Intestine, Small|Mice|Nucleotide Motifs|Promoter Regions, Genetic|Transcription, Genetic', 'Journal Article', '10.1186/s12859-019-2732-6', '31074378', 'A key problem in systems biology is the determination of the regulatory mechanism corresponding to a phenotype. An empirical approach in this regard is to compare the expression profiles of cells under two conditions or tissues from two phenotypes and to unravel the underlying transcriptional regulation. We have proposed the method BASE to statistically infer the effective regulatory factors that are responsible for the gene expression differentiation with the help from the binding data between factors and genes. Usually the protein-DNA binding data are obtained by ChIP-seq experiments, which could be costly and are condition-specific.'),
(98988, 2019, 'bmc', 'Computed tomography angiography-based analysis of high-risk intracerebral haemorrhage patients by employing a mathematical model', 'Zhang, Le|Li, Jin|Yin, Kaikai|Jiang, Zhouyang|Li, Tingting|Hu, Rong|Yu, Zheng|Feng, Hua|Chen, Yujie', 'College of Computer and Information Science, Southwest University, Chongqing, 400715, Peoples Republic of China. zhangle06@scu.edu.cn.|College of Computer and Information Science, Southwest University, Chongqing, 400715, Peoples Republic of China.|College of Computer and Information Science, Southwest University, Chongqing, 400715, Peoples Republic of China.|Department of Neurosurgery, Southwest Hospital, Third Military Medical University, Chongqing, 400038, Peoples Republic of China.|School of Mathematics and Statistics, Southwest University, Chongqing, 400715, Peoples Republic of China.|Department of Neurosurgery, Southwest Hospital, Third Military Medical University, Chongqing, 400038, Peoples Republic of China.|Department of Neurosurgery, Fuling Central Hospital, Chongqing, 400715, Peoples Republic of China.|Department of Neurosurgery, Southwest Hospital, Third Military Medical University, Chongqing, 400038, Peoples Republic of China.|Department of Neurosurgery, Southwest Hospital, Third Military Medical University, Chongqing, 400038, Peoples Republic of China. yujiechen6886@foxmail.com.', 'China|China|China|China|China|China|China|China|China', 5, 'Cerebral Hemorrhage|Computed Tomography Angiography|Humans|Hypertension|Models, Theoretical|Predictive Value of Tests|Risk Factors', 'Journal Article', '10.1186/s12859-019-2741-5', '31074379', 'Haemorrhagic stroke accounts for approximately 31.52% of all stroke cases, and the most common origin is hypertension. However, little is known about the method to identify high-risk populations of hypertensive intracerebral haemorrhage.'),
(98989, 2019, 'bmc', 'Extracting predictors for lung adenocarcinoma based on Granger causality test and stepwise character selection', 'Fan, Xuemeng|Wang, Yaolai|Tang, Xu-Qing', 'School of Science, Jiangnan University, Wuxi, 214122, China.|School of Science, Jiangnan University, Wuxi, 214122, China.|School of Science, Jiangnan University, Wuxi, 214122, China. txq5139@jiangnan.edu.cn.', 'China|China|China', 1, 'Adenocarcinoma|Aged|Algorithms|Computational Biology|DNA Methylation|Female|Gene Regulatory Networks|Humans|Lung Neoplasms|Male|MicroRNAs|Middle Aged', 'Journal Article', '10.1186/s12859-019-2739-z', '31074380', 'Lung adenocarcinoma is the most common type of lung cancer, with high mortality worldwide. Its occurrence and development were thoroughly studied by high-throughput expression microarray, which produced abundant data on gene expression, DNA methylation, and miRNA quantification. However, the hub genes, which can be served as bio-markers for discriminating cancer and healthy individuals, are not well screened.'),
(98990, 2019, 'bmc', 'VPAC: Variational projection for accurate clustering of single-cell transcriptomic data', 'Chen, Shengquan|Hua, Kui|Cui, Hongfei|Jiang, Rui', 'MOE Key Laboratory of Bioinformatics, Bioinformatics Division, Beijing National Research Center for Information Science and Technology, Department of Automation, Tsinghua University, Beijing, 100084, China.|MOE Key Laboratory of Bioinformatics, Bioinformatics Division, Beijing National Research Center for Information Science and Technology, Department of Automation, Tsinghua University, Beijing, 100084, China.|MOE Key Laboratory of Bioinformatics, Bioinformatics Division, Beijing National Research Center for Information Science and Technology, Department of Automation, Tsinghua University, Beijing, 100084, China.|MOE Key Laboratory of Bioinformatics, Bioinformatics Division, Beijing National Research Center for Information Science and Technology, Department of Automation, Tsinghua University, Beijing, 100084, China. ruijiang@tsinghua.edu.cn.', 'China|China|China|China', 0, 'Algorithms|Cluster Analysis|Gene Expression Profiling|Humans|Sequence Analysis, RNA|Single-Cell Analysis|T-Lymphocytes|Transcriptome', 'Journal Article', '10.1186/s12859-019-2742-4', '31074382', 'Single-cell RNA-sequencing (scRNA-seq) technologies have advanced rapidly in recent years and enabled the quantitative characterization at a microscopic resolution. With the exponential growth of the number of cells profiled in individual scRNA-seq experiments, the demand for identifying putative cell types from the data has become a great challenge that appeals for novel computational methods. Although a variety of algorithms have recently been proposed for single-cell clustering, such limitations as low accuracy, inferior robustness, and inadequate stability greatly impede the scope of applications of these methods.'),
(98991, 2019, 'bmc', 'Predicting stage-specific cancer related genes and their dynamic modules by integrating multiple datasets', 'Aouiche, Chaima|Chen, Bolin|Shang, Xuequn', 'School of Computer Science, Northwestern Polytechnical University, Xian, 710072, China.|School of Computer Science, Northwestern Polytechnical University, Xian, 710072, China. blchen@nwpu.edu.cn.|School of Computer Science, Northwestern Polytechnical University, Xian, 710072, China.', 'China|China|China', 0, 'Algorithms|Computational Biology|Databases, Genetic|Disease Progression|Gene Regulatory Networks|Humans|Molecular Sequence Annotation|Neoplasm Proteins|Neoplasm Staging|Neoplasms|Predictive Value of Tests', 'Journal Article', '10.1186/s12859-019-2740-6', '31074385', 'The mechanism of many complex diseases has not been detected accurately in terms of their stage evolution. Previous studies mainly focus on the identification of associations between genes and individual diseases, but less is known about their associations with specific disease stages. Exploring biological modules through different disease stages could provide valuable knowledge to genomic and clinical research.'),
(98992, 2019, 'bmc', 'Screening drug target combinations in disease-related molecular networks', 'Luo, Min|Jiao, Jianfeng|Wang, Ruiqi', 'Department of Mathematics, Shanghai University, No.99, Shangda Road, Shanghai, China.|Department of Mathematics, Shanghai University, No.99, Shangda Road, Shanghai, China.|Department of Mathematics, Shanghai University, No.99, Shangda Road, Shanghai, China. rqwang@shu.edu.cn.', 'China|China|China', 0, 'Algorithms|Drug Combinations|Drug Delivery Systems|Drug Discovery|Drug Evaluation, Preclinical|Humans|Metabolic Networks and Pathways|Neoplasms', 'Journal Article', '10.1186/s12859-019-2730-8', '31074386', 'For treating a complex disease such as cancer, some effective means are needed to control biological networks that underlies the disease. The one-target one-drug paradigm has been the dominating drug discovery approach in the past decades. Compared to single target-based drugs, combination drug targets may overcome many limitations of single drug target and achieve a more effective and safer control of the disease. Most of existing combination drug targets are developed based on clinical experience or text-and-trial strategy, which cannot provide theoretical guidelines for designing and screening effective drug combinations. Therefore, systematic identification of multiple drug targets and optimal intervention strategy needs to be developed.'),
(98993, 2019, 'bmc', 'Detecting the stable point of therapeutic effect of chronic myeloid leukemia based on dynamic network biomarkers', 'Xu, Junhua|Wu, Min|Zhu, Shanshan|Lei, Jinzhi|Gao, Jie', 'School of Science,Jiangnan University, Wuxi, 214122, China.|School of Science,Jiangnan University, Wuxi, 214122, China.|School of Science,Jiangnan University, Wuxi, 214122, China.|Zhou Pei-Yuan Center for Applied Mathematics, Tsinghua University, Beijing, 100084, China.|School of Science,Jiangnan University, Wuxi, 214122, China. gaojie@jiangnan.edu.cn.', 'China|China|China|China|China', 0, 'Antineoplastic Combined Chemotherapy Protocols|Biomarkers, Tumor|Case-Control Studies|Gene Expression Regulation, Neoplastic|Gene Regulatory Networks|Humans|Leukemia, Myelogenous, Chronic, BCR-ABL Positive', 'Journal Article', '10.1186/s12859-019-2738-0', '31074387', 'Most researches of chronic myeloid leukemia (CML) are currently focused on the treatment methods, while there are relatively few researches on the progress of patients condition after drug treatment. Traditional biomarkers of disease can only distinguish normal state from disease state, and cannot recognize the pre-stable state after drug treatment.'),
(98994, 2019, 'bmc', 'Multiscale modeling reveals angiogenesis-induced drug resistance in brain tumors and predicts a synergistic drug combination targeting EGFR and VEGFR pathways', 'Liang, Weishan|Zheng, Yongjiang|Zhang, Ji|Sun, Xiaoqiang', 'Zhong-shan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China.|Department of Hematology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.|Department of Neurosurgery, State Key Laboratory of Oncology in South China, Sun Yat-Sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510275, China.|Zhong-shan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China. sunxq6@mail.sysu.edu.cn.', 'China|China|China|China', 4, 'Brain Neoplasms|Drug Combinations|Drug Resistance, Neoplasm|Drug Synergism|ErbB Receptors|Humans|Models, Statistical|Neovascularization, Pathologic|Prognosis|Protein Kinase Inhibitors|Signal Transduction|Survival Rate|Tumor Microenvironment|Vascular Endothelial Growth Factor Receptor-1', 'Journal Article', '10.1186/s12859-019-2737-1', '31074391', 'Experimental studies have demonstrated that both the extracellular vasculature or microenvironment and intracellular molecular network (e.g., epidermal growth factor receptor (EGFR) signaling pathway) are important for brain tumor growth. Additionally, some drugs have been developed to inhibit EGFR signaling pathways. However, how angiogenesis affects the response of tumor cells to drug treatment has rarely been mechanistically studied. Therefore, a multiscale model is required to investigate such complex biological systems that contain interactions and feedback among multiple levels.'),
(98995, 2019, 'bmc', 'pTrimmer: An efficient tool to trim primers of multiplex deep sequencing data', 'Zhang, Xiaolong|Shao, Yanyan|Tian, Jichao|Liao, Yuwei|Li, Peiying|Zhang, Yu|Chen, Jun|Li, Zhiguang', 'Center of Genome and Personalized Medicine, Institute of Cancer Stem Cell, Dalian Medical University, Dalian, 116044, Liaoning, China.|Center of Genome and Personalized Medicine, Institute of Cancer Stem Cell, Dalian Medical University, Dalian, 116044, Liaoning, China.|Center of Genome and Personalized Medicine, Institute of Cancer Stem Cell, Dalian Medical University, Dalian, 116044, Liaoning, China.|Center of Genome and Personalized Medicine, Institute of Cancer Stem Cell, Dalian Medical University, Dalian, 116044, Liaoning, China.|Center of Genome and Personalized Medicine, Institute of Cancer Stem Cell, Dalian Medical University, Dalian, 116044, Liaoning, China.|Center of Genome and Personalized Medicine, Institute of Cancer Stem Cell, Dalian Medical University, Dalian, 116044, Liaoning, China.|The Second Hospital of Dalian Medical University, 467th Zhongshan Road, Shahekou District, Dalian, 116023, Liaoning, China. chenjundl@vip.sina.com.|Center of Genome and Personalized Medicine, Institute of Cancer Stem Cell, Dalian Medical University, Dalian, 116044, Liaoning, China. zhiguangli2013@126.com.', 'China|China|China|China|China|China|China|China', 0, 'Algorithms|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-019-2854-x', '31077131', 'With the widespread use of multiple amplicon-sequencing (MAS) in genetic variation detection, an efficient tool is required to remove primer sequences from short reads to ensure the reliability of downstream analysis. Although some tools are currently available, their efficiency and accuracy require improvement in trimming large scale of primers in high throughput target genome sequencing. This issue is becoming more urgent considering the potential clinical implementation of MAS for processing patient samples. We here developed pTrimmer that could handle thousands of primers simultaneously with greatly improved accuracy and performance.'),
(98996, 2019, 'bmc', 'miRspongeR: an R/Bioconductor package for the identification and analysis of miRNA sponge interaction networks and modules', 'Zhang, Junpeng|Liu, Lin|Xu, Taosheng|Xie, Yong|Zhao, Chunwen|Li, Jiuyong|Le, Thuc Duy', 'School of Engineering, Dali University, Dali, 671003, Yunnan, China. zhangjunpeng_411@yahoo.com.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China.|School of Engineering, Dali University, Dali, 671003, Yunnan, China.|School of Engineering, Dali University, Dali, 671003, Yunnan, China.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia. thuc.le@unisa.edu.au.', 'China|Australia|China|China|China|Australia|Australia', 0, 'Gene Regulatory Networks|Humans|MicroRNAs', 'Journal Article', '10.1186/s12859-019-2861-y', '31077152', 'A microRNA (miRNA) sponge is an RNA molecule with multiple tandem miRNA response elements that can sequester miRNAs from their target mRNAs. Despite growing appreciation of the importance of miRNA sponges, our knowledge of their complex functions remains limited. Moreover, there is still a lack of miRNA sponge research tools that help researchers to quickly compare their proposed methods with other methods, apply existing methods to new datasets, or select appropriate methods for assisting in subsequent experimental design.'),
(98997, 2019, 'bmc', 'anamiR: integrated analysis of MicroRNA and gene expression profiling', 'Wang, Ti-Tai|Lee, Chien-Yueh|Lai, Liang-Chuan|Tsai, Mong-Hsun|Lu, Tzu-Pin|Chuang, Eric Y', 'Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, 10617, Taiwan.|Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, 10617, Taiwan.|Graduate Institute of Physiology, National Taiwan University, Taipei, 10051, Taiwan.|Bioinformatics and Biostatistics Core, Center of Genomic Medicine, National Taiwan University, Taipei, 10055, Taiwan.|Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, 10055, Taiwan. tplu@ntu.edu.tw.|Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, 10617, Taiwan. chuangey@ntu.edu.tw.', '-|-|-|-|-|-', 1, 'Algorithms|Gene Expression Profiling|Humans|MicroRNAs', 'Journal Article', '10.1186/s12859-019-2870-x', '31088348', 'With advancements in high-throughput technologies, the cost of obtaining expression profiles of both mRNA and microRNA in the same individual has substantially decreased. Integrated analysis of these profiles can help to elucidate the functional effects of RNA expression in complex diseases, such as cancer. However, fundamental discrepancies are observed in the results from microRNA-mRNA target gene prediction algorithms, and few packages can be used to analyze microRNA and mRNA expression levels simultaneously.'),
(98998, 2019, 'bmc', 'Variable cellular decision-making behavior in a constant synthetic network topology', 'Shah, Najaf A|Sarkar, Casim A', 'Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.|Department of Biomedical Engineering, College of Science and Engineering, University of Minnesota, Minneapolis, MN, USA. csarkar@umn.edu.', 'United States|United States', 0, 'Cell Physiological Phenomena|Gene Regulatory Networks|Saccharomyces cerevisiae', 'Journal Article', '10.1186/s12859-019-2866-6', '31088350', 'Modules of interacting components arranged in specific network topologies have evolved to perform a diverse array of cellular functions. For a network with a constant topological structure, its function within a cell may still be tuned by changing the number of instances of a particular component (e.g., gene copy number) or by modulating the intrinsic biochemical properties of a component (e.g., binding strength or catalytic efficiency). How such perturbations affect cellular response dynamics remains poorly understood. Here, we explored these effects in a common decision-making motif, cross-antagonism with autoregulation, by synthetically constructing this network in yeast.'),
(98999, 2019, 'bmc', 'A random walk-based method to identify driver genes by integrating the subcellular localization and variation frequency into bipartite graph', 'Song, Junrong|Peng, Wei|Wang, Feng', 'Faculty of Management and Economics/Computer center/Faculty of Information Engineering and Automation/Technology Application Key Lab of Yunnan Province, Kunming University of Science and Technology, Lianhua Road, 650050, Kunming, Peoples Republic of China.|Faculty of Management and Economics/Computer center/Faculty of Information Engineering and Automation/Technology Application Key Lab of Yunnan Province, Kunming University of Science and Technology, Lianhua Road, 650050, Kunming, Peoples Republic of China. weipeng1980@gmail.com.|Faculty of Management and Economics/Computer center/Faculty of Information Engineering and Automation/Technology Application Key Lab of Yunnan Province, Kunming University of Science and Technology, Lianhua Road, 650050, Kunming, Peoples Republic of China.', 'China|China|China', 3, 'Gene Regulatory Networks|Genomics|Humans|Reproducibility of Results', 'Journal Article', '10.1186/s12859-019-2847-9', '31088372', 'Cancer as a worldwide problem is driven by genomic alterations. With the advent of high-throughput sequencing technology, a huge amount of genomic data generates at every second which offer many valuable cancer information and meanwhile throw a big challenge to those investigators. As the major characteristic of cancer is heterogeneity and most of alterations are supposed to be useless passenger mutations that make no contribution to the cancer progress. Hence, how to dig out driver genes that have effect on a selective growth advantage in tumor cells from those tremendously and noisily data is still an urgent task.'),
(99000, 2019, 'bmc', 'Balancing sensitivity and specificity in distinguishing TCR groups by CDR sequence similarity', 'Thakkar, Neerja|Bailey-Kellogg, Chris', 'Department of Computer Science, Dartmouth, Hanover, NH, USA.|Department of Computer Science, Dartmouth, Hanover, NH, USA. cbk@cs.dartmouth.edu.', 'United States|United States', 0, 'Amino Acid Motifs|Amino Acid Sequence|Complementarity Determining Regions|Epitopes|Humans|Major Histocompatibility Complex|Peptides|Receptors, Antigen, T-Cell|Sequence Homology, Amino Acid|Twins', 'Journal Article', '10.1186/s12859-019-2864-8', '31092185', 'Repertoire sequencing is enabling deep explorations into the cellular immune response, including the characterization of commonalities and differences among T cell receptor (TCR) repertoires from different individuals, pathologies, and antigen specificities. In seeking to understand the generality of patterns observed in different groups of TCRs, it is necessary to balance how well each pattern represents the diversity among TCRs from one group (sensitivity) vs. how many TCRs from other groups it also represents (specificity). The variable complementarity determining regions (CDRs), particularly the third CDRs (CDR3s) interact with major histocompatibility complex (MHC)-presented epitopes from putative antigens, and thus encode the determinants of recognition.'),
(99001, 2019, 'bmc', 'Time-dependent antagonist-agonist switching in receptor tyrosine kinase-mediated signaling', 'Ghosh, Alokendra|Radhakrishnan, Ravi', 'Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, USA.|Department of Bioengineering, University of Pennsylvania, Philadelphia, USA. rradhak@seas.upenn.edu.', 'United States|United States', 2, 'Animals|Caseins|Cell Differentiation|Cell Line|Cell Nucleus|Epithelial Cells|Janus Kinase 2|Ligands|Mice|Models, Biological|Protein Multimerization|Protein Transport|RNA, Messenger|Receptor Protein-Tyrosine Kinases|Receptor, ErbB-4|STAT5 Transcription Factor|Signal Transduction|Time Factors|Transcription, Genetic', 'Journal Article', '10.1186/s12859-019-2816-3', '31092187', 'ErbB4/HER4 is a unique member of the ErbB family of receptor tyrosine kinases concerning its activation of anti-proliferative JAK2-STAT5 pathway when stimulated by ligand Neuregulin (NRG). Activation of this pathway leads to expression of genes like xcexb2-casein which promote cell differentiation. Recent experimental studies on mouse HC11 mammary epithelial cells stimulated by ligand Neuregulin (NRG) showed a time-dependent switching behavior in the xcexb2-casein expression. This behavior cannot be explained using currently available mechanistic models of the JAK-STAT pathway. We constructed an improved mechanistic model which introduces two crucial modifications to the canonical HER4-JAK2-STAT5 pathway based on literature findings. These modifications include competitive HER4 heterodimerization with other members of the ErbB family and a slower JAK2 independent activation STAT5 through HER4. We also performed global sensitivity analysis on the model to test the robustness of the predictions and parameter combinations that are sensitive to the outcome.'),
(99002, 2019, 'bmc', 'PathMe: merging and exploring mechanistic pathway knowledge', 'Domingo-Fernández, Daniel|Mubeen, Sarah|Marín-Llaó, Josep|Hoyt, Charles Tapley|Hofmann-Apitius, Martin', 'Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53754, Sankt Augustin, Germany. daniel.domingo.fernandez@scai.fraunhofer.de.|Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53754, Sankt Augustin, Germany.|Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53754, Sankt Augustin, Germany.|Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53754, Sankt Augustin, Germany.|Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53754, Sankt Augustin, Germany.', 'Germany|Germany|Germany|Germany|Germany', 0, 'Computational Biology|Databases as Topic|Databases, Factual|Humans|Signal Transduction|Software|TOR Serine-Threonine Kinases', 'Journal Article', '10.1186/s12859-019-2863-9', '31092193', 'The complexity of representing biological systems is compounded by an ever-expanding body of knowledge emerging from multi-omics experiments. A number of pathway databases have facilitated pathway-centric approaches that assist in the interpretation of molecular signatures yielded by these experiments. However, the lack of interoperability between pathway databases has hindered the ability to harmonize these resources and to exploit their consolidated knowledge. Such a unification of pathway knowledge is imperative in enhancing the comprehension and modeling of biological abstractions.'),
(99003, 2019, 'bmc', 'rstoolbox - a Python library for large-scale analysis of computational protein design data and structural bioinformatics', 'Bonet, Jaume|Harteveld, Zander|Sesterhenn, Fabian|Scheck, Andreas|Correia, Bruno E', 'Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, CH-1015, Lausanne, Switzerland.|Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, CH-1015, Lausanne, Switzerland.|Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, CH-1015, Lausanne, Switzerland.|Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, CH-1015, Lausanne, Switzerland.|Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, CH-1015, Lausanne, Switzerland. bruno.correia@epfl.ch.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 0, 'Amino Acid Sequence|Computational Biology|Computing Methodologies|Proteins|Reproducibility of Results|Software', 'Journal Article', '10.1186/s12859-019-2796-3', '31092198', 'Large-scale datasets of protein structures and sequences are becoming ubiquitous in many domains of biological research. Experimental approaches and computational modelling methods are generating biological data at an unprecedented rate. The detailed analysis of structure-sequence relationships is critical to unveil governing principles of protein folding, stability and function. Computational protein design (CPD) has emerged as an important structure-based approach to engineer proteins for novel functions. Generally, CPD workflows rely on the generation of large numbers of structural models to search for the optimal structure-sequence configurations. As such, an important step of the CPD process is the selection of a small subset of sequences to be experimentally characterized. Given the limitations of current CPD scoring functions, multi-step design protocols and elaborated analysis of the decoy populations have become essential for the selection of sequences for experimental characterization and the success of CPD strategies.'),
(99004, 2019, 'bmc', 'Correction to: Similarities and differences between variants called with human reference genome HG19 or HG38', 'Pan, Bohu|Kusko, Rebecca|Xiao, Wenming|Zheng, Yuanting|Liu, Zhichao|Xiao, Chunlin|Sakkiah, Sugunadevi|Guo, Wenjing|Gong, Ping|Zhang, Chaoyang|Ge, Weigong|Shi, Leming|Tong, Weida|Hong, Huixiao', 'Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA.|Immuneering Corporation, Cambridge, MA, 02142, USA.|Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA.|Center for Pharmacogenomics, Fudan University, Shanghai, China.|Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA.|National Center for Biotechnological Information, National Institutes of Health, Bethesda, MD, 20894, USA.|Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA.|Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA.|Environmental Laboratory, US Army Engineer Research and Development Center, Vicksburg, MS, 39180, USA.|School of Computing, The University of Southern Mississippi, Hattiesburg, MS, 39406, USA.|Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA.|Center for Pharmacogenomics, Fudan University, Shanghai, China.|Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA.|Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA. huixiao.hong@fda.hhs.gov.', 'United States|United States|United States|China|United States|United States|United States|United States|United States|United States|United States|China|United States|United States', 0, '-', 'Published Erratum', '10.1186/s12859-019-2776-7', '31092200', 'After publication of this supplement article.'),
(99005, 2019, 'bmc', 'HOME: a histogram based machine learning approach for effective identification of differentially methylated regions', 'Srivastava, Akanksha|Karpievitch, Yuliya V|Eichten, Steven R|Borevitz, Justin O|Lister, Ryan', 'ARC Centre of Excellence in Plant Energy Biology, The University of Western Australia, Perth, Australia.|ARC Centre of Excellence in Plant Energy Biology, The University of Western Australia, Perth, Australia.|ARC Centre of Excellence in Plant Energy Biology, The Australian National University, Canberra, Australia.|ARC Centre of Excellence in Plant Energy Biology, The Australian National University, Canberra, Australia.|ARC Centre of Excellence in Plant Energy Biology, The University of Western Australia, Perth, Australia. ryan.lister@uwa.edu.au.', 'Australia|Australia|Australia|Australia|Australia', 6, 'Algorithms|Animals|Computer Simulation|DNA Methylation|Databases, Genetic|Machine Learning|Mice|Molecular Sequence Annotation|Time Factors', 'Journal Article', '10.1186/s12859-019-2845-y', '31096906', 'The development of whole genome bisulfite sequencing has made it possible to identify methylation differences at single base resolution throughout an entire genome. However, a persistent challenge in DNA methylome analysis is the accurate identification of differentially methylated regions (DMRs) between samples. Sensitive and specific identification of DMRs among different conditions requires accurate and efficient algorithms, and while various tools have been developed to tackle this problem, they frequently suffer from inaccurate DMR boundary identification and high false positive rate.'),
(99006, 2019, 'bmc', 'GenePy - a score for estimating gene pathogenicity in individuals using next-generation sequencing data', 'Mossotto, E|Ashton, J J|OGorman, L|Pengelly, R J|Beattie, R M|MacArthur, B D|Ennis, S', 'Department of Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK. Enrico.Mossotto@soton.ac.uk.|Department of Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK.|Department of Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK.|Department of Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK.|Department of Paediatric Gastroenterology, Southampton Childrens Hospital, Southampton, UK.|Institute for Life Sciences, University of Southampton, Southampton, UK.|Department of Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 1, 'Alleles|Cohort Studies|Databases, Genetic|Exome|Gene Frequency|High-Throughput Nucleotide Sequencing|Humans|Phenotype|Polymorphism, Single Nucleotide|Software|Virulence|Whole Exome Sequencing|Zygote', 'Journal Article', '10.1186/s12859-019-2877-3', '31096927', 'Next-generation sequencing is revolutionising diagnosis and treatment of rare diseases, however its application to understanding common disease aetiology is limited. Rare disease applications binarily attribute genetic change(s) at a single locus to a specific phenotype. In common diseases, where multiple genetic variants within and across genes contribute to disease, binary modelling cannot capture the burden of pathogenicity harboured by an individual across a given gene/pathway. We present GenePy, a novel gene-level scoring system for integration and analysis of next-generation sequencing data on a per-individual basis that transforms NGS data interpretation from variant-level to gene-level. This simple and flexible scoring system is intuitive and amenable to integration for machine learning, network and topological approaches, facilitating the investigation of complex phenotypes.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(99007, 2019, 'bmc', 'Bioinformatics Resource Manager: a systems biology web tool for microRNA and omics data integration', 'Brown, Joseph|Phillips, Aaron R|Lewis, David A|Mans, Michael-Andres|Chang, Yvonne|Tanguay, Robert L|Peterson, Elena S|Waters, Katrina M|Tilton, Susan C', 'Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA.|Computing & Analytics Division, Pacific Northwest National Laboratory, Richland, WA, USA.|Computing & Analytics Division, Pacific Northwest National Laboratory, Richland, WA, USA.|Environmental and Molecular Toxicology Department, Oregon State University, Corvallis, OR, USA.|Environmental and Molecular Toxicology Department, Oregon State University, Corvallis, OR, USA.|Environmental and Molecular Toxicology Department, Oregon State University, Corvallis, OR, USA.|Computing & Analytics Division, Pacific Northwest National Laboratory, Richland, WA, USA.|Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA. katrina.waters@pnnl.gov.|Environmental and Molecular Toxicology Department, Oregon State University, Corvallis, OR, USA. susan.tilton@oregonstate.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 1, 'Animals|Base Sequence|Computational Biology|Genomics|Humans|Internet|Macaca mulatta|Mice|MicroRNAs|Proteomics|RNA, Messenger|Rats|Search Engine|Sequence Analysis, RNA|Species Specificity|Systems Biology|Zebrafish', 'Journal Article', '10.1186/s12859-019-2805-6', '31101000', 'The Bioinformatics Resource Manager (BRM) is a web-based tool developed to facilitate identifier conversion and data integration for Homo sapiens (human), Mus musculus (mouse), Rattus norvegicus (rat), Danio rerio (zebrafish), and Macaca mulatta (macaque), as well as perform orthologous conversions among the supported species. In addition to providing a robust means of identifier conversion, BRM also incorporates a suite of microRNA (miRNA)-target databases upon which to query target genes or to perform reverse target lookups using gene identifiers.'),
(99008, 2019, 'bmc', 'ROIMCR: a powerful analysis strategy for LC-MS metabolomic datasets', 'Gorrochategui, Eva|Jaumot, Joaquim|Tauler, Romà', 'Department of Environmental Chemistry, Institute of Environmental Assessment and Water Research (IDAEA), Consejo Superior de Investigaciones Científicas (CSIC), Jorsi Girona 18-25, Barcelona, 08034, Catalonia, Spain.|Department of Environmental Chemistry, Institute of Environmental Assessment and Water Research (IDAEA), Consejo Superior de Investigaciones Científicas (CSIC), Jorsi Girona 18-25, Barcelona, 08034, Catalonia, Spain.|Department of Environmental Chemistry, Institute of Environmental Assessment and Water Research (IDAEA), Consejo Superior de Investigaciones Científicas (CSIC), Jorsi Girona 18-25, Barcelona, 08034, Catalonia, Spain. roma.tauler@idaea.csic.es.', 'Spain|Spain|Spain', 0, 'Biomarkers|Chromatography, Liquid|Databases as Topic|Least-Squares Analysis|Metabolomics|Multivariate Analysis|Software|Tandem Mass Spectrometry', 'Journal Article', '10.1186/s12859-019-2848-8', '31101001', 'The analysis of LC-MS metabolomic datasets appears to be a challenging task in a wide range of disciplines since it demands the highly extensive processing of a vast amount of data. Different LC-MS data analysis packages have been developed in the last few years to facilitate this analysis. However, most of these strategies involve chromatographic alignment and peak shaping and often associate each feature (i.e., chromatographic peak) with a unique m/z measurement. Thus, the development of an alternative data analysis strategy that is applicable to most types of MS datasets and properly addresses these issues is still a challenge in the metabolomics field.'),
(99009, 2019, 'bmc', 'Improving the power of gene set enrichment analyses', 'Roder, Joanna|Linstid, Benjamin|Oliveira, Carlos', 'Biodesix Inc, 2970 Wilderness Pl, Ste100, Boulder, CO, 80301, USA. joanna.roder@biodesix.com.|Biodesix Inc, 2970 Wilderness Pl, Ste100, Boulder, CO, 80301, USA.|Biodesix Inc, 2970 Wilderness Pl, Ste100, Boulder, CO, 80301, USA.', 'United States|United States|United States', 0, 'Breast Neoplasms|Cohort Studies|Databases as Topic|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Phenotype|RNA, Messenger|Sample Size', 'Journal Article', '10.1186/s12859-019-2850-1', '31101008', 'Set enrichment methods are commonly used to analyze high-dimensional molecular data and gain biological insight into molecular or clinical phenotypes. One important category of analysis methods employs an enrichment score, which is created from ranked univariate correlations between phenotype and each molecular attribute. Estimates of the significance of the associations are determined via a null distribution generated from phenotype permutation. We investigate some statistical properties of this method and demonstrate how alternative assessments of enrichment can be used to increase the statistical power of such analyses to detect associations between phenotype and biological processes and pathways.'),
(99010, 2019, 'bmc', 'Weekly ILI patient ratio change prediction using news articles with support vector machine', 'Kim, Juhyeon|Ahn, Insung', 'Department of data-centric problem solving research, Korea Institute of Science and Technology Information, Yuseong-gu, Daejeon, Korea.|Department of data-centric problem solving research, Korea Institute of Science and Technology Information, Yuseong-gu, Daejeon, Korea. isahn@kisti.re.kr.', '-|-', 2, 'Epidemics|Humans|Influenza, Human|Internet|Publications|Support Vector Machine', 'Journal Article', '10.1186/s12859-019-2894-2', '31109286', 'Influenza continues to pose a serious threat to human health worldwide. For this reason, detecting influenza infection patterns is critical. However, as the epidemic spread of influenza occurs sporadically and rapidly, it is not easy to estimate the future variance of influenza virus infection. Furthermore, accumulating influenza related data is not easy, because the type of data that is associated with influenza is very limited. For these reasons, identifying useful data and building a prediction model with these data are necessary steps toward predicting if the number of patients will increase or decrease. On the Internet, numerous press releases are published every day that reflect currently pending issues.'),
(99011, 2019, 'bmc', 'Quantification of experimentally induced nucleotide conversions in high-throughput sequencing datasets', 'Neumann, Tobias|Herzog, Veronika A|Muhar, Matthias|von Haeseler, Arndt|Zuber, Johannes|Ameres, Stefan L|Rescheneder, Philipp', 'Research Institute of Molecular Pathology (IMP), Campus-Vienna-Biocenter 1, Vienna BioCenter (VBC), 1030, Vienna, Austria. tobias.neumann@imp.ac.at.|Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Dr. Bohr-Gasse 3, VBC, 1030, Vienna, Austria.|Research Institute of Molecular Pathology (IMP), Campus-Vienna-Biocenter 1, Vienna BioCenter (VBC), 1030, Vienna, Austria.|Center for Integrative Bioinformatics Vienna, Max F. Perutz Laboratories, University of Vienna, Medical University of Vienna, Dr. Bohrgasse 9, VBC, 1030, Vienna, Austria.|Research Institute of Molecular Pathology (IMP), Campus-Vienna-Biocenter 1, Vienna BioCenter (VBC), 1030, Vienna, Austria.|Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Dr. Bohr-Gasse 3, VBC, 1030, Vienna, Austria.|Center for Integrative Bioinformatics Vienna, Max F. Perutz Laboratories, University of Vienna, Medical University of Vienna, Dr. Bohrgasse 9, VBC, 1030, Vienna, Austria. philipp.rescheneder@gmail.com.', 'Austria|Austria|Austria|Austria|Austria|Austria|Austria', 9, 'High-Throughput Nucleotide Sequencing|Nucleotides|Polymorphism, Single Nucleotide|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/s12859-019-2849-7', '31109287', 'Methods to read out naturally occurring or experimentally introduced nucleic acid modifications are emerging as powerful tools to study dynamic cellular processes. The recovery, quantification and interpretation of such events in high-throughput sequencing datasets demands specialized bioinformatics approaches.'),
(99012, 2019, 'bmc', 'Pairwise alignment of nucleotide sequences using maximal exact matches', 'Bayat, Arash|Gaëta, Bruno|Ignjatovic, Aleksandar|Parameswaran, Sri', 'School of Computer Science and Engineering, University of New South Wales (UNSW), Sydney, 2052, Australia. a.bayat@unsw.edu.au.|School of Computer Science and Engineering, University of New South Wales (UNSW), Sydney, 2052, Australia.|School of Computer Science and Engineering, University of New South Wales (UNSW), Sydney, 2052, Australia.|School of Computer Science and Engineering, University of New South Wales (UNSW), Sydney, 2052, Australia.', 'Australia|Australia|Australia|Australia', 0, 'Algorithms|Nucleotides|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-019-2827-0', '31113356', 'Pairwise alignment of short DNA sequences with affine-gap scoring is a common processing step performed in a range of bioinformatics analyses. Dynamic programming (i.e. Smith-Waterman algorithm) is widely used for this purpose. Despite using data level parallelisation, pairwise alignment consumes much time. There are faster alignment algorithms but they suffer from the lack of accuracy.'),
(99013, 2019, 'bmc', 'Knowledge-guided convolutional networks for chemical-disease relation extraction', 'Zhou, Huiwei|Lang, Chengkun|Liu, Zhuang|Ning, Shixian|Lin, Yingyu|Du, Lei', 'School of Computer Science and Technology, Dalian University of Technology, Chuangxinyuan Building, No.2 Linggong Road, Ganjingzi District, Dalian, 116024, Liaoning, China. zhouhuiwei@dlut.edu.cn.|School of Computer Science and Technology, Dalian University of Technology, Chuangxinyuan Building, No.2 Linggong Road, Ganjingzi District, Dalian, 116024, Liaoning, China.|School of Computer Science and Technology, Dalian University of Technology, Chuangxinyuan Building, No.2 Linggong Road, Ganjingzi District, Dalian, 116024, Liaoning, China.|School of Computer Science and Technology, Dalian University of Technology, Chuangxinyuan Building, No.2 Linggong Road, Ganjingzi District, Dalian, 116024, Liaoning, China.|School of Foreign Languages, Dalian University of Technology, Arts Building, No.2 Linggong Road, Ganjingzi District, Dalian, 116024, Liaoning, China.|School of Mathematical Sciences, Dalian University of Technology, Chuangxinyuan Building, No.2 Linggong Road, Ganjingzi District, Dalian, 116024, Liaoning, China.', 'China|China|China|China|China|China', 0, 'Data Mining|Disease|Drug-Related Side Effects and Adverse Reactions|Humans|Knowledge Bases', 'Journal Article', '10.1186/s12859-019-2873-7', '31113357', 'Automatic extraction of chemical-disease relations (CDR) from unstructured text is of essential importance for disease treatment and drug development. Meanwhile, biomedical experts have built many highly-structured knowledge bases (KBs), which contain prior knowledge about chemicals and diseases. Prior knowledge provides strong support for CDR extraction. How to make full use of it is worth studying.'),
(99014, 2019, 'bmc', 'DRfit: a Java tool for the analysis of discrete data from multi-well plate assays', 'Hofmann, Andreas|Preston, Sarah|Cross, Megan|Herath, H M P Dilrukshi|Simon, Anne|Gasser, Robin B', 'Griffith Institute for Drug Discovery, Griffith University, Nathan, Queensland, 4111, Australia. a.hofmann@griffith.edu.au.|Faculty of Science and Technology, Federation University, Ballarat, Victoria, 3350, Australia.|Griffith Institute for Drug Discovery, Griffith University, Nathan, Queensland, 4111, Australia.|Department of Veterinary Biosciences, Melbourne Veterinary School, The University of Melbourne, Parkville, Victoria, 3010, Australia.|Université Claude Bernard Lyon 1, Bâtiment Curien, Villeurbanne and Laboratoire Chimie et Biologie des Membranes et des Nanoobjets, Université de Bordeaux, Pessac, France.|Department of Veterinary Biosciences, Melbourne Veterinary School, The University of Melbourne, Parkville, Victoria, 3010, Australia.', 'Australia|Australia|Australia|Australia|France|Australia', 1, 'Biological Science Disciplines|Data Interpretation, Statistical|Software|User-Computer Interface', 'Journal Article', '10.1186/s12859-019-2891-5', '31113359', 'Analyses of replicates in sets of discrete data, typically acquired in multi-well plate formats, is a recurring task in many contemporary areas in the Life Sciences. The availability of accessible cross-platform data analysis tools for such fundamental tasks in varied projects and environments is an important prerequisite to ensuring a reliable and timely turnaround as well as to provide practical analytical tools for student training.'),
(99015, 2019, 'bmc', 'Solr-Plant: efficient extraction of plant names from text', 'Sharma, Vivekanand|Restrepo, Maria Isabel|Sarkar, Indra Neil', 'Center for Biomedical Informatics, Brown University, Box G-R, Providence, RI, USA.|Center for Biomedical Informatics, Brown University, Box G-R, Providence, RI, USA.|Center for Biomedical Informatics, Brown University, Box G-R, Providence, RI, USA. neil_sarkar@brown.edu.', 'United States|United States|United States', 2, 'Algorithms|Data Mining|Databases as Topic|Plants|Terminology as Topic', 'Journal Article', '10.1186/s12859-019-2874-6', '31117932', 'The retrieval of plant-related information is a challenging task due to variations in species name mentions as well as spelling or typographical errors across data sources. Scalable solutions are needed for identifying plant name mentions from text and resolving them to accepted taxonomic names.'),
(99016, 2019, 'bmc', 'NeoPredPipe: high-throughput neoantigen prediction and recognition potential pipeline', 'Schenck, Ryan O|Lakatos, Eszter|Gatenbee, Chandler|Graham, Trevor A|Anderson, Alexander R A', 'Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, FL, 33612, USA. ryan.schenck@univ.ox.ac.uk.|Evolution and Cancer Laboratory, Barts Cancer Institute, Queen Mary University of London, London, EC1M, UK.|Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, FL, 33612, USA.|Evolution and Cancer Laboratory, Barts Cancer Institute, Queen Mary University of London, London, EC1M, UK.|Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, FL, 33612, USA.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 11, 'Antigens, Neoplasm|Colorectal Neoplasms|High-Throughput Nucleotide Sequencing|Humans|Software', 'Journal Article', '10.1186/s12859-019-2876-4', '31117948', 'Next generation sequencing has yielded an unparalleled means of quickly determining the molecular make-up of patient tumors. In conjunction with emerging, effective immunotherapeutics for a number of cancers, this rapid data generation necessitates a paired high-throughput means of predicting and assessing neoantigens from tumor variants that may stimulate immune response.'),
(99017, 2019, 'bmc', 'Rare variant phasing using paired tumor:normal sequence data', 'Buckley, Alexandra R|Ideker, Trey|Carter, Hannah|Schork, Nicholas J', 'Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla, CA, USA.|Division of Medical Genetics, Department of Medicine, University of California San Diego, La Jolla, CA, USA.|Division of Medical Genetics, Department of Medicine, University of California San Diego, La Jolla, CA, USA.|Human Biology Program, J. Craig Venter Institute, La Jolla, CA, USA. nschork@jcvi.org.', 'United States|United States|United States|United States', 0, 'Base Sequence|DNA Copy Number Variations|Gene Frequency|Genetic Predisposition to Disease|Genetic Variation|Haplotypes|Humans|Neoplasms|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-019-2753-1', '31132991', 'In standard high throughput sequencing analysis, genetic variants are not assigned to a homologous chromosome of origin. This process, called haplotype phasing, can reveal information important for understanding the relationship between genetic variants and biological phenotypes. For example, in genes that carry multiple heterozygous missense variants, phasing resolves whether one or both gene copies are altered. Here, we present a novel approach to phasing variants that takes advantage of unique properties of paired tumor:normal sequencing data from cancer studies.'),
(99018, 2019, 'bmc', 'Capturing the differences between humoral immunity in the normal and tumor environments from repertoire-seq of B-cell receptors using supervised machine learning', 'Konishi, Hiroki|Komura, Daisuke|Katoh, Hiroto|Atsumi, Shinichiro|Koda, Hirotomo|Yamamoto, Asami|Seto, Yasuyuki|Fukayama, Masashi|Yamaguchi, Rui|Imoto, Seiya|Ishikawa, Shumpei', 'Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-0033, Japan.|Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-0033, Japan.|Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-0033, Japan.|Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-0033, Japan.|Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-0033, Japan.|Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-0033, Japan.|Department of Gastrointestinal Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-0033, Japan.|Department of Pathology, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-0033, Japan.|Institute of Medical Science, Human Genome Center, The University of Tokyo, Tokyo, 108-8639, Japan.|Institute of Medical Science, Health Intelligence Center, The University of Tokyo, Tokyo, 108-8639, Japan.|Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-0033, Japan. ishum-prm@m.u-tokyo.ac.jp.', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan', 3, 'Algorithms|Amino Acid Motifs|Amino Acid Sequence|Area Under Curve|Complementarity Determining Regions|Databases, Genetic|High-Throughput Nucleotide Sequencing|Humans|Immunity, Humoral|Immunoglobulins|ROC Curve|Receptors, Antigen, B-Cell|Supervised Machine Learning|Tumor Microenvironment', 'Journal Article', '10.1186/s12859-019-2853-y', '31138102', 'The recent success of immunotherapy in treating tumors has attracted increasing interest in research related to the adaptive immune system in the tumor microenvironment. Recent advances in next-generation sequencing technology enabled the sequencing of whole T-cell receptors (TCRs) and B-cell receptors (BCRs)/immunoglobulins (Igs) in the tumor microenvironment. Since BCRs/Igs in tumor tissues have high affinities for tumor-specific antigens, the patterns of their amino acid sequences and other sequence-independent features such as the number of somatic hypermutations (SHMs) may differ between the normal and tumor microenvironments. However, given the high diversity of BCRs/Igs and the rarity of recurrent sequences among individuals, it is far more difficult to capture such differences in BCR/Ig sequences than in TCR sequences. The aim of this study was to explore the possibility of discriminating BCRs/Igs in tumor and in normal tissues, by capturing these differences using supervised machine learning methods applied to RNA sequences of BCRs/Igs.'),
(99019, 2019, 'bmc', 'Drug repositioning of herbal compounds via a machine-learning approach', 'Kim, Eunyoung|Choi, A-Sol|Nam, Hojung', 'School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Buk-gu, Gwangju, 61005, Republic of Korea.|School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Buk-gu, Gwangju, 61005, Republic of Korea.|School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Buk-gu, Gwangju, 61005, Republic of Korea. hjnam@gist.ac.kr.', '-|-|-', 5, 'Algorithms|Drug Repositioning|Gene Ontology|Humans|Logistic Models|Machine Learning|Models, Biological|Pharmaceutical Preparations|Phenotype|Phytochemicals|Reproducibility of Results', 'Journal Article', '10.1186/s12859-019-2811-8', '31138103', 'Drug repositioning, also known as drug repurposing, defines new indications for existing drugs and can be used as an alternative to drug development. In recent years, the accumulation of large volumes of information related to drugs and diseases has led to the development of various computational approaches for drug repositioning. Although herbal medicines have had a great impact on current drug discovery, there are still a large number of herbal compounds that have no definite indications.'),
(99020, 2019, 'bmc', 'Computational determination of hERG-related cardiotoxicity of drug candidates', 'Lee, Hyang-Mi|Yu, Myeong-Sang|Kazmi, Sayada Reemsha|Oh, Seong Yun|Rhee, Ki-Hyeong|Bae, Myung-Ae|Lee, Byung Ho|Shin, Dae-Seop|Oh, Kwang-Seok|Ceong, Hyithaek|Lee, Donghyun|Na, Dokyun', 'School of integrative engineering, Chung-Ang University, Seoul, Republic of Korea.|School of integrative engineering, Chung-Ang University, Seoul, Republic of Korea.|School of integrative engineering, Chung-Ang University, Seoul, Republic of Korea.|School of integrative engineering, Chung-Ang University, Seoul, Republic of Korea.|College of Industrial Sciences, Kongju National University, Yesan, Republic of Korea.|Bio-based Technology Research Center, Korea Research Institute of Chemical Technology, 141 Gajeong-ro, Yuseong, Daejeon, 34114, Republic of Korea.|Information-based Drug Research Center, Korea Research Institute of Chemical Technology, 141 Gajeong-ro, Yuseong, Daejeon, 34114, Republic of Korea.|Bio-based Technology Research Center, Korea Research Institute of Chemical Technology, 141 Gajeong-ro, Yuseong, Daejeon, 34114, Republic of Korea.|Information-based Drug Research Center, Korea Research Institute of Chemical Technology, 141 Gajeong-ro, Yuseong, Daejeon, 34114, Republic of Korea.|Department of Multimedia, Chonnam National University, Yeosu, Republic of Korea.|School of integrative engineering, Chung-Ang University, Seoul, Republic of Korea. dhlee@cau.ac.kr.|School of integrative engineering, Chung-Ang University, Seoul, Republic of Korea. blisszen@cau.ac.kr.', '-|-|-|-|-|-|-|-|-|-|-|-', 4, 'Animals|Area Under Curve|Cardiotoxicity|Databases, Genetic|Ether-A-Go-Go Potassium Channels|Guinea Pigs|Humans|Machine Learning|Neural Networks (Computer)|ROC Curve', 'Journal Article', '10.1186/s12859-019-2814-5', '31138104', 'Drug candidates often cause an unwanted blockage of the potassium ion channel of the human ether-a-go-go-related gene (hERG). The blockage leads to long QT syndrome (LQTS), which is a severe life-threatening cardiac side effect. Therefore, a virtual screening method to predict drug-induced hERG-related cardiotoxicity could facilitate drug discovery by filtering out toxic drug candidates.'),
(99021, 2019, 'bmc', 'Is automatic detection of hidden knowledge an anomaly?', 'Preiss, Judita', 'University of Salford, The School of Computing, Science & Engineering, Newton Building, Salford, Greater Manchester, M5 4WT, UK. J.Preiss@salford.ac.uk.', 'United Kingdom', 0, 'Algorithms|Automation|Humans|Knowledge|Literature Based Discovery|Publications|Semantics', 'Journal Article', '10.1186/s12859-019-2815-4', '31138105', 'The quantity of documents being published requires researchers to specialize to a narrower field, meaning that inferable connections between publications (particularly from different domains) can be missed. This has given rise to automatic literature based discovery (LBD). However, unless heavily filtered, LBD generates more potential new knowledge than can be manually verified and another form of selection is required before the results can be passed onto a user. Since a large proportion of the automatically generated hidden knowledge is valid but generally known, we investigate the hypothesis that non trivial, interesting, hidden knowledge can be treated as an anomaly and identified using anomaly detection approaches.'),
(99022, 2019, 'bmc', 'MHCSeqNet: a deep neural network model for universal MHC binding prediction', 'Phloyphisut, Poomarin|Pornputtapong, Natapol|Sriswasdi, Sira|Chuangsuwanich, Ekapol', 'Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, 254 Phayathai Road, Pathumwan, Bangkok, 10330, Thailand.|Department of Biochemistry and Microbiology, Faculty of Pharmaceutical Sciences, Chulalongkorn University, 254 Phayathai Road, Pathumwan, Bangkok, 10330, Thailand. natapol.p@chula.ac.th.|Computational Molecular Biology Group, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand. sira.sr@chula.ac.th.|Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, 254 Phayathai Road, Pathumwan, Bangkok, 10330, Thailand. ekapol.c@chula.ac.th.', 'Thailand|Thailand|Thailand|Thailand', 6, 'Alleles|Animals|Area Under Curve|Databases, Protein|Histocompatibility Antigens Class I|Humans|Mice|Models, Biological|Neural Networks (Computer)|Peptides|Protein Binding|Proteome|Software', 'Journal Article', '10.1186/s12859-019-2892-4', '31138107', 'Immunotherapy is an emerging approach in cancer treatment that activates the host immune system to destroy cancer cells expressing unique peptide signatures (neoepitopes). Administrations of cancer-specific neoepitopes in the form of synthetic peptide vaccine have been proven effective in both mouse models and human patients. Because only a tiny fraction of cancer-specific neoepitopes actually elicits immune response, selection of potent, immunogenic neoepitopes remains a challenging step in cancer vaccine development. A basic approach for immunogenicity prediction is based on the premise that effective neoepitope should bind with the Major Histocompatibility Complex (MHC) with high affinity.'),
(99023, 2019, 'bmc', 'Comparison of kNN and k-means optimization methods of reference set selection for improved CNV callers performance', 'Kuxc5x9bmirek, Wiktor|Szmurxc5x82o, Agnieszka|Wiewixc3xb3rka, Marek|Nowak, Robert|Gambin, Tomasz', 'Institute of Computer Science, Warsaw University of Technology, ul. Nowowiejska 15/19, Warsaw, 00-665, Poland.|Institute of Computer Science, Warsaw University of Technology, ul. Nowowiejska 15/19, Warsaw, 00-665, Poland.|Institute of Computer Science, Warsaw University of Technology, ul. Nowowiejska 15/19, Warsaw, 00-665, Poland.|Institute of Computer Science, Warsaw University of Technology, ul. Nowowiejska 15/19, Warsaw, 00-665, Poland.|Institute of Computer Science, Warsaw University of Technology, ul. Nowowiejska 15/19, Warsaw, 00-665, Poland. T.Gambin@ii.pw.edu.pl.', 'Poland|Poland|Poland|Poland|Poland', 3, 'Algorithms|Benchmarking|DNA Copy Number Variations|Databases, Genetic|Female|Humans|Male|Reference Standards|Sample Size', 'Comparative Study', '10.1186/s12859-019-2889-z', '31138108', 'There are over 25 tools dedicated for the detection of Copy Number Variants (CNVs) using Whole Exome Sequencing (WES) data based on read depth analysis. The tools reported consist of several steps, including: (i) calculation of read depth for each sequencing target, (ii) normalization, (iii) segmentation and (iv) actual CNV calling. The essential aspect of the entire process is the normalization stage, in which systematic errors and biases are removed and the reference sample set is used to increase the signal-to-noise ratio. Although some CNV calling tools use dedicated algorithms to obtain the optimal reference sample set, most of the advanced CNV callers do not include this feature. To our knowledge, this work is the first attempt to assess the impact of reference sample set selection on CNV detection performance.'),
(99024, 2019, 'bmc', 'CollaboNet: collaboration of deep neural networks for biomedical named entity recognition', 'Yoon, Wonjin|So, Chan Ho|Lee, Jinhyuk|Kang, Jaewoo', 'Department of Computer Science and Engineering, Korea University, Seoul, 02841, Republic of Korea.|Interdisciplinary Graduate Program in Bioinformatics, Korea University, Seoul, 02841, Republic of Korea.|Department of Computer Science and Engineering, Korea University, Seoul, 02841, Republic of Korea.|Department of Computer Science and Engineering, Korea University, Seoul, 02841, Republic of Korea. kangj@korea.ac.kr.', '-|-|-|-', 15, 'Animals|Data Mining|Deep Learning|Humans|Mice|Models, Theoretical|Neural Networks (Computer)', 'Journal Article', '10.1186/s12859-019-2813-6', '31138109', 'Finding biomedical named entities is one of the most essential tasks in biomedical text mining. Recently, deep learning-based approaches have been applied to biomedical named entity recognition (BioNER) and showed promising results. However, as deep learning approaches need an abundant amount of training data, a lack of data can hinder performance. BioNER datasets are scarce resources and each dataset covers only a small subset of entity types. Furthermore, many bio entities are polysemous, which is one of the major obstacles in named entity recognition.'),
(99025, 2019, 'bmc', 'Robust identification of molecular phenotypes using semi-supervised learning', 'Roder, Heinrich|Oliveira, Carlos|Net, Lelia|Linstid, Benjamin|Tsypin, Maxim|Roder, Joanna', 'Biodesix Inc, 2970 Wilderness Pl, Ste100, Boulder, CO, 80301, USA.|Biodesix Inc, 2970 Wilderness Pl, Ste100, Boulder, CO, 80301, USA.|Biodesix Inc, 2970 Wilderness Pl, Ste100, Boulder, CO, 80301, USA.|Biodesix Inc, 2970 Wilderness Pl, Ste100, Boulder, CO, 80301, USA.|Biodesix Inc, 2970 Wilderness Pl, Ste100, Boulder, CO, 80301, USA.|Biodesix Inc, 2970 Wilderness Pl, Ste100, Boulder, CO, 80301, USA. joanna.roder@biodesix.com.', 'United States|United States|United States|United States|United States|United States', 3, 'Algorithms|Breast Neoplasms|Chemotherapy, Adjuvant|Databases as Topic|Disease-Free Survival|Female|Gene Expression Regulation, Neoplastic|Humans|Lymphoma|Phenotype|Prognosis|RNA, Messenger|Supervised Machine Learning', 'Journal Article', '10.1186/s12859-019-2885-3', '31138112', 'Modern molecular profiling techniques are yielding vast amounts of data from patient samples that could be utilized with machine learning methods to provide important biological insights and improvements in patient outcomes. Unsupervised methods have been successfully used to identify molecularly-defined disease subtypes. However, these approaches do not take advantage of potential additional clinical outcome information. Supervised methods can be implemented when training classes are apparent (e.g., responders or non-responders to treatment). However, training classes can be difficult to define when assessing relative benefit of one therapy over another using gold standard clinical endpoints, since it is often not clear how much benefit each individual patient receives.'),
(99026, 2019, 'bmc', 'TelomereHunter - in silico estimation of telomere content and composition from cancer genomes', 'Feuerbach, Lars|Sieverling, Lina|Deeg, Katharina I|Ginsbach, Philip|Hutter, Barbara|Buchhalter, Ivo|Northcott, Paul A|Mughal, Sadaf S|Chudasama, Priya|Glimm, Hanno|Scholl, Claudia|Lichter, Peter|Frxc3xb6hling, Stefan|Pfister, Stefan M|Jones, David T W|Rippe, Karsten|Brors, Benedikt', 'Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany. l.feuerbach@dkfz-heidelberg.de.|Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany.|Research Group Genome Organization & Function/Division of Chromatin Networks, German Cancer Research Center (DKFZ) and BioQuant Center, 69120, Heidelberg, Germany.|Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany.|Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany.|Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany.|Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany.|Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany.|Division of Translational Medical Oncology, National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany.|Translational Functional Cancer Genomics, National Center for Tumor Diseases (NCT) and German Cancer Research Center (DKFZ), Heidelberg, Germany.|Division of Translational Medical Oncology, National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany.|German Cancer Consortium (DKTK), 69120, Heidelberg, Germany.|Division of Translational Medical Oncology, National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany.|Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany.|German Cancer Consortium (DKTK), 69120, Heidelberg, Germany.|Research Group Genome Organization & Function/Division of Chromatin Networks, German Cancer Research Center (DKFZ) and BioQuant Center, 69120, Heidelberg, Germany.|Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germ', 2, 'Base Sequence|Computer Simulation|Genome|Glioblastoma|High-Throughput Nucleotide Sequencing|Humans|Medulloblastoma|Neoplasms|Software|Telomere|Whole Exome Sequencing|Whole Genome Sequencing', 'Journal Article', '10.1186/s12859-019-2851-0', '31138115', 'Establishment of telomere maintenance mechanisms is a universal step in tumor development to achieve replicative immortality. These processes leave molecular footprints in cancer genomes in the form of altered telomere content and aberrations in telomere composition. To retrieve these telomere characteristics from high-throughput sequencing data the available computational approaches need to be extended and optimized to fully exploit the information provided by large scale cancer genome data sets.'),
(99027, 2019, 'bmc', 'Multi-domain semantic similarity in biomedical research', 'Ferreira, João D|Couto, Francisco M', 'LASIGE, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal. jdferreira@fc.ul.pt.|LASIGE, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal.', 'Portugal|Portugal', 1, 'Biomedical Research|Epidemics|Humans|Semantics', 'Journal Article', '10.1186/s12859-019-2810-9', '31138117', 'Given the increasing amount of biomedical resources that are being annotated with concepts from more than one ontology and covering multiple domains of knowledge, it is important to devise mechanisms to compare these resources that take into account the various domains of annotation. For example, metabolic pathways are annotated with their enzymes and their metabolites, and thus similarity measures should compare them with respect to both of those domains simultaneously.'),
(99028, 2019, 'bmc', 'Conventionally used reference genes are not outstanding for normalization of gene expression in human cancer research', 'Jo, Jihoon|Choi, Sunkyung|Oh, Jooseong|Lee, Sung-Gwon|Choi, Song Yi|Kim, Kee K|Park, Chungoo', 'School of Biological Sciences and Technology, Chonnam National University, 77 Yongbong-Ro, Buk-Ku, GwangJu, 61186, Republic of Korea.|Department of Biochemistry, Chungnam National University, 99 Daehak-Ro, Yuseong-Ku, Daejeon, 34134, Republic of Korea.|School of Biological Sciences and Technology, Chonnam National University, 77 Yongbong-Ro, Buk-Ku, GwangJu, 61186, Republic of Korea.|School of Biological Sciences and Technology, Chonnam National University, 77 Yongbong-Ro, Buk-Ku, GwangJu, 61186, Republic of Korea.|Department of Pathology, Chungnam National University, 282 Munhwa-Ro, Jung-Ku, Daejeon, 35015, Republic of Korea. sychoi@cnu.ac.kr.|Department of Biochemistry, Chungnam National University, 99 Daehak-Ro, Yuseong-Ku, Daejeon, 34134, Republic of Korea. kimkk@cnu.ac.kr.|School of Biological Sciences and Technology, Chonnam National University, 77 Yongbong-Ro, Buk-Ku, GwangJu, 61186, Republic of Korea. chungoo@jnu.ac.kr.', '-|-|-|-|-|-|-', 5, 'Biomedical Research|Databases, Genetic|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Genes|Humans|Membrane Glycoproteins|Neoplasms|Real-Time Polymerase Chain Reaction|Reference Standards|Reproducibility of Results', 'Journal Article', '10.1186/s12859-019-2809-2', '31138119', 'The selection of reference genes is essential for quantifying gene expression. Theoretically they should be expressed stably and not regulated by experimental or pathological conditions. However, identification and validation of reference genes for human cancer research are still being regarded as a critical point, because cancerous tissues often represent genetic instability and heterogeneity. Recent pan-cancer studies have demonstrated the importance of the appropriate selection of reference genes for use as internal controls for the normalization of gene expression, however, no stably expressed, consensus reference genes valid for a range of different human cancers have yet been identified.'),
(99029, 2019, 'bmc', 'Efficient estimation of grouped survival models', 'Li, Zhiguo|Lin, Jiaxing|Sibley, Alexander B|Truong, Tracy|Chua, Katherina C|Jiang, Yu|McCarthy, Janice|Kroetz, Deanna L|Allen, Andrew|Owzar, Kouros', 'Department of Biostatistics and Bioinformatics, Duke University, Durham, USA. zhiguo.li@duke.edu.|Department of Biostatistics and Bioinformatics, Duke University, Durham, USA.|Duke Cancer Institute, Duke University, Durham, USA.|Department of Biostatistics and Bioinformatics, Duke University, Durham, USA.|Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, USA.|Department of Biostatistics and Bioinformatics, Duke University, Durham, USA.|Department of Biostatistics and Bioinformatics, Duke University, Durham, USA.|Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, USA.|Department of Biostatistics and Bioinformatics, Duke University, Durham, USA.|Department of Biostatistics and Bioinformatics, Duke University, Durham, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Benchmarking|Gene Frequency|Genome-Wide Association Study|Humans|Likelihood Functions|Models, Genetic|Phenotype|Software|Statistics as Topic', 'Journal Article', '10.1186/s12859-019-2899-x', '31138120', 'Time- and dose-to-event phenotypes used in basic science and translational studies are commonly measured imprecisely or incompletely due to limitations of the experimental design or data collection schema. For example, drug-induced toxicities are not reported by the actual time or dose triggering the event, but rather are inferred from the cycle or dose to which the event is attributed. This exemplifies a prevalent type of imprecise measurement called grouped failure time, where times or doses are restricted to discrete increments. Failure to appropriately account for the grouped nature of the data, when present, may lead to biased analyses.'),
(99030, 2019, 'bmc', 'Batch correction evaluation framework using a-priori gene-gene associations: applied to the GTEx dataset', 'Somekh, Judith|Shen-Orr, Shai S|Kohane, Isaac S', 'Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. judith_somekh@is.haifa.ac.il.|Faculty of Medicine, Technion - Israel Institute of Technology, Haifa, Israel.|Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.', 'United States|Israel|United States', 2, 'Algorithms|Area Under Curve|Computational Biology|Databases, Genetic|Epistasis, Genetic|Gene Expression Regulation|Genes|Humans|ROC Curve|Subcutaneous Fat', 'Journal Article', '10.1186/s12859-019-2855-9', '31138121', 'Correcting a heterogeneous dataset that presents artefacts from several confounders is often an essential bioinformatics task. Attempting to remove these batch effects will result in some biologically meaningful signals being lost. Thus, a central challenge is assessing if the removal of unwanted technical variation harms the biological signal that is of interest to the researcher.'),
(99031, 2019, 'bmc', 'CODA-ML: context-specific biological knowledge representation for systemic physiology analysis', 'Kwon, Mijin|Yim, Soorin|Kim, Gwangmin|Lee, Saehwan|Jeong, Chungsun|Lee, Doheon', 'Department of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea.|Department of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea.|Department of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea.|Department of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea.|Department of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea.|Department of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea. dhlee@kaist.ac.kr.', '-|-|-|-|-|-', 0, 'Humans|Knowledge|Language|Models, Biological|Physiology|Software', 'Journal Article', '10.1186/s12859-019-2812-7', '31138123', 'Computational analysis of complex diseases involving multiple organs requires the integration of multiple different models into a unified model. Different models are often constructed in heterogeneous formats. Thus, the integration of the models requires a standard language format that can effectively represent essential biological information. However, the previously introduced formats have limitations that prevent from adequately representing essential biological information, particularly specifications of bio-molecules and biological contexts.'),
(99032, 2019, 'bmc', 'Control of multilayer biological networks and applied to target identification of complex diseases', 'Zheng, Wei|Wang, Dingjie|Zou, Xiufen', 'School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, China.|School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, China.|School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, China. xfzou@whu.edu.cn.', 'China|China|China', 2, 'Algorithms|Cell Communication|Colitis|Colonic Neoplasms|Databases as Topic|Disease|Gene Regulatory Networks|HIV-1|Humans|Nonlinear Dynamics', 'Journal Article', '10.1186/s12859-019-2841-2', '31138124', 'Networks have been widely used to model the structures of various biological systems. The ultimate aim of research on biological networks is to steer biological system structures to desired states by manipulating signals. Despite great advances in the linear control of single-layer networks, it has been observed that many complex biological systems have a multilayer networked structure and extremely complicated nonlinear processes.'),
(99033, 2019, 'bmc', 'flowDiv: a new pipeline for analyzing flow cytometric diversity', 'Wanderley, Bruno M S|A Araújo, Daniel S|Quiroga, María V|Amado, André M|Neto, Adrião D D|Sarmento, Hugo|Metz, Sebastián D|Unrein, Fernando', 'Instituto Metrópole Digital, Universidade Federal do Rio Grande do Norte, Natal, Brazil.|Instituto Metrópole Digital, Universidade Federal do Rio Grande do Norte, Natal, Brazil.|Instituto Tecnológico de Chascomús (INTECH), Universidad Nacional de San Martín (UNSAM) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina.|Departamento de Oceanografia e Limnologia, Universidade Federal do Rio Grande do Norte, Natal, Brazil.|Instituto Metrópole Digital, Universidade Federal do Rio Grande do Norte, Natal, Brazil.|Departamento de Hidrobiologia, Universidade Federal de São Carlos, São Carlos, Brazil.|Instituto Tecnológico de Chascomús (INTECH), Universidad Nacional de San Martín (UNSAM) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina.|Instituto Tecnológico de Chascomús (INTECH), Universidad Nacional de San Martín (UNSAM) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina. funrein@intech.gov.ar.', 'Brazil|Brazil|Argentina|Brazil|Brazil|Brazil|Argentina|Argentina', 0, 'Biodiversity|Flow Cytometry|Humans|Lakes|Microbiota|Principal Component Analysis|Software|Statistics, Nonparametric', 'Journal Article', '10.1186/s12859-019-2787-4', '31138128', 'Flow cytometry (FCM) is one of the most commonly used technologies for analysis of numerous biological systems at the cellular level, from cancer cells to microbial communities. Its high potential and wide applicability led to the development of various analytical protocols, which are often not interchangeable between fields of expertise. Environmental science in particular faces difficulty in adapting to non-specific protocols, mainly because of the highly heterogeneous nature of environmental samples. This variety, although it is intrinsic to environmental studies, makes it difficult to adjust analytical protocols to maintain both mathematical formalism and comprehensible biological interpretations, principally for questions that rely on the evaluation of differences between cytograms, an approach also termed cytometric diversity. Despite the availability of promising bioinformatic tools conceived for or adapted to cytometric diversity, most of them still cannot deal with common technical issues such as the integration of differently acquired datasets, the optimal number of bins, and the effective correlation of bins to previously known cytometric populations.'),
(99034, 2019, 'bmc', 'An application of convolutional neural networks with salient features for relation classification', 'Dashdorj, Zolzaya|Song, Min', 'Department of Library and Information Science, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul, 120-749, Republic of Korea.|Department of Library and Information Science, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul, 120-749, Republic of Korea. min.song@yonsei.ac.kr.', '-|-', 2, 'Algorithms|Databases as Topic|Models, Theoretical|Neural Networks (Computer)', 'Journal Article', '10.1186/s12859-019-2808-3', '31138159', 'Due to the advent of deep learning, the increasing number of studies in the biomedical domain has attracted much interest in feature extraction and classification tasks. In this research, we seek the best combination of feature set and hyperparameter setting of deep learning algorithms for relation classification. To this end, we incorporate an entity and relation extraction tool, PKDE4J to extract biomedical features (i.e., biomedical entities, relations) for the relation classification. We compared the chosen Convolutional Neural Networks (CNN) based classification model with the most widely used learning algorithms.'),
(99035, 2019, 'bmc', 'Identifying lncRNA-mediated regulatory modules via ChIA-PET network analysis', 'Thiel, Denise|Conrad, Nataša Djurdjevac|Ntini, Evgenia|Peschutter, Ria X|Siebert, Heike|Marsico, Annalisa', 'Max Planck Institute for Molecular Genetics, Berlin, Ihnestraße 63-73, Berlin, 14195, Germany.|Zuse Institute Berlin (ZIB), Takustraße 7, Berlin, 14195, Germany.|Max Planck Institute for Molecular Genetics, Berlin, Ihnestraße 63-73, Berlin, 14195, Germany.|Max Planck Institute for Molecular Genetics, Berlin, Ihnestraße 63-73, Berlin, 14195, Germany.|Department of Mathematics and Informatics, Freie Universität, Berlin, Arnimallee 7, Berlin, 14195, Germany.|Max Planck Institute for Molecular Genetics, Berlin, Ihnestraße 63-73, Berlin, 14195, Germany. marsico@molgen.mpg.de.', 'Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Chromatin|Gene Expression Regulation|Gene Regulatory Networks|Humans|K562 Cells|RNA, Long Noncoding|RNA, Messenger|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-019-2900-8', '31142264', 'Although several studies have provided insights into the role of long non-coding RNAs (lncRNAs), the majority of them have unknown function. Recent evidence has shown the importance of both lncRNAs and chromatin interactions in transcriptional regulation. Although network-based methods, mainly exploiting gene-lncRNA co-expression, have been applied to characterize lncRNA of unknown function by means of guilt-by-association, no strategy exists so far which identifies mRNA-lncRNA functional modules based on the 3D chromatin interaction graph.');
