INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(99036, 2019, 'bmc', 'Pairwise visual comparison of small RNA secondary structures with base pair probabilities', 'Léger, Serge|Costa, Maria Beatriz Walter|Tulpan, Dan', 'Digital Technologies Research Center, National Research Council Canada, 100 des Aboiteaux St, Moncton, NB, E1A7R1, Canada.|Department of Computer Science, TFome Research Group, Bioinformatics Group, Interdisciplinary Center of Bioinformatics, University of Leipzig, Härtelstrasse 16-18, D-04107, Leipzig, Germany.|Department of Animal Biosciences, Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, Ontario, Canada. dtulpan@uoguelph.ca.', 'Canada|Germany|Canada', 0, 'Algorithms|Base Pairing|CRISPR-Cas Systems|Evolution, Molecular|Humans|Nucleic Acid Conformation|Probability|RNA|RNA, Guide|Virulence|Yersinia', 'Journal Article', '10.1186/s12859-019-2902-6', '31142266', 'Predicted RNA secondary structures are typically visualized using dot-plots for base pair binding probabilities and planar graphs for unique structures, such as the minimum free energy structure. These are however difficult to analyze simultaneously.'),
(99037, 2019, 'bmc', 'Time varying causal network reconstruction of a mouse cell cycle', 'Masnadi-Shirazi, Maryam|Maurya, Mano R|Pao, Gerald|Ke, Eugene|Verma, Inder M|Subramaniam, Shankar', 'Department of Electrical and Computer Engineering and Bioengineering, 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.|Salk institute for Biological Studies, 10010 N Torrey Pines Rd, La Jolla, CA, 92037, USA.|Salk institute for Biological Studies, 10010 N Torrey Pines Rd, La Jolla, CA, 92037, USA.|Salk institute for Biological Studies, 10010 N Torrey Pines Rd, La Jolla, CA, 92037, USA.|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|United States|United States|United States', 1, 'Algorithms|Animals|Cell Cycle|Cell Cycle Checkpoints|Embryo, Mammalian|Fibroblasts|G1 Phase|Gene Regulatory Networks|Genes, cdc|Mice|Time Factors', 'Journal Article', '10.1186/s12859-019-2895-1', '31142274', 'Biochemical networks are often described through static or time-averaged measurements of the component macromolecules. Temporal variation in these components plays an important role in both describing the dynamical nature of the network as well as providing insights into causal mechanisms. Few methods exist, specifically for systems with many variables, for analyzing time series data to identify distinct temporal regimes and the corresponding time-varying causal networks and mechanisms.'),
(99038, 2019, 'bmc', 'Pairwise efficiency: a new mathematical approach to qPCR data analysis increases the precision of the calibration curve assay', 'Panina, Yulia|Germond, Arno|David, Brit G|Watanabe, Tomonobu M', 'Laboratory for Comprehensive Bioimaging, RIKEN Center for Biosystems Dynamics Research (BDR), 6-2-3 Furuedai, Suita, Osaka, 565-0874, Japan.|Laboratory for Comprehensive Bioimaging, RIKEN Center for Biosystems Dynamics Research (BDR), 6-2-3 Furuedai, Suita, Osaka, 565-0874, Japan.|Laboratory for Comprehensive Bioimaging, RIKEN Center for Biosystems Dynamics Research (BDR), 6-2-3 Furuedai, Suita, Osaka, 565-0874, Japan.|Laboratory for Comprehensive Bioimaging, RIKEN Center for Biosystems Dynamics Research (BDR), 6-2-3 Furuedai, Suita, Osaka, 565-0874, Japan. tomowatanabe@riken.jp.', 'Japan|Japan|Japan|Japan', 0, 'Animals|Calibration|Cell Line|Data Analysis|Indicator Dilution Techniques|Mice|Monte Carlo Method|Real-Time Polymerase Chain Reaction', 'Journal Article', '10.1186/s12859-019-2911-5', '31146686', 'The real-time quantitative polymerase chain reaction (qPCR) is routinely used for quantification of nucleic acids and is considered the gold standard in the field of relative nucleic acid measurements. The efficiency of the qPCR reaction is one of the most important parameters in data analysis in qPCR experiments. The Minimum Information for publication of Quantitative real-time PCR Experiments (MIQE) guidelines recommends the calibration curve as the method of choice for estimation of qPCR efficiency. The precision of this method has been reported to be between SDxe2x80x89=xe2x80x890.007 (three replicates) and SDxe2x80x89=xe2x80x890.022 (no replicates).'),
(99039, 2019, 'bmc', 'Dynamics of gene regulatory networks and their dependence on network topology and quantitative parameters - the case of phage xcexbb', 'Ruklisa, Dace|Brazma, Alvis|Cerans, Karlis|Schlitt, Thomas|Viksna, Juris', 'Newnham College, University of Cambridge, Sidgwick Avenue, Cambridge, CB3 9DF, UK. dr320@cam.ac.uk.|European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, CB10 1SD, UK.|Institute of Mathematics and Computer Science, University of Latvia, Rainis blvd. 29, Riga, LV 1459, Latvia.|Division of Molecular Neuroscience, Faculty of Psychology, University of Basel, Basel, 4055, Switzerland.|Institute of Mathematics and Computer Science, University of Latvia, Rainis blvd. 29, Riga, LV 1459, Latvia.', 'United Kingdom|United Kingdom|Latvia|Switzerland|Latvia', 0, 'Bacteriophage lambda|Gene Expression Regulation, Viral|Gene Regulatory Networks|Lysogeny|Models, Biological|Mutation|Operon|Software', 'Journal Article', '10.1186/s12859-019-2909-z', '31151381', 'Gene regulatory networks can be modelled in various ways depending on the level of detail required and biological questions addressed. One of the earliest formalisms used for modeling is a Boolean network, although these models cannot describe most temporal aspects of a biological system. Differential equation models have also been used to model gene regulatory networks, but these frameworks tend to be too detailed for large models and many quantitative parameters might not be deducible in practice. Hybrid models bridge the gap between these two model classes - these are useful when concentration changes are important while the information about precise concentrations and binding site affinities is partial.'),
(99040, 2019, 'bmc', 'A comparison of three programming languages for a full-fledged next-generation sequencing tool', 'Costanza, Pascal|Herzeel, Charlotte|Verachtert, Wilfried', 'imec, ExaScience Lab, Kapeldreef 75, Leuven, 3001, Belgium.|imec, ExaScience Lab, Kapeldreef 75, Leuven, 3001, Belgium.|imec, ExaScience Lab, Kapeldreef 75, Leuven, 3001, Belgium.', 'Belgium|Belgium|Belgium', 0, 'Benchmarking|High-Throughput Nucleotide Sequencing|Humans|Programming Languages|Software|Time Factors', 'Comparative Study', '10.1186/s12859-019-2903-5', '31159721', 'elPrep is an established multi-threaded framework for preparing SAM and BAM files in sequencing pipelines. To achieve good performance, its software architecture makes only a single pass through a SAM/BAM file for multiple preparation steps, and keeps sequencing data as much as possible in main memory. Similar to other SAM/BAM tools, management of heap memory is a complex task in elPrep, and it became a serious productivity bottleneck in its original implementation language during recent further development of elPrep. We therefore investigated three alternative programming languages: Go and Java using a concurrent, parallel garbage collector on the one hand, and C++17 using reference counting on the other hand for handling large amounts of heap objects. We reimplemented elPrep in all three languages and benchmarked their runtime performance and memory use.'),
(99041, 2019, 'bmc', 'Illumina error correction near highly repetitive DNA regions improves de novo genome assembly', 'Heydari, Mahdi|Miclotte, Giles|Van de Peer, Yves|Fostier, Jan', 'Department of Information Technology, Ghent University-imec, IDLab, Ghent, B-9052, Belgium.|Department of Information Technology, Ghent University-imec, IDLab, Ghent, B-9052, Belgium.|Bioinformatics Institute Ghent, Ghent, B-9052, Belgium.|Department of Information Technology, Ghent University-imec, IDLab, Ghent, B-9052, Belgium. jan.fostier@ugent.be.', 'Belgium|Belgium|Belgium|Belgium', 0, 'Algorithms|Animals|DNA|Databases, Nucleic Acid|Genome|Humans|Repetitive Sequences, Nucleic Acid|Sequence Alignment|Sequence Analysis, DNA|Time Factors', 'Journal Article', '10.1186/s12859-019-2906-2', '31159722', 'Several standalone error correction tools have been proposed to correct sequencing errors in Illumina data in order to facilitate de novo genome assembly. However, in a recent survey, we showed that state-of-the-art assemblers often did not benefit from this pre-correction step. We found that many error correction tools introduce new errors in reads that overlap highly repetitive DNA regions such as low-complexity patterns or short homopolymers, ultimately leading to a more fragmented assembly.'),
(99042, 2019, 'bmc', 'A dual controllability analysis of influenza virus-host protein-protein interaction networks for antiviral drug target discovery', 'Ackerman, Emily E|Alcorn, John F|Hase, Takeshi|Shoemaker, Jason E', 'Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, PA, USA.|Division of Pulmonary Medicine, Allergy, and Immunology, Department of Pediatrics, Childrens Hospital of Pittsburgh of UPMC, Pittsburgh, PA, USA.|The Systems Biology Institute, Saisei Ikedayama Bldg. 5-10-25 Higashi Gotanda, Shinagawa, Tokyo, 141-0022, Japan.|Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, PA, USA. jason.shoemaker@pitt.edu.', 'United States|United States|Japan|United States', 0, 'Antiviral Agents|Drug Delivery Systems|Drug Discovery|Host-Pathogen Interactions|Humans|Influenza A virus|Protein Interaction Maps|RNA, Small Interfering|Reproducibility of Results|Virus Replication', 'Journal Article', '10.1186/s12859-019-2917-z', '31159726', 'Host factors of influenza virus replication are often found in key topological positions within protein-protein interaction networks. This work explores how protein states can be manipulated through controllability analysis: the determination of the minimum manipulation needed to drive the cell system to any desired state. Here, we complete a two-part controllability analysis of two protein networks: a host network representing the healthy cell state and an influenza A virus-host network representing the infected cell state. In this context, controllability analyses aim to identify key regulating host factors of the infected cells progression. This knowledge can be utilized in further biological analysis to understand disease dynamics and isolate proteins for study as drug target candidates.'),
(99043, 2019, 'bmc', 'ANDIS: an atomic angle- and distance-dependent statistical potential for protein structure quality assessment', 'Yu, Zhongwang|Yao, Yuangen|Deng, Haiyou|Yi, Ming', 'Department of Physics, College of Science, Huazhong Agricultural University, Wuhan, 430070, China.|Department of Physics, College of Science, Huazhong Agricultural University, Wuhan, 430070, China.|Department of Physics, College of Science, Huazhong Agricultural University, Wuhan, 430070, China. hydeng@mail.hzau.edu.cn.|Department of Physics, College of Science, Huazhong Agricultural University, Wuhan, 430070, China. yiming@mail.hzau.edu.cn.', 'China|China|China|China', 0, 'Computational Biology|Databases, Protein|Protein Conformation|Protein Folding|Proteins|Software|Statistics as Topic', 'Journal Article', '10.1186/s12859-019-2898-y', '31159742', 'The knowledge-based statistical potential has been widely used in protein structure modeling and model quality assessment. They are commonly evaluated based on their abilities of native recognition as well as decoy discrimination. However, these two aspects are found to be mutually exclusive in many statistical potentials.'),
(99044, 2019, 'bmc', 'Differential analysis of combinatorial protein complexes with CompleXChange', 'Will, Thorsten|Helms, Volkhard', 'Center for Bioinformatics, Saarland University, Campus E2.1, Saarbrücken, 66123, Germany.|Center for Bioinformatics, Saarland University, Campus E2.1, Saarbrücken, 66123, Germany. volkhard.helms@bioinformatik.uni-saarland.de.', 'Germany|Germany', 0, 'Benchmarking|Databases, Protein|Female|Humans|Multiprotein Complexes|Programming, Linear|Reproducibility of Results|Software|Transcription Factors', 'Journal Article', '10.1186/s12859-019-2852-z', '31159772', 'Although a considerable number of proteins operate as multiprotein complexes and not on their own, organism-wide studies so far are only able to quantify individual proteins or protein-coding genes in a condition-specific manner for a sizeable number of samples, but not their assemblies. Consequently, there exist large amounts of transcriptomic data and an increasing amount of data on proteome abundance, but quantitative knowledge on complexomes is missing. This deficiency impedes the applicability of the powerful tool of differential analysis in the realm of macromolecular complexes. Here, we present a pipeline for differential analysis of protein complexes based on predicted or manually assigned complexes and inferred complex abundances, which can be easily applied on a whole-genome scale.'),
(99045, 2019, 'bmc', 'TAR-VIR: a pipeline for TARgeted VIRal strain reconstruction from metagenomic data', 'Chen, Jiao|Huang, Jiating|Sun, Yanni', 'Computer Science and Engineering, Michigan State University, East Lansing, 48824, USA.|Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou, 510006, China.|Electronic Engineering, City University of Hong Kong, Hong Kong, China. yannisun@cityu.edu.hk.', 'United States|China|China', 0, 'Humans|Metagenomics|RNA Viruses|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/s12859-019-2878-2', '31164077', 'Strain-level RNA virus characterization is essential for developing prevention and treatment strategies. Viral metagenomic data, which can contain sequences of both known and novel viruses, provide new opportunities for characterizing RNA viruses. Although there are a number of pipelines for analyzing viruses in metagenomic data, they have different limitations. First, viruses that lack closely related reference genomes cannot be detected with high sensitivity. Second, strain-level analysis is usually missing.'),
(99046, 2019, 'bmc', 'COMBImage2: a parallel computational framework for higher-order drug combination analysis that includes automated plate design, matched filter based object counting and temporal data mining', 'Chantzi, Efthymia|Jarvius, Malin|Niklasson, Mia|Segerman, Anna|Gustafsson, Mats G', 'Department of Medical Sciences, Cancer Pharmacology and Computational Medicine, Uppsala University, Uppsala, Sweden. efthymia.chantzi@medsci.uu.se.|Department of Medical Sciences, Cancer Pharmacology and Computational Medicine, Uppsala University, Uppsala, Sweden.|Department of Immunology, Genetics and Pathology, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden.|Department of Medical Sciences, Cancer Pharmacology and Computational Medicine, Uppsala University, Uppsala, Sweden.|Department of Medical Sciences, Cancer Pharmacology and Computational Medicine, Uppsala University, Uppsala, Sweden.', 'Sweden|Sweden|Sweden|Sweden|Sweden', 0, 'Algorithms|Antineoplastic Agents|Apoptosis|Data Mining|Drug Combinations|Glioblastoma|Humans|Microscopy, Video|Neoplasm Recurrence, Local|Pilot Projects', 'Journal Article', '10.1186/s12859-019-2908-0', '31164078', 'Pharmacological treatment of complex diseases using more than two drugs is commonplace in the clinic due to better efficacy, decreased toxicity and reduced risk for developing resistance. However, many of these higher-order treatments have not undergone any detailed preceding in vitro evaluation that could support their therapeutic potential and reveal disease related insights. Despite the increased medical need for discovery and development of higher-order drug combinations, very few reports from systematic large-scale studies along this direction exist. A major reason is lack of computational tools that enable automated design and analysis of exhaustive drug combination experiments, where all possible subsets among a panel of pre-selected drugs have to be evaluated.'),
(99047, 2019, 'bmc', 'Detection and visualization of communities in mass spectrometry imaging data', 'Wüllems, Karsten|Kölling, Jan|Bednarz, Hanna|Niehaus, Karsten|Hans, Volkmar H|Nattkemper, Tim W', 'International Research Training Group Computational Methods for the Analysis of the Diversity and Dynamics of Genomes, Bielefeld University, Universitätsstraße 25, Bielefeld, 33613, Germany. wuellems@cebitec.uni-bielefeld.de.|International Research Training Group Computational Methods for the Analysis of the Diversity and Dynamics of Genomes, Bielefeld University, Universitätsstraße 25, Bielefeld, 33613, Germany.|Center for Biotechnology (CeBiTec), Universitätsstraße 25, Bielefeld, 33613, Germany.|Center for Biotechnology (CeBiTec), Universitätsstraße 25, Bielefeld, 33613, Germany.|Department of Neuropathology, Institute for Clinical Pathology, Dietrich-Bonhoeffer-Klinikum, Salvador-Allende-Straße 30, Neubrandenburg, 17036, Germany.|Biodata Mining Group, Faculty of Technology, Bielefeld University, Universitätsstraße 25, Bielefeld, 33613, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Brain Neoplasms|Cluster Analysis|Data Visualization|Glioblastoma|Hordeum|Humans|Principal Component Analysis|Seeds|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Journal Article', '10.1186/s12859-019-2890-6', '31164082', 'The spatial distribution and colocalization of functionally related metabolites is analysed in order to investigate the spatial (and functional) aspects of molecular networks. We propose to consider community detection for the analysis of m/z-images to group molecules with correlative spatial distribution into communities so they hint at functional networks or pathway activity. To detect communities, we investigate a spectral approach by optimizing the modularity measure. We present an analysis pipeline and an online interactive visualization tool to facilitate explorative analysis of the results. The approach is illustrated with synthetical benchmark data and two real world data sets (barley seed and glioblastoma section).'),
(99048, 2019, 'bmc', 'GRASP2: fast and memory-efficient gene-centric assembly and homolog search for metagenomic sequencing data', 'Zhong, Cuncong|Yang, Youngik|Yooseph, Shibu', 'Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS, 66045, USA. cczhong@ku.edu.|National Marine Biodiversity Institute of Korea, 101-75, Jangsan-ro, Janghang-eup, Seochun-gun, Chungchungnam-do, 33662, South Korea.|Department of Computer Science, University of Central Florida, Orlando, FL, 32816, USA.', 'United States|United States|United States', 0, 'Algorithms|Aquatic Organisms|Genes|Metagenomics|Microbiota|ROC Curve|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Software|Time Factors', 'Journal Article', '10.1186/s12859-019-2818-1', '31167633', 'A crucial task in metagenomic analysis is to annotate the function and taxonomy of the sequencing reads generated from a microbiome sample. In general, the reads can either be assembled into contigs and searched against reference databases, or individually searched without assembly. The first approach may suffer from fragmentary and incomplete assembly, while the second is hampered by the reduced functional signal contained in the short reads. To tackle these issues, we have previously developed GRASP (Guided Reference-based Assembly of Short Peptides), which accepts a reference protein sequence as input and aims to assemble its homologs from a database containing fragmentary protein sequences. In addition to a gene-centric assembly tool, GRASP also serves as a homolog search tool when using the assembled protein sequences as templates to recruit reads. GRASP has significantly improved recall rate (60-80% vs. 30-40%) compared to other homolog search tools such as BLAST. However, GRASP is both time- and space-consuming. Subsequently, we developed GRASPx, which is 30X faster than GRASP. Here, we present a completely redesigned algorithm, GRASP2, for this computational problem.'),
(99049, 2019, 'bmc', 'MATria: a unified centrality algorithm', 'Cickovski, Trevor|Aguiar-Pulido, Vanessa|Narasimhan, Giri', 'Bioinformatics Research Group (BioRG) & Biomolecular Sciences Institute, School of Computing & Information Sciences, Florida International University, 11200 SW 8th St, Miami, 33199, FL, USA. tcickovs@fiu.edu.|Center for Neurogenetics, Weill Cornell Medical College, New York, 10021, NY, USA.|Bioinformatics Research Group (BioRG) & Biomolecular Sciences Institute, School of Computing & Information Sciences, Florida International University, 11200 SW 8th St, Miami, 33199, FL, USA.', 'United States|United States|United States', 0, 'Algorithms|Animals|Bacteria|Computer Graphics|Gene Regulatory Networks|Ostreidae|Time Factors', 'Journal Article', '10.1186/s12859-019-2820-7', '31167635', 'Computing centrality is a foundational concept in social networking that involves finding the most central or important nodes. In some biological networks defining importance is difficult, which then creates challenges in finding an appropriate centrality algorithm.'),
(99050, 2019, 'bmc', 'BAMSE: Bayesian model selection for tumor phylogeny inference among multiple samples', 'Toosi, Hosein|Moeini, Ali|Hajirasouliha, Iman', 'Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.|School of Engineering Sciences, College of Engineering, University of Tehran, Tehran, Iran. moeini@ut.ac.ir.|Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA. imh2003@med.cornell.edu.', 'United States|United States|United States', 0, 'Algorithms|Bayes Theorem|Carcinoma, Renal Cell|Computational Biology|Computer Simulation|Humans|Kidney Neoplasms|Models, Biological|Mutation|Neoplasms|Phylogeny|Software', 'Journal Article', '10.1186/s12859-019-2824-3', '31167637', 'Intra-tumor heterogeneity is known to contribute to cancer complexity and drug resistance. Understanding the number of distinct subclones and the evolutionary relationships between them is scientifically and clinically very important and still a challenging problem.'),
(99051, 2019, 'bmc', 'Multi-allelic positional Burrows-Wheeler transform', 'Naseri, Ardalan|Zhi, Degui|Zhang, Shaojie', 'Department of Computer Science, University of Central Florida, Orlando, FL, 32816, USA.|School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.|Department of Computer Science, University of Central Florida, Orlando, FL, 32816, USA. shzhang@cs.ucf.edu.', 'United States|United States|United States', 0, 'Algorithms|Alleles|Data Compression|Genes|Haplotypes|Humans|Time Factors', 'Journal Article', '10.1186/s12859-019-2821-6', '31167638', 'Recent advances in whole-genome sequencing and SNP array technology have led to the generation of a large amount of genotype data. Large volumes of genotype data will require faster and more efficient methods for storing and searching the data. Positional Burrows-Wheeler Transform (PBWT) provides an appropriate data structure for bi-allelic data. With the increasing sample sizes, more multi-allelic sites are expected to be observed. Hence, there is a necessity to handle multi-allelic genotype data.'),
(99052, 2019, 'bmc', 'Attenuating dependence on structural data in computing protein energy landscapes', 'Morris, David|Maximova, Tatiana|Plaku, Erion|Shehu, Amarda', 'Department of Computer Science, George Mason University, Fairfax, 22030, VA, USA.|Department of Computer Science, George Mason University, Fairfax, 22030, VA, USA.|Department of Electrical Engineering and Computer Science, The Catholic University of America, Washington, 20064, D.C., USA.|Department of Computer Science, George Mason University, Fairfax, 22030, VA, USA. amarda@gmu.edu.', 'United States|United States|United States|United States', 0, 'Algorithms|Computational Biology|Guanosine Diphosphate|Humans|Motion|Principal Component Analysis|Proteins|Thermodynamics', 'Journal Article', '10.1186/s12859-019-2822-5', '31167640', 'Nearly all cellular processes involve proteins structurally rearranging to accommodate molecular partners. The energy landscape underscores the inherent nature of proteins as dynamic molecules interconverting between structures with varying energies. In principle, reconstructing a proteins energy landscape holds the key to characterizing the structural dynamics and its regulation of protein function. In practice, the disparate spatio-temporal scales spanned by the slow dynamics challenge both wet and dry laboratories. However, the growing number of deposited structures for proteins central to human biology presents an opportunity to infer the relevant dynamics via exploitation of the information encoded in such structures about equilibrium dynamics.'),
(99053, 2019, 'bmc', 'String correction using the Damerau-Levenshtein distance', 'Zhao, Chunchun|Sahni, Sartaj', 'Department of Computer and Information Science and Engineering, University of Florida, Gainesville, 32611, FL, USA. czhao@cise.ufl.edu.|Department of Computer and Information Science and Engineering, University of Florida, Gainesville, 32611, FL, USA.', 'United States|United States', 5, 'Algorithms|Base Sequence|Computational Biology|Time Factors', 'Journal Article', '10.1186/s12859-019-2819-0', '31167641', 'In the string correction problem, we are to transform one string into another using a set of prescribed edit operations. In string correction using the Damerau-Levenshtein (DL) distance, the permissible edit operations are: substitution, insertion, deletion and transposition. Several algorithms for string correction using the DL distance have been proposed. The fastest and most space efficient of these algorithms is due to Lowrance and Wagner. It computes the DL distance between strings of length m and n, respectively, in O(mn) time and O(mn) space. In this paper, we focus on the development of algorithms whose asymptotic space complexity is less and whose actual runtime and energy consumption are less than those of the algorithm of Lowrance and Wagner.'),
(99054, 2019, 'bmc', 'Deep convolutional neural networks for mammography: advances, challenges and applications', 'Abdelhafiz, Dina|Yang, Clifford|Ammar, Reda|Nabavi, Sheida', 'Department of Computer Science and Engineering, University of Connecticut, Storrs, 06269, CT, USA. dina.abdelhafiz@uconn.edu.|Department of Diagnostic Imaging, University of Connecticut Health Center, Farmington, 06030, CT, USA.|Department of Computer Science and Engineering, University of Connecticut, Storrs, 06269, CT, USA.|Department of Computer Science and Engineering, University of Connecticut, Storrs, 06269, CT, USA.', 'United States|United States|United States|United States', 20, 'Breast Neoplasms|Databases, Factual|Deep Learning|Female|Humans|Image Processing, Computer-Assisted|Mammography|Neural Networks (Computer)|Publications|Surveys and Questionnaires', 'Journal Article', '10.1186/s12859-019-2823-4', '31167642', 'The limitations of traditional computer-aided detection (CAD) systems for mammography, the extreme importance of early detection of breast cancer and the high impact of the false diagnosis of patients drive researchers to investigate deep learning (DL) methods for mammograms (MGs). Recent breakthroughs in DL, in particular, convolutional neural networks (CNNs) have achieved remarkable advances in the medical fields. Specifically, CNNs are used in mammography for lesion localization and detection, risk assessment, image retrieval, and classification tasks. CNNs also help radiologists providing more accurate diagnosis by delivering precise quantitative analysis of suspicious lesions.'),
(99055, 2019, 'bmc', 'A computational method to aid the design and analysis of single cell RNA-seq experiments for cell type identification', 'Abrams, Douglas|Kumar, Parveen|Karuturi, R Krishna Murthy|George, Joshy', 'Colby College, Waterville, ME, USA.|The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA.|The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA. krishna.karuturi@jax.org.|The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA. joshy.george@jax.org.', 'United States|United States|United States|United States', 0, 'Algorithms|Computational Biology|Computer Simulation|Databases, Genetic|Gene Expression Profiling|Humans|Research Design|Sample Size|Sequence Analysis, RNA|Single-Cell Analysis', 'Journal Article', '10.1186/s12859-019-2817-2', '31167661', 'The advent of single cell RNA sequencing (scRNA-seq) enabled researchers to study transcriptomic activity within individual cells and identify inherent cell types in the sample. Although numerous computational tools have been developed to analyze single cell transcriptomes, there are no published studies and analytical packages available to guide experimental design and to devise suitable analysis procedure for cell type identification.'),
(99056, 2019, 'bmc', 'Probe computing model based on small molecular switch', 'Wang, Yanan|Lv, Qi|Zhang, Yingying|Wang, Luhui|Dong, Yafei', 'School of Computer Science, Shaanxi Normal University, Xian, 710119, China.|College of Life Sciences, Shaanxi Normal University, Xian, 710119, China.|School of Computer Science, Shaanxi Normal University, Xian, 710119, China.|College of Life Sciences, Shaanxi Normal University, Xian, 710119, China.|School of Computer Science, Shaanxi Normal University, Xian, 710119, China. dongyf@snnu.edu.cn.', 'China|China|China|China|China', 0, 'Computer Simulation|DNA|DNA, Catalytic|Fluorescence|Logic|Molecular Probes|Signal Processing, Computer-Assisted|Streptavidin', 'Journal Article', '10.1186/s12859-019-2767-8', '31182004', 'DNA is a promising candidate for the construction of biological devices due to its unique properties, including structural simplicity, convenient synthesis, high flexibility, and predictable behavior. And DNA has been widely used to construct the advanced logic devices.'),
(99057, 2019, 'bmc', 'Combining gene ontology with deep neural networks to enhance the clustering of single cell RNA-Seq data', 'Peng, Jiajie|Wang, Xiaoyu|Shang, Xuequn', 'School of Computer Science, Northwestern Polytechnical University, Xian, 710072, China.|School of Computer Science, Northwestern Polytechnical University, Xian, 710072, China.|School of Computer Science, Northwestern Polytechnical University, Xian, 710072, China. shang@nwpu.edu.cn.', 'China|China|China', 21, 'Algorithms|Animals|Base Sequence|Cluster Analysis|Databases, Genetic|Gene Ontology|Humans|Mice|Neural Networks (Computer)|RNA|Sequence Analysis, RNA|Single-Cell Analysis', 'Journal Article', '10.1186/s12859-019-2769-6', '31182005', 'Single cell RNA sequencing (scRNA-seq) is applied to assay the individual transcriptomes of large numbers of cells. The gene expression at single-cell level provides an opportunity for better understanding of cell function and new discoveries in biomedical areas. To ensure that the single-cell based gene expression data are interpreted appropriately, it is crucial to develop new computational methods.'),
(99058, 2019, 'bmc', 'L2,1-GRMF: an improved graph regularized matrix factorization method to predict drug-target interactions', 'Cui, Zhen|Gao, Ying-Lian|Liu, Jin-Xing|Dai, Ling-Yun|Yuan, Sha-Sha', 'School of Information Science and Engineering, Qufu Normal University, Rizhao, China.|Library of Qufu Normal University, Qufu Normal University, Rizhao, China.|School of Information Science and Engineering, Qufu Normal University, Rizhao, China. sdcavell@qfnu.edu.cn.|School of Information Science and Engineering, Qufu Normal University, Rizhao, China.|School of Information Science and Engineering, Qufu Normal University, Rizhao, China.', 'China|China|China|China|China', 0, 'Algorithms|Databases as Topic|Drug Interactions|Humans|Reproducibility of Results', 'Journal Article', '10.1186/s12859-019-2768-7', '31182006', 'Predicting drug-target interactions is time-consuming and expensive. It is important to present the accuracy of the calculation method. There are many algorithms to predict global interactions, some of which use drug-target networks for prediction (ie, a bipartite graph of bound drug pairs and targets known to interact). Although these algorithms can predict some drug-target interactions to some extent, there is little effect for some new drugs or targets that have no known interaction.'),
(99059, 2019, 'bmc', 'An advanced approach to identify antimicrobial peptides and their function types for penaeus through machine learning strategies', 'Lin, Yuan|Cai, Yinyin|Liu, Juan|Lin, Chen|Liu, Xiangrong', 'Department of Computer Science, School of Information Science and Technology, Xiamen University, Xiamen, 361005, China.|Department of Computer Science, School of Information Science and Technology, Xiamen University, Xiamen, 361005, China.|Department of Instrumental and Electrical Engineering, School of Aerospace Engineering, Xiamen University, Xiamen, 361005, China.|Department of Computer Science, School of Information Science and Technology, Xiamen University, Xiamen, 361005, China.|Department of Computer Science, School of Information Science and Technology, Xiamen University, Xiamen, 361005, China. xrliu@xmu.edu.cn.', 'China|China|China|China|China', 0, 'Amino Acid Sequence|Animals|Antimicrobial Cationic Peptides|Databases, Protein|Machine Learning|Penaeidae|Reproducibility of Results', 'Journal Article', '10.1186/s12859-019-2766-9', '31182007', 'Antimicrobial peptides (AMPs) are essential components of the innate immune system and can protect the host from various pathogenic bacteria. The marine environment is known to be one of the richest sources for AMPs. Effective usage of AMPs and their derivatives can greatly improve the immunity and breeding survival rate of aquatic products. It is highly desirable to develop computational tools for rapidly and accurately identifying AMPs and their functional types, for the purpose of helping design new and more effective antimicrobial agents.'),
(99060, 2019, 'bmc', 'Preface', 'Han, Henry|Liu, Wenbin', 'Department of Computer and Information Science, Fordham University, Lincoln Center, 113 W. 60th Street, New York, NY, 10023, USA. xhan9@fordham.edu.|Institute of Computational Science and Technology, Guangzhou University, College city 240, Guangzhou, 510006, China.', 'United States|China', 0, '-', 'Editorial', '10.1186/s12859-019-2765-x', '31182011', '-'),
(99061, 2019, 'bmc', 'Integrated entropy-based approach for analyzing exons and introns in DNA sequences', 'Li, Junyi|Zhang, Li|Li, Huinian|Ping, Yuan|Xu, Qingzhe|Wang, Rongjie|Tan, Renjie|Wang, Zhen|Liu, Bo|Wang, Yadong', 'School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, 518055, China.|School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, 518055, China.|School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, 518055, China.|School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, 518055, China.|School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, 518055, China.|School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China.|School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China.|CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.|School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China.|School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, 518055, China. ydwang@hit.edu.cn.', 'China|China|China|China|China|China|China|China|China|China', 0, 'Algorithms|Base Sequence|Chromosomes, Human|DNA|Entropy|Exons|Genome, Human|Humans|Introns|Promoter Regions, Genetic|ROC Curve|Sequence Analysis, DNA|Signal Processing, Computer-Assisted', 'Journal Article', '10.1186/s12859-019-2772-y', '31182012', 'Numerous essential algorithms and methods, including entropy-based quantitative methods, have been developed to analyze complex DNA sequences since the last decade. Exons and introns are the most notable components of DNA and their identification and prediction are always the focus of state-of-the-art research.'),
(99062, 2019, 'bmc', 'Dynamic genome-scale cell-specific metabolic models reveal novel inter-cellular and intra-cellular metabolic communications during ovarian follicle development', 'Peñalver Bernabé, Beatriz|Thiele, Ines|Galdones, Eugene|Siletz, Anaar|Chandrasekaran, Sriram|Woodruff, Teresa K|Broadbelt, Linda J|Shea, Lonnie D', 'Department of Pediatrics, University of California San Diego, La Jolla, CA, 92093, USA.|Luxembourg Center for Systems Biology, University of Luxembourg, Esch-sur-Alzette, Luxembourg, L-4365, Luxembourg.|Department of Obstetrics and Gynecology, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.|Department of Obstetrics and Gynecology, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.|Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA.|Department of Obstetrics and Gynecology, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.|Department of Chemical and Biological Engineering, Northwestern University Feinberg School of Medicine, Evanston, IL, 60208, USA.|Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA. ldshea@umich.edu.', 'United States|Luxembourg|United States|United States|United States|United States|United States|United States', 1, 'Animals|Cell Communication|Cell Differentiation|Female|Genome|Metabolic Networks and Pathways|Mice|Models, Biological|Ovarian Follicle', 'Journal Article', '10.1186/s12859-019-2825-2', '31182013', 'The maturation of the female germ cell, the oocyte, requires the synthesis and storing of all the necessary metabolites to support multiple divisions after fertilization. Oocyte maturation is only possible in the presence of surrounding, diverse, and changing layers of somatic cells. Our understanding of metabolic interactions between the oocyte and somatic cells has been limited due to dynamic nature of ovarian follicle development, thus warranting a systems approach.'),
(99063, 2019, 'bmc', 'A hybrid gene selection method based on gene scoring strategy and improved particle swarm optimization', 'Han, Fei|Tang, Di|Sun, Yu-Wen-Tian|Cheng, Zhun|Jiang, Jing|Li, Qiu-Wei', 'School of Computer Science and Communication Engineering, Jiangsu University, Xuefu Road, Zhenjiang, Jiangsu, China. hanfei@ujs.edu.cn.|School of Computer Science and Communication Engineering, Jiangsu University, Xuefu Road, Zhenjiang, Jiangsu, China.|School of Computer Science and Communication Engineering, Jiangsu University, Xuefu Road, Zhenjiang, Jiangsu, China.|School of Engineering, Nanjing Agricultural University, Weigang Road, Nanjing, Jiangsu, China.|School of Computer Science and Communication Engineering, Jiangsu University, Xuefu Road, Zhenjiang, Jiangsu, China.|School of Computer Science and Communication Engineering, Jiangsu University, Xuefu Road, Zhenjiang, Jiangsu, China.', 'China|China|China|China|China|China', 0, 'Algorithms|Databases, Genetic|Genes|Humans|Machine Learning|Neoplasms', 'Journal Article', '10.1186/s12859-019-2773-x', '31182017', 'Gene selection is one of the critical steps in the course of the classification of microarray data. Since particle swarm optimization has no complicated evolutionary operators and fewer parameters need to be adjusted, it has been used increasingly as an effective technique for gene selection. Since particle swarm optimization is apt to converge to local minima which lead to premature convergence, some particle swarm optimization based gene selection methods may select non-optimal genes with high probability. To select predictive genes with low redundancy as well as not filtering out key genes is still a challenge.'),
(99064, 2019, 'bmc', 'Scoring amino acid mutation to predict pandemic risk of avian influenza virus', 'Qiang, Xiaoli|Kou, Zheng', 'Institute of Computing Science and Technology, Guangzhou University, 230 Wai Huan Xi Road, Guangzhou Higher Education Mega Center, Guangzhou, 510006, Peoples Republic of China.|Institute of Computing Science and Technology, Guangzhou University, 230 Wai Huan Xi Road, Guangzhou Higher Education Mega Center, Guangzhou, 510006, Peoples Republic of China. kouzhengcn@foxmail.com.', 'China|China', 4, 'Algorithms|Amino Acids|Animals|Birds|Computer Simulation|Databases as Topic|Genome, Viral|Influenza in Birds|Machine Learning|Mutation|Pandemics|Reassortant Viruses|Risk Factors', 'Journal Article', '10.1186/s12859-019-2770-0', '31182019', 'Avian influenza virus can directly cross species barriers and infect humans with high fatality. As antigen novelty for human host, the public health is being challenged seriously. The pandemic risk of avian influenza viruses should be analyzed and a prediction model should be constructed for virology applications.'),
(99065, 2019, 'bmc', 'Protein-protein interaction prediction using a hybrid feature representation and a stacked generalization scheme', 'Chen, Kuan-Hsi|Wang, Tsai-Feng|Hu, Yuh-Jyh', 'College of Computer Science, National Chiao Tung University, Hsinchu, 300, Taiwan.|Institute of Data Science and Engineering, National Chiao Tung University, Hsinchu, 300, Taiwan.|Institute of Biomedical Engineering, College of Computer Science, National Chiao Tung University, Hsinchu, 300, Taiwan. yhu@cs.nctu.edu.tw.', '-|-|-', 4, 'Algorithms|Animals|Area Under Curve|Databases, Protein|Gene Ontology|Humans|Molecular Sequence Annotation|Protein Interaction Mapping', 'Journal Article', '10.1186/s12859-019-2907-1', '31182027', 'Although various machine learning-based predictors have been developed for estimating protein-protein interactions, their performances vary with dataset and species, and are affected by two primary aspects: choice of learning algorithm, and the representation of protein pairs. To improve the performance of predicting protein-protein interactions, we exploit the synergy of multiple learning algorithms, and utilize the expressiveness of different protein-pair features.'),
(99066, 2019, 'bmc', 'A new fruit fly optimization algorithm enhanced support vector machine for diagnosis of breast cancer based on high-level features', 'Huang, Hui|Feng, Xian|Zhou, Suying|Jiang, Jionghui|Chen, Huiling|Li, Yuping|Li, Chengye', 'School of Marine Science and Technology, Northwestern Polytechnical University, Xian, 710072, China.|School of Marine Science and Technology, Northwestern Polytechnical University, Xian, 710072, China.|Pathology Department of Wenzhou Peoples Hospital, Wenzhou, 325035, China.|Zhijiang College of Zhejiang University of Technology, Hangzhou, 310024, China.|Department of Computer Science, Wenzhou University, Wenzhou, 325035, China. chenhuiling.jlu@gmail.com.|Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.|Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China. lichengye41@126.com.', 'China|China|China|China|China|China|China', 0, 'Animals|Breast Neoplasms|Drosophila melanogaster|Female|Humans|Neural Networks (Computer)|Reproducibility of Results|Support Vector Machine', 'Journal Article', '10.1186/s12859-019-2771-z', '31182028', 'It is of great clinical significance to develop an accurate computer aided system to accurately diagnose the breast cancer. In this study, an enhanced machine learning framework is established to diagnose the breast cancer. The core of this framework is to adopt fruit fly optimization algorithm (FOA) enhanced by Levy flight (LF) strategy (LFOA) to optimize two key parameters of support vector machine (SVM) and build LFOA-based SVM (LFOA-SVM) for diagnosing the breast cancer. The high-level features abstracted from the volunteers are utilized to diagnose the breast cancer for the first time.'),
(99067, 2019, 'bmc', 'ProteinNet: a standardized data set for machine learning of protein structure', 'AlQuraishi, Mohammed', 'Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, MA, 02115, USA. alquraishi@hms.harvard.edu.', 'United States', 0, 'Amino Acid Sequence|Databases, Protein|Machine Learning|Proteins|Reference Standards|Sequence Alignment', 'Journal Article', '10.1186/s12859-019-2932-0', '31185886', 'Rapid progress in deep learning has spurred its application to bioinformatics problems including protein structure prediction and design. In classic machine learning problems like computer vision, progress has been driven by standardized data sets that facilitate fair assessment of new methods and lower the barrier to entry for non-domain experts. While data sets of protein sequence and structure exist, they lack certain components critical for machine learning, including high-quality multiple sequence alignments and insulated training/validation splits that account for deep but only weakly detectable homology across protein space.'),
(99068, 2019, 'bmc', 'The Kendrick modelling platform: language abstractions and tools for epidemiology', 'Bui T, Mai Anh|Papoulias, Nick|Stinckwich, Serge|Ziane, Mikal|Roche, Benjamin', 'Software Engineering Department, School of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi, Vietnam. anhbtm@soict.hust.edu.vn.|Université de La Rochelle, UMR 7266 LIENSs, CNRS, La Rochelle, France.|Sorbonne Université, IRD, Unité de Modélisation Mathématiques et Informatique des Systèmes Complexes, UMMISCO, F-93143, Bondy, France.|Université de Paris, Paris, France.|Sorbonne Université, IRD, Unité de Modélisation Mathématiques et Informatique des Systèmes Complexes, UMMISCO, F-93143, Bondy, France.', '-|France|France|France|France', 3, 'Algorithms|Animals|Computer Simulation|Culicidae|Disease Vectors|Epidemiologic Methods|Host-Parasite Interactions|Language|Models, Theoretical|Reproducibility of Results|Stochastic Processes', 'Journal Article', '10.1186/s12859-019-2843-0', '31185887', 'Mathematical and computational models are widely used to study the transmission, pathogenicity, and propagation of infectious diseases. Unfortunately, complex mathematical models are difficult to define, reuse and reproduce because they are composed of several concerns that are intertwined. The problem is even worse for computational models because the epidemiological concerns are also intertwined with low-level implementation details that are not easily accessible to non-computing scientists. Our goal is to make compartmental epidemiological models easier to define, reuse and reproduce by facilitating implementation of different simulation approaches with only very little programming knowledge.'),
(99069, 2019, 'bmc', 'Taxonomy based performance metrics for evaluating taxonomic assignment methods', 'Chen, Chung-Yen|Tang, Sen-Lin|Chou, Seng-Cho T', 'Department of Information Management, National Taiwan University, Taipei, 106, Taiwan.|Biodiversity Research Center, Academia Sinica, Taipei, 115, Taiwan.|Department of Information Management, National Taiwan University, Taipei, 106, Taiwan. chou@ntu.edu.tw.', '-|-|-', 0, 'Bacteria|Classification|Databases, Genetic|Metagenomics|Microbiota|Phylogeny|RNA, Ribosomal, 16S|RNA, Ribosomal, 18S', 'Journal Article', '10.1186/s12859-019-2896-0', '31185897', 'Metagenomics experiments often make inferences about microbial communities by sequencing 16S and 18S rRNA, and taxonomic assignment is a fundamental step in such studies. This paper addresses the weaknesses in two types of metrics commonly used by previous studies for measuring the performance of existing taxonomic assignment methods: Sequence count based metrics and Binary error measurement. These metrics made performance evaluation results biased, less informative and mutually incomparable.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(99070, 2019, 'bmc', 'WellInverter: a web application for the analysis of fluorescent reporter gene data', 'Martin, Yannick|Page, Michel|Blanchet, Christophe|de Jong, Hidde', 'Univ. Grenoble Alpes, Inria, Grenoble, 38000, France.|Univ. Grenoble Alpes, Inria, Grenoble, 38000, France.|Institut Français de Bioinformatique, IFB-core, Orsay, 91403, France.|Univ. Grenoble Alpes, Inria, Grenoble, 38000, France. hidde.de-jong@inria.fr.', 'France|France|France|France', 0, 'Algorithms|Computational Biology|Escherichia coli|Fluorescence|Genes, Bacterial|Genes, Reporter|Internet|Promoter Regions, Genetic|Software|User-Computer Interface', 'Journal Article', '10.1186/s12859-019-2920-4', '31185910', 'Fluorescent reporter genes have become widely used for monitoring gene expression in living cells. When a microbial strain carrying a reporter gene is grown in a microplate reader, the fluorescence and the absorbance (optical density) of the culture can be automatically measured every few minutes in a highly parallelized way. The extraction of useful information from the resulting large amounts of data is not easy to achieve, because the fluorescence and absorbance measurements are only indirectly related to promoter activities and protein concentrations, requiring mathematical models of the expression of reporter genes for their interpretation. Although the principles of the analysis of reporter gene data are well-established today, there is a lack of general-purpose bioinformatics tools based on generic measurement models and sound inference procedures. This has motivated the development of WellInverter, a web application based on well-known methods for regularized linear inversion.'),
(99071, 2019, 'bmc', 'Bayesian model selection for the Drosophila gap gene network', 'Zubair, Asif|Rosen, I Gary|Nuzhdin, Sergey V|Marjoram, Paul', 'Molecular and Computational Biology, USC, 1050 Childs Way, Los Angeles, CA 90089-2532, US. asifzuba@usc.edu.|Department of Mathematics, USC, 3620 S. Vermont Ave., Los Angeles, CA 90089-2532, US.|Molecular and Computational Biology, USC, 1050 Childs Way, Los Angeles, CA 90089-2532, US.|Molecular and Computational Biology, USC, 1050 Childs Way, Los Angeles, CA 90089-2532, US.', '-|-|-|-', 0, 'Animals|Bayes Theorem|Drosophila Proteins|Drosophila melanogaster|Gene Expression Profiling|Gene Expression Regulation, Developmental|Gene Regulatory Networks|Likelihood Functions|Markov Chains|Models, Genetic|Monte Carlo Method', 'Journal Article', '10.1186/s12859-019-2888-0', '31195954', 'The gap gene system controls the early cascade of the segmentation pathway in Drosophila melanogaster as well as other insects. Owing to its tractability and key role in embryo patterning, this system has been the focus for both computational modelers and experimentalists. The gap gene expression dynamics can be considered strictly as a one-dimensional process and modeled as a system of reaction-diffusion equations. While substantial progress has been made in modeling this phenomenon, there still remains a deficit of approaches to evaluate competing hypotheses. Most of the model development has happened in isolation and there has been little attempt to compare candidate models.'),
(99072, 2019, 'bmc', 'Identification of critical connectors in the directed reaction-centric graphs of microbial metabolic networks', 'Kim, Eun-Youn|Ashlock, Daniel|Yoon, Sung Ho', 'School of Basic Sciences, Hanbat National University, Daejeon, 34158, Republic of Korea.|Department of Mathematics and Statistics, the University of Guelph, Guelph, Ontario, N1G 2W1, Canada.|Department of Bioscience and Biotechnology, Konkuk University, Seoul, 05029, Republic of Korea. syoon@konkuk.ac.kr.', 'United Kingdom|Canada|United Kingdom', 0, 'Bacteria|Escherichia coli|Metabolic Networks and Pathways', 'Journal Article', '10.1186/s12859-019-2897-z', '31195955', 'Detection of central nodes in asymmetrically directed biological networks depends on centrality metrics quantifying individual nodes importance in a network. In topological analyses on metabolic networks, various centrality metrics have been mostly applied to metabolite-centric graphs. However, centrality metrics including those not depending on high connections are largely unexplored for directed reaction-centric graphs.'),
(99073, 2019, 'bmc', 'Difficulty in inferring microbial community structure based on co-occurrence network approaches', 'Hirano, Hokuto|Takemoto, Kazuhiro', 'Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, Fukuoka, 820-8502, Japan.|Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, Fukuoka, 820-8502, Japan. takemoto@bio.kyutech.ac.jp.', 'Japan|Japan', 8, 'Ecosystem|Microbiota|Models, Biological', 'Journal Article', '10.1186/s12859-019-2915-1', '31195956', 'Co-occurrence networks-ecological associations between sampled populations of microbial communities inferred from taxonomic composition data obtained from high-throughput sequencing techniques-are widely used in microbial ecology. Several co-occurrence network methods have been proposed. Co-occurrence network methods only infer ecological associations and are often used to discuss species interactions. However, validity of this application of co-occurrence network methods is currently debated. In particular, they simply evaluate using parametric statistical models, even though microbial compositions are determined through population dynamics.'),
(99074, 2019, 'bmc', 'Prediction of activity and specificity of CRISPR-Cpf1 using convolutional deep learning neural networks', 'Luo, Jiesi|Chen, Wei|Xue, Li|Tang, Bin', 'Department of Pharmacology, Key Laboratory for Aging and Regenerative Medicine, School of Pharmacy, Southwest Medical University, Luzhou, Sichuan, China. ljs@swmu.edu.cn.|Center for Bioinformatics and Systems Biology and Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA.|School of Public Health, Southwest Medical University, Luzhou, Sichuan, China.|Basic Medical College of Southwest Medical University, Luzhou, Sichuan, China. bt@swmu.edu.cn.', 'China|United States|China|China', 0, 'Algorithms|Base Sequence|CRISPR-Cas Systems|Deep Learning|Endonucleases|Neural Networks (Computer)|RNA, Guide', 'Journal Article', '10.1186/s12859-019-2939-6', '31195957', 'CRISPR-Cpf1 has recently been reported as another RNA-guided endonuclease of class 2 CRISPR-Cas system, which expands the molecular biology toolkit for genome editing. However, most of the online tools and applications to date have been developed primarily for the Cas9. There are a limited number of tools available for the Cpf1.'),
(99075, 2019, 'bmc', 'CLoDSA: a tool for augmentation in classification, localization, detection, semantic segmentation and instance segmentation tasks', 'Casado-García, Ángela|Domínguez, César|García-Domínguez, Manuel|Heras, Jónathan|Inés, Adrián|Mata, Eloy|Pascual, Vico', 'Department of Mathematics and Computer Science, University of La Rioja, Ed. CCT. C/ Madre de Dios 53, Logroño, 26006, Spain.|Department of Mathematics and Computer Science, University of La Rioja, Ed. CCT. C/ Madre de Dios 53, Logroño, 26006, Spain.|Department of Mathematics and Computer Science, University of La Rioja, Ed. CCT. C/ Madre de Dios 53, Logroño, 26006, Spain.|Department of Mathematics and Computer Science, University of La Rioja, Ed. CCT. C/ Madre de Dios 53, Logroño, 26006, Spain. jonathan.heras@unirioja.es.|Department of Mathematics and Computer Science, University of La Rioja, Ed. CCT. C/ Madre de Dios 53, Logroño, 26006, Spain.|Department of Mathematics and Computer Science, University of La Rioja, Ed. CCT. C/ Madre de Dios 53, Logroño, 26006, Spain.|Department of Mathematics and Computer Science, University of La Rioja, Ed. CCT. C/ Madre de Dios 53, Logroño, 26006, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain', 6, 'Algorithms|Animals|Deep Learning|Humans|Image Processing, Computer-Assisted|Malaria|Models, Theoretical|Parasites|Semantics', 'Journal Article', '10.1186/s12859-019-2931-1', '31195959', 'Deep learning techniques have been successfully applied to bioimaging problems, however, these methods are highly data demanding. An approach to deal with the lack of data and avoid overfitting is the application of data augmentation, a technique that generates new training samples from the original dataset by applying different kinds of transformations. Several tools exist to apply data augmentation in the context of image classification, but it does not exist a similar tool for the problems of localization, detection, semantic segmentation or instance segmentation that works not only with 2 dimensional images but also with multi-dimensional images (such as stacks or videos).'),
(99076, 2019, 'bmc', 'Statistical representation models for mutation information within genomic data', 'xc3x96zcan xc5x9eimxc5x9fek, N xc3x96zlem|xc3x96zgxc3xbcr, Arzucan|Gxc3xbcrgen, Fikret', 'Department of Computer Engineering, Boxc4x9fazixc3xa7i University, xc4xb0stanbul, Turkey.|Department of Computer Engineering, Boxc4x9fazixc3xa7i University, xc4xb0stanbul, Turkey. arzucan.ozgur@boun.edu.tr.|Department of Computer Engineering, Boxc4x9fazixc3xa7i University, xc4xb0stanbul, Turkey. gurgen@boun.edu.tr.', 'Turkey|Turkey|Turkey', 0, 'Databases, Genetic|Exons|Genomics|Humans|Introns|Machine Learning|Models, Genetic|Models, Statistical|Mutation|Neoplasms|Neural Networks (Computer)', 'Journal Article', '10.1186/s12859-019-2868-4', '31195961', 'As DNA sequencing technologies are improving and getting cheaper, genomic data can be utilized for diagnosis of many diseases such as cancer. Human raw genome data is huge in size for computational systems. Therefore, there is a need for a compact and accurate representation of the valuable information in DNA. The occurrence of complex genetic disorders often results from multiple gene mutations. The effect of each mutation is not equal for the development of a disease. Inspired from the field of information retrieval, we propose using the term frequency (tf) and BM25 term weighting measures with the inverse document frequency (idf) and relevance frequency (rf) measures to weight genes based on their mutations. The underlying assumption is that the more mutations a gene has in patients with a certain disease and the less mutations it has in other patients, the more discriminative that gene is.'),
(99077, 2019, 'bmc', 'pcaExplorer: an R/Bioconductor package for interacting with RNA-seq principal components', 'Marini, Federico|Binder, Harald', 'Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center of the Johannes Gutenberg University Mainz, Obere Zahlbacher Str. 69, Mainz, 55131, Germany. marinif@uni-mainz.de.|Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Stefan-Meier-Str. 26, Freiburg, 79104, Germany.', 'Germany|Germany', 0, 'Base Sequence|Data Curation|Humans|Principal Component Analysis|RNA|Reproducibility of Results|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/s12859-019-2879-1', '31195976', 'Principal component analysis (PCA) is frequently used in genomics applications for quality assessment and exploratory analysis in high-dimensional data, such as RNA sequencing (RNA-seq) gene expression assays. Despite the availability of many software packages developed for this purpose, an interactive and comprehensive interface for performing these operations is lacking.'),
(99078, 2019, 'bmc', 'AutoCryoPicker: an unsupervised learning approach for fully automated single particle picking in Cryo-EM images', 'Al-Azzawi, Adil|Ouadou, Anes|Tanner, John J|Cheng, Jianlin', 'Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO, 65211, USA.|Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO, 65211, USA.|Departments of Biochemistry and Chemistry, University of Missouri, Columbia, MO, 65211-2060, USA.|Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO, 65211, USA. chengji@missouri.edu.', 'United States|United States|United States|United States', 4, 'Algorithms|Automation|Cluster Analysis|Cryoelectron Microscopy|Image Processing, Computer-Assisted|Software|Unsupervised Machine Learning', 'Journal Article', '10.1186/s12859-019-2926-y', '31195977', 'An important task of macromolecular structure determination by cryo-electron microscopy (cryo-EM) is the identification of single particles in micrographs (particle picking). Due to the necessity of human involvement in the process, current particle picking techniques are time consuming and often result in many false positives and negatives. Adjusting the parameters to eliminate false positives often excludes true particles in certain orientations. The supervised machine learning (e.g. deep learning) methods for particle picking often need a large training dataset, which requires extensive manual annotation. Other reference-dependent methods rely on low-resolution templates for particle detection, matching and picking, and therefore, are not fully automated. These issues motivate us to develop a fully automated, unbiased framework for particle picking.'),
(99079, 2019, 'bmc', 'fastJT: An R package for robust and efficient feature selection for machine learning and genome-wide association studies', 'Lin, Jiaxing|Sibley, Alexander|Shterev, Ivo|Nixon, Andrew|Innocenti, Federico|Chan, Cliburn|Owzar, Kouros', 'Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA.|Duke Cancer Institute, Duke University Medical Center, Durham, NC, USA.|Duke Human Vaccine Institute, Duke University Medical Center, Durham, NC, USA.|Duke Cancer Institute, Duke University Medical Center, Durham, NC, USA.|Division of Pharmacotherapy and Experimental Therapeutics, Chapel Hill, NC, USA.|Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA.|Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA. Kouros.Owzar@duke.edu.', 'United States|United States|United States|United States|United States|United States|United States', 1, 'Algorithms|Blood Proteins|Computer Simulation|Genome-Wide Association Study|Machine Learning|Polymorphism, Single Nucleotide|Quantitative Trait, Heritable', 'Journal Article', '10.1186/s12859-019-2869-3', '31195980', 'Parametric feature selection methods for machine learning and association studies based on genetic data are not robust with respect to outliers or influential observations. While rank-based, distribution-free statistics offer a robust alternative to parametric methods, their practical utility can be limited, as they demand significant computational resources when analyzing high-dimensional data. For genetic studies that seek to identify variants, the hypothesis is constrained, since it is typically assumed that the effect of the genotype on the phenotype is monotone (e.g., an additive genetic effect). Similarly, predictors for machine learning applications may have natural ordering constraints. Cross-validation for feature selection in these high-dimensional contexts necessitates highly efficient computational algorithms for the robust evaluation of many features.'),
(99080, 2019, 'bmc', 'A dropout-regularized classifier development approach optimized for precision medicine test discovery from omics data', 'Roder, Joanna|Oliveira, Carlos|Net, Lelia|Tsypin, Maxim|Linstid, Benjamin|Roder, Heinrich', '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.|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.', 'United States|United States|United States|United States|United States|United States', 2, 'Algorithms|Area Under Curve|Carcinoma, Non-Small-Cell Lung|Databases, Genetic|Genomics|Humans|Lung Neoplasms|Machine Learning|Male|Precision Medicine|Prostatic Neoplasms|Survival Analysis', 'Journal Article', '10.1186/s12859-019-2922-2', '31196002', 'Modern genomic and proteomic profiling methods produce large amounts of data from tissue and blood-based samples that are of potential utility for improving patient care. However, the design of precision medicine tests for unmet clinical needs from this information in the small cohorts available for test discovery remains a challenging task. Obtaining reliable performance assessments at the earliest stages of test development can also be problematic. We describe a novel approach to classifier development designed to create clinically useful tests together with reliable estimates of their performance. The method incorporates elements of traditional and modern machine learning to facilitate the use of cohorts where the number of samples is less than the number of measured patient attributes. It is based on a hierarchy of classification and information abstraction and combines boosting, bagging, and strong dropout regularization.'),
(99081, 2019, 'bmc', 'Enhancing ontology-driven diagnostic reasoning with a symptom-dependency-aware Naïve Bayes classifier', 'Shen, Ying|Li, Yaliang|Zheng, Hai-Tao|Tang, Buzhou|Yang, Min', 'School of Electronics and Computer Engineering, Peking University Shenzhen Graduate School, Shenzhen, 518055, Peoples Republic of China.|Alibaba Group, Bellevue, WA, USA.|School of Information Science and Technology, Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055, Peoples Republic of China.|Harbin Institute of Technology (Shenzhen), Shenzhen, 518055, Peoples Republic of China.|SIAT, Chinese Academy of Sciences, Shenzhen, 518055, Peoples Republic of China. min.yang@siat.ac.cn.', 'China|United States|China|China|China', 5, 'Algorithms|Area Under Curve|Bayes Theorem|Diagnostic Techniques and Procedures|Disease|Electronic Health Records|Humans|Knowledge Bases|Probability|ROC Curve', 'Journal Article', '10.1186/s12859-019-2924-0', '31196129', 'Ontology has attracted substantial attention from both academia and industry. Handling uncertainty reasoning is important in researching ontology. For example, when a patient is suffering from cirrhosis, the appearance of abdominal vein varices is four times more likely than the presence of bitter taste. Such medical knowledge is crucial for decision-making in various medical applications but is missing from existing medical ontologies. In this paper, we aim to discover medical knowledge probabilities from electronic medical record (EMR) texts to enrich ontologies. First, we build an ontology by identifying meaningful entity mentions from EMRs. Then, we propose a symptom-dependency-aware naïve Bayes classifier (SDNB) that is based on the assumption that there is a level of dependency among symptoms. To ensure the accuracy of the diagnostic classification, we incorporate the probability of a disease into the ontology via innovative approaches.'),
(99082, 2019, 'bmc', 'Filtering procedures for untargeted LC-MS metabolomics data', 'Schiffman, Courtney|Petrick, Lauren|Perttula, Kelsi|Yano, Yukiko|Carlsson, Henrik|Whitehead, Todd|Metayer, Catherine|Hayes, Josie|Rappaport, Stephen|Dudoit, Sandrine', 'Division of Biostatistics, UC Berkeley, Berkeley, 94720, USA. courtneys@berkeley.edu.|The Senator Frank R. Lautenberg Environmental Health Sciences Laboratory, Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, USA.|Division of Environmental Health Sciences, UC Berkeley, Berkeley, 94720, USA.|Division of Environmental Health Sciences, UC Berkeley, Berkeley, 94720, USA.|Division of Environmental Health Sciences, UC Berkeley, Berkeley, 94720, USA.|Division of Epidemiology, UC Berkeley, Berkeley, 94720, USA.|Division of Epidemiology, UC Berkeley, Berkeley, 94720, USA.|Division of Environmental Health Sciences, UC Berkeley, Berkeley, 94720, USA.|Division of Environmental Health Sciences, UC Berkeley, Berkeley, 94720, USA.|Division of Biostatistics, UC Berkeley, Berkeley, 94720, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 7, 'Chromatography, Liquid|Colorectal Neoplasms|Databases as Topic|Humans|Metabolic Networks and Pathways|Metabolomics|Tandem Mass Spectrometry', 'Journal Article', '10.1186/s12859-019-2871-9', '31200644', 'Untargeted metabolomics datasets contain large proportions of uninformative features that can impede subsequent statistical analysis such as biomarker discovery and metabolic pathway analysis. Thus, there is a need for versatile and data-adaptive methods for filtering data prior to investigating the underlying biological phenomena. Here, we propose a data-adaptive pipeline for filtering metabolomics data that are generated by liquid chromatography-mass spectrometry (LC-MS) platforms. Our data-adaptive pipeline includes novel methods for filtering features based on blank samples, proportions of missing values, and estimated intra-class correlation coefficients.'),
(99083, 2019, 'bmc', 'Performance assessment of variant calling pipelines using human whole exome sequencing and simulated data', 'Kumaran, Manojkumar|Subramanian, Umadevi|Devarajan, Bharanidharan', 'Department of Bioinformatics, Aravind Medical Research Foundation, Madurai, Tamil Nadu, 625020, India.|Department of Bioinformatics, Aravind Medical Research Foundation, Madurai, Tamil Nadu, 625020, India.|Department of Bioinformatics, Aravind Medical Research Foundation, Madurai, Tamil Nadu, 625020, India. bharanid@gmail.com.', 'India|India|India', 2, 'Base Pairing|Computer Simulation|Exome|Genotype|High-Throughput Nucleotide Sequencing|Humans|INDEL Mutation|Polymorphism, Single Nucleotide|ROC Curve|Whole Exome Sequencing', 'Journal Article', '10.1186/s12859-019-2928-9', '31208315', 'Whole exome sequencing (WES) is a cost-effective method that identifies clinical variants but it demands accurate variant caller tools. Currently available tools have variable accuracy in predicting specific clinical variants. But it may be possible to find the best combination of aligner-variant caller tools for detecting accurate single nucleotide variants (SNVs) and small insertion and deletion (InDels) separately. Moreover, many important aspects of InDel detection are overlooked while comparing the performance of tools, particularly its base pair length.'),
(99084, 2019, 'bmc', 'Periodicity and dosage optimization of an RNAi model in eukaryotes cells', 'Ma, Tongle|Pei, Yongzhen|Li, Changguo|Zhu, Meixia', 'School of Computer Science and Technology Tianjin Polytechnic University, Tianjin, 300387, China.|School of Computer Science and Technology Tianjin Polytechnic University, Tianjin, 300387, China. yongzhenpei@163.com.|Department of Basic Science, Army Military Transportation University, Tianjin, 300361, China.|School of Computer Science and Technology Tianjin Polytechnic University, Tianjin, 300387, China.', 'China|China|China|China', 3, 'Animals|Computer Simulation|Eukaryotic Cells|Humans|Models, Genetic|Periodicity|RNA Interference|RNA, Messenger|RNA, Small Interfering', 'Journal Article', '10.1186/s12859-019-2925-z', '31208316', 'As a highly efficient and specific gene regulation technology, RNAi has broad application fields and good prospects. The effect of RNAi enhances as the dosage of siRNA increases, while an exorbitant siRNA dosage will inhibit the RNAi effect. So it is crucial to formulate a dose-effect model to describe the degradation effects of the target mRNA at different siRNA dosages.'),
(99085, 2019, 'bmc', 'Aneuvis: web-based exploration of numerical chromosomal variation in single cells', 'Piqué, Daniel G|Andriani, Grasiella A|Maggi, Elaine|Zimmerman, Samuel E|Greally, John M|Montagna, Cristina|Mar, Jessica C', 'Department of Systems Biology, Albert Einstein College of Medicine, Bronx, NY, 10461, USA.|Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA.|Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA.|Department of Systems Biology, Albert Einstein College of Medicine, Bronx, NY, 10461, USA.|Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA.|Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA. cristina.montagna@einstein.yu.edu.|Department of Systems Biology, Albert Einstein College of Medicine, Bronx, NY, 10461, USA. j.mar@uq.edu.au.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Cell Line|Chromosomes|DNA Copy Number Variations|Humans|Internet|Single-Cell Analysis|Software|User-Computer Interface|Whole Genome Sequencing', 'Journal Article', '10.1186/s12859-019-2842-1', '31208319', 'Numerical chromosomal variation is a hallmark of populations of malignant cells. Identifying the factors that promote numerical chromosomal variation is important for understanding mechanisms of carcinogenesis. However, the ability to quantify and visualize differences in chromosome number between experimentally-defined groups (e.g. control vs treated) obtained from single-cell experiments is currently limited by the lack of user-friendly software.'),
(99086, 2019, 'bmc', 'Analysis and prediction of human acetylation using a cascade classifier based on support vector machine', 'Ning, Qiao|Yu, Miao|Ji, Jinchao|Ma, Zhiqiang|Zhao, Xiaowei', 'School of Information Science and Technology, Northeast Normal University, Changchun, 130117, China.|School of Information Science and Technology, Northeast Normal University, Changchun, 130117, China.|School of Information Science and Technology, Northeast Normal University, Changchun, 130117, China.|School of Information Science and Technology, Northeast Normal University, Changchun, 130117, China. zhaoxw303@nenu.edu.cn.|School of Information Science and Technology, Northeast Normal University, Changchun, 130117, China. zhaoxw303@nenu.edu.cn.', 'China|China|China|China|China', 1, 'Acetylation|Amino Acid Sequence|Animals|Computational Biology|Databases, Protein|Gene Ontology|Humans|Lysine|Mice|Molecular Sequence Annotation|Position-Specific Scoring Matrices|Protein Processing, Post-Translational|Protein Structure, Secondary|Proteins|Rats|Support Vector Machine', 'Journal Article', '10.1186/s12859-019-2938-7', '31208321', 'Acetylation on lysine is a widespread post-translational modification which is reversible and plays a crucial role in some biological activities. To better understand the mechanism, it is necessary to identify acetylation sites in proteins accurately. Computational methods are popular because they are more convenient and faster than experimental methods. In this study, we proposed a new computational method to predict acetylation sites in human by combining sequence features and structural features including physicochemical property (PCP), position specific score matrix (PSSM), auto covariation (AC), residue composition (RC), secondary structure (SS) and accessible surface area (ASA), which can well characterize the information of acetylated lysine sites. Besides, a two-step feature selection was applied, which combined mRMR and IFS. It finally trained a cascade classifier based on SVM, which successfully solved the imbalance between positive samples and negative samples and covered all negative sample information.'),
(99087, 2019, 'bmc', 'Laboratory information management software for engineered mini-protein therapeutic workflow', 'Brusniak, Mi-Youn|Ramos, Hector|Lee, Bernard|Olson, James M', 'Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, WA, 98109, USA. mbrusnia@fredhutch.org.|Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, WA, 98109, USA.|LabKey Software, 617 Eastlake Ave E #400, Seattle, WA, 98109, USA.|Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, WA, 98109, USA.', 'United States|United States|United States|United States', 0, 'Automation|Humans|Information Management|Laboratories|Protein Engineering|Proteins|Software|User-Computer Interface|Workflow', 'Journal Article', '10.1186/s12859-019-2935-x', '31208323', 'Protein based therapeutics are one of the fastest growing classes of novel medical interventions in areas such as cancer, infectious disease, and inflammation. Protein engineering plays an important role in the optimization of desired therapeutic properties such as reducing immunogenicity, increasing stability for storage, increasing target specificity, etc. One category of protein therapeutics is nature-inspired bioengineered cystine-dense peptides (CDPs) for various biological targets. These engineered proteins are often further modified by synthetic chemistry. For example, candidate mini-proteins can be conjugated into active small molecule drugs. We refer to modified mini-proteins as Optides (Optimized peptides). To efficiently serve the multidisciplinary lab scientists with varied therapeutic portfolio research goals in a non-commercial setting, a cost effective extendable laboratory information management system (LIMS) is/was needed.'),
(99088, 2019, 'bmc', 'Machine learning with the TCGA-HNSC dataset: improving usability by addressing inconsistency, sparsity, and high-dimensionality', 'Rendleman, Michael C|Buatti, John M|Braun, Terry A|Smith, Brian J|Nwakama, Chibuzo|Beichel, Reinhard R|Brown, Bart|Casavant, Thomas L', 'Department of Electrical and Computer Engineering, Center for Bioinformatics and Computational Biology, University of Iowa, 5017 Seamans Center, Iowa City, IA, 52242, USA. michael-rendleman@uiowa.edu.|Department of Radiation Oncology, Carver College of Medicine, University of Iowa Carver College of Medicine, LL-W Pomerantz Family Pavilion, 200 Hawkins Drive, Iowa City, IA, 52242-1089, USA.|Department of Biomedical Engineering, Center for Bioinformatics and Computational Biology, University of Iowa, 5017 Seamans Center, Iowa City, IA, 52242, USA.|Department of Biostatistics, University of Iowa, 145 N. Riverside Drive, 100 CPHB, Iowa City, IA, 52242, USA.|Department of Electrical and Computer Engineering, Center for Bioinformatics and Computational Biology, University of Iowa, 5017 Seamans Center, Iowa City, IA, 52242, USA.|Iowa Institute for Biomedical Imaging, Department of Electrical and Computer Engineering, Department of Internal Medicine, The University of Iowa, Iowa City, 52242, IA, USA.|Center for Bioinformatics and Computational Biology, University of Iowa, 5017 Seamans Center, Iowa City, IA, 52242, USA.|Department of Electrical and Computer Engineering, Center for Bioinformatics and Computational Biology, University of Iowa, 5017 Seamans Center, Iowa City, IA, 52242, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 4, 'Algorithms|Area Under Curve|Databases, Genetic|Gene Ontology|Humans|Machine Learning|Principal Component Analysis|RNA, Neoplasm|Squamous Cell Carcinoma of Head and Neck', 'Journal Article', '10.1186/s12859-019-2929-8', '31208324', 'In the era of precision oncology and publicly available datasets, the amount of information available for each patient case has dramatically increased. From clinical variables and PET-CT radiomics measures to DNA-variant and RNA expression profiles, such a wide variety of data presents a multitude of challenges. Large clinical datasets are subject to sparsely and/or inconsistently populated fields. Corresponding sequencing profiles can suffer from the problem of high-dimensionality, where making useful inferences can be difficult without correspondingly large numbers of instances. In this paper we report a novel deployment of machine learning techniques to handle data sparsity and high dimensionality, while evaluating potential biomarkers in the form of unsupervised transformations of RNA data. We apply preprocessing, MICE imputation, and sparse principal component analysis (SPCA) to improve the usability of more than 500 patient cases from the TCGA-HNSC dataset for enhancing future oncological decision support for Head and Neck Squamous Cell Carcinoma (HNSCC).'),
(99089, 2019, 'bmc', 'FastProNGS: fast preprocessing of next-generation sequencing reads', 'Liu, Xiaoshuang|Yan, Zhenhe|Wu, Chao|Yang, Yang|Li, Xiaomin|Zhang, Guangxin', 'Megagenomics Corporation, Beijing, China.|Megagenomics Corporation, Beijing, China.|National Renewable Energy Laboratory CO, Colorado, USA.|Megagenomics Corporation, Beijing, China.|Megagenomics Corporation, Beijing, China.|Megagenomics Corporation, Beijing, China. zhangguangxin@megagenomics.cn.', 'China|China|United States|China|China|China', 1, 'High-Throughput Nucleotide Sequencing|Humans|Quality Control|Software|Time Factors|Workflow', 'Journal Article', '10.1186/s12859-019-2936-9', '31208325', 'Next-generation sequencing technology is developing rapidly and the vast amount of data that is generated needs to be preprocessed for downstream analyses. However, until now, software that can efficiently make all the quality assessments and filtration of raw data is still lacking.'),
(99090, 2019, 'bmc', 'Combining learning and constraints for genome-wide protein annotation', 'Teso, Stefano|Masera, Luca|Diligenti, Michelangelo|Passerini, Andrea', 'Computer Science Department, KULeuven, Celestijnenlaan 200 A bus 2402, Leuven, 3001, Belgium.|Department of Information Engineering and Computer Science, University of Trento, Via Sommarive, 5, Povo di Trento, 38123, Italy.|Department of Information Engineering and Mathematics, University of Siena, San Niccolò, via Roma, 56, Siena, 53100, Italy.|Department of Information Engineering and Computer Science, University of Trento, Via Sommarive, 5, Povo di Trento, 38123, Italy. andrea.passerini@unitn.it.', 'Belgium|Italy|Italy|Italy', 0, 'Algorithms|Decision Making|Gene Ontology|Genome, Fungal|Genomics|Molecular Sequence Annotation|Proteins|Saccharomyces cerevisiae', 'Journal Article', '10.1186/s12859-019-2875-5', '31208327', 'The advent of high-throughput experimental techniques paved the way to genome-wide computational analysis and predictive annotation studies. When considering the joint annotation of a large set of related entities, like all proteins of a certain genome, many candidate annotations could be inconsistent, or very unlikely, given the existing knowledge. A sound predictive framework capable of accounting for this type of constraints in making predictions could substantially contribute to the quality of machine-generated annotations at a genomic scale.'),
(99091, 2019, 'bmc', 'DeepACLSTM: deep asymmetric convolutional long short-term memory neural models for protein secondary structure prediction', 'Guo, Yanbu|Li, Weihua|Wang, Bingyi|Liu, Huiqing|Zhou, Dongming', 'School of Information Science and Engineering, Yunnan University, Kunming, 650091, China.|School of Information Science and Engineering, Yunnan University, Kunming, 650091, China. liweihua@ynu.edu.cn.|Research Institute of Resource Insects, Chinese Academy of Forestry, Kunming, 650224, China. wbykm@aliyun.com.|School of Information Science and Engineering, Yunnan University, Kunming, 650091, China.|School of Information Science and Engineering, Yunnan University, Kunming, 650091, China.', 'China|China|China|China|China', 6, 'Algorithms|Deep Learning|Models, Theoretical|Neural Networks (Computer)|Protein Domains|Protein Structure, Secondary|Proteins', 'Journal Article', '10.1186/s12859-019-2940-0', '31208331', 'Protein secondary structure (PSS) is critical to further predict the tertiary structure, understand protein function and design drugs. However, experimental techniques of PSS are time consuming and expensive, and thus its very urgent to develop efficient computational approaches for predicting PSS based on sequence information alone. Moreover, the feature matrix of a protein contains two dimensions: the amino-acid residue dimension and the feature vector dimension. Existing deep learning based methods have achieved remarkable performances of PSS prediction, but the methods often utilize the features from the amino-acid dimension. Thus, there is still room to improve computational methods of PSS prediction.'),
(99092, 2019, 'bmc', 'DDmap: a MATLAB package for the double digest problem using multiple genetic operators', 'Wang, Licheng|Suo, Jingwen|Pan, Yun|Li, Lixiang', 'State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, 10 West Tucheng Road, Haidian District, Beijing, 100876, China.|State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, 10 West Tucheng Road, Haidian District, Beijing, 100876, China.|School of Computer Science, Communication University of China, 1 East of Dingfuzhuang Street, Chaoyang District, Beijing, 100024, China. pany@cuc.edu.cn.|State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, 10 West Tucheng Road, Haidian District, Beijing, 100876, China.', 'China|China|China|China', 1, 'Algorithms|DNA|Operator Regions, Genetic|Physical Chromosome Mapping|Software', 'Journal Article', '10.1186/s12859-019-2862-x', '31215408', 'In computational biology, the physical mapping of DNA is a key problem. We know that the double digest problem (DDP) is NP-complete. Many algorithms have been proposed for solving the DDP, although it is still far from being resolved.'),
(99093, 2019, 'bmc', 'Network-based prediction of polygenic disease genes involved in cell motility', 'Bern, Miriam|King, Alexander|Applewhite, Derek A|Ritz, Anna', 'Biology Department, Reed College, Portland, OR, USA.|Biology Department, Reed College, Portland, OR, USA.|Biology Department, Reed College, Portland, OR, USA.|Biology Department, Reed College, Portland, OR, USA. aritz@reed.edu.', 'United States|United States|United States|United States', 0, 'Algorithms|Autistic Disorder|Cell Movement|Disease|Gene Regulatory Networks|Genetic Association Studies|Humans|Machine Learning|Multifactorial Inheritance|Phenotype|ROC Curve|Reproducibility of Results|Schizophrenia', 'Journal Article', '10.1186/s12859-019-2834-1', '31216978', 'Schizophrenia and autism are examples of polygenic diseases caused by a multitude of genetic variants, many of which are still poorly understood. Recently, both diseases have been associated with disrupted neuron motility and migration patterns, suggesting that aberrant cell motility is a phenotype for these neurological diseases.'),
(99094, 2019, 'bmc', 'A stochastic model of size control in the budding yeast cell cycle', 'Ahmadian, Mansooreh|Tyson, John J|Cao, Yang', 'Department of Computer Science, Virginia Tech, Blacksburg, VA, USA.|Department of Biological Sciences, Virginia Tech, Blacksburg, VA, USA.|Department of Computer Science, Virginia Tech, Blacksburg, VA, USA. ycao@vt.edu.', 'United States|United States|United States', 3, 'Cell Cycle|G1 Phase|Gene Expression Regulation, Fungal|Models, Biological|Mutation|Phenotype|Ploidies|RNA, Messenger|Saccharomyces cerevisiae|Stochastic Processes', 'Journal Article', '10.1186/s12859-019-2839-9', '31216979', 'Cell size is a key characteristic that significantly affects many aspects of cellular physiology. There are specific control mechanisms during cell cycle that maintain the cell size within a range from generation to generation. Such control mechanisms introduce substantial variabilities to important properties of the cell cycle such as growth and division. To quantitatively study the effect of such variability in progression through cell cycle, detailed stochastic models are required.'),
(99095, 2019, 'bmc', 'Recursive model for dose-time responses in pharmacological studies', 'Dhruba, Saugato Rahman|Rahman, Aminur|Rahman, Raziur|Ghosh, Souparno|Pal, Ranadip', 'Department of Electrical and Computer Engineering, Texas Tech University, 1012 Boston Ave, Lubbock, 79409, TX, USA.|Department of Mathematics and Statistics, Texas Tech University, 1108 Memorial Circle, Lubbock, 79409, TX, USA.|Department of Electrical and Computer Engineering, Texas Tech University, 1012 Boston Ave, Lubbock, 79409, TX, USA.|Department of Mathematics and Statistics, Texas Tech University, 1108 Memorial Circle, Lubbock, 79409, TX, USA. souparno.ghosh@ttu.edu.|Department of Electrical and Computer Engineering, Texas Tech University, 1012 Boston Ave, Lubbock, 79409, TX, USA.', 'United States|United States|United States|United States|United States', 1, 'Computer Simulation|Databases as Topic|Dose-Response Relationship, Drug|Humans|Models, Theoretical|Pharmacology|Time Factors', 'Journal Article', '10.1186/s12859-019-2831-4', '31216980', 'Clinical studies often track dose-response curves of subjects over time. One can easily model the dose-response curve at each time point with Hill equation, but such a model fails to capture the temporal evolution of the curves. On the other hand, one can use Gompertz equation to model the temporal behaviors at each dose without capturing the evolution of time curves across dosage.'),
(99096, 2019, 'bmc', 'Analysis and remedy of negativity problem in hybrid stochastic simulation algorithm and its application', 'Chen, Minghan|Cao, Yang', 'Department of Computer Science, Virginia Tech, Blacksburg, 24061, VA, USA.|Department of Computer Science, Virginia Tech, Blacksburg, 24061, VA, USA. ycao@cs.vt.edu.', 'United States|United States', 1, 'Algorithms|Caulobacter|Cell Cycle|Computer Simulation|Models, Theoretical|Nonlinear Dynamics|Probability|Stochastic Processes', 'Journal Article', '10.1186/s12859-019-2836-z', '31216983', 'The hybrid stochastic simulation algorithm, proposed by Haseltine and Rawlings (HR), is a combination of differential equations for traditional deterministic models and Gillespies algorithm (SSA) for stochastic models. The HR hybrid method can significantly improve the efficiency of stochastic simulations for multiscale biochemical networks. Previous studies on the accuracy analysis for a linear chain reaction system showed that the HR hybrid method is accurate if the scale difference between fast and slow reactions is above a certain threshold, regardless of population scales. However, the population of some reactant species might be driven negative if they are involved in both deterministic and stochastic systems.'),
(99097, 2019, 'bmc', 'A linear delay algorithm for enumerating all connected induced subgraphs', 'Alokshiya, Mohammed|Salem, Saeed|Abed, Fidaa', 'North Dakota State University, Fargo, ND 58102, USA.|North Dakota State University, Fargo, ND 58102, USA. saeed.salem@ndsu.edu.|University of Jeddah, Jeddah 23218, Saudi Arabia.', 'United States|United States|Saudi Arabia', 2, 'Algorithms|Humans|Protein Interaction Maps|Time Factors', 'Journal Article', '10.1186/s12859-019-2837-y', '31216984', 'Real biological and social data is increasingly being represented as graphs. Pattern-mining-based graph learning and analysis techniques report meaningful biological subnetworks that elucidate important interactions among entities. At the backbone of these algorithms is the enumeration of pattern space.'),
(99098, 2019, 'bmc', 'Cross-population analysis for functional characterization of type II diabetes variants', 'Elmansy, Dalia|Koyutürk, Mehmet', 'Department of Electrical Engineering and Computer Science, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH, 44106, USA. dfe4@case.edu.|Department of Electrical Engineering and Computer Science, Center for Proteomics and Bioinformatics, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH, 44106, USA.', 'United States|United States', 1, 'Asian Continental Ancestry Group|Diabetes Mellitus, Type 2|Ethnic Groups|Genetic Predisposition to Disease|Genome, Human|Genome-Wide Association Study|Humans|Male|Polymorphism, Single Nucleotide|Prostatic Neoplasms|Protein Interaction Maps', 'Journal Article', '10.1186/s12859-019-2835-0', '31216985', 'As Genome-Wide Association Studies (GWAS) have been increasingly used with data from various populations, it has been observed that data from different populations reveal different sets of Single Nucleotide Polymorphisms (SNPs) that are associated with the same disease. Using Type II Diabetes (T2D) as a test case, we develop measures and methods to characterize the functional overlap of SNPs associated with the same disease across populations.'),
(99099, 2019, 'bmc', 'Characterizing building blocks of resource constrained biological networks', 'Ren, Yuanfang|Ay, Ahmet|Dobra, Alin|Kahveci, Tamer', 'Computer and Information Science and Engineering, University of Florida, Gainesville, 32611, FL, USA.|Departments of Biology and Mathematics, Colgate University, Hamilton, 13346, NY, USA.|Computer and Information Science and Engineering, University of Florida, Gainesville, 32611, FL, USA.|Computer and Information Science and Engineering, University of Florida, Gainesville, 32611, FL, USA. tamer@cise.ufl.edu.', 'United States|United States|United States|United States', 1, 'Algorithms|Databases, Genetic|Gene Regulatory Networks|Genes, Fungal|Models, Biological|Oxidative Stress|Saccharomyces cerevisiae', 'Journal Article', '10.1186/s12859-019-2838-x', '31216986', 'Identification of motifs-recurrent and statistically significant patterns-in biological networks is the key to understand the design principles, and to infer governing mechanisms of biological systems. This, however, is a computationally challenging task. This task is further complicated as biological interactions depend on limited resources, i.e., a reaction takes place if the reactant molecule concentrations are above a certain threshold level. This biochemical property implies that network edges can participate in a limited number of motifs simultaneously. Existing motif counting methods ignore this problem. This simplification often leads to inaccurate motif counts (over- or under-estimates), and thus, wrong biological interpretations.'),
(99100, 2019, 'bmc', 'Optimal clustering with missing values', 'Boluki, Shahin|Zamani Dadaneh, Siamak|Qian, Xiaoning|Dougherty, Edward R', 'Department of Electrical and Computer Engineering, Texas A&M University, MS3128 TAMU, College Station, 77843, TX, USA.|Department of Electrical and Computer Engineering, Texas A&M University, MS3128 TAMU, College Station, 77843, TX, USA.|Department of Electrical and Computer Engineering, Texas A&M University, MS3128 TAMU, College Station, 77843, TX, USA.|Department of Electrical and Computer Engineering, Texas A&M University, MS3128 TAMU, College Station, 77843, TX, USA. edward@ece.tamu.edu.', 'United States|United States|United States|United States', 3, 'Algorithms|Breast Neoplasms|Cluster Analysis|Computer Simulation|Female|Gene Expression Profiling|Humans|Models, Theoretical|Normal Distribution|Probability', 'Journal Article', '10.1186/s12859-019-2832-3', '31216989', 'Missing values frequently arise in modern biomedical studies due to various reasons, including missing tests or complex profiling technologies for different omics measurements. Missing values can complicate the application of clustering algorithms, whose goals are to group points based on some similarity criterion. A common practice for dealing with missing values in the context of clustering is to first impute the missing values, and then apply the clustering algorithm on the completed data.'),
(99101, 2019, 'bmc', 'MetaNN: accurate classification of host phenotypes from metagenomic data using neural networks', 'Lo, Chieh|Marculescu, Radu', 'Department of Electrical and Computer Engineering, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA, USA.|Department of Electrical and Computer Engineering, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA, USA. radum@cmu.edu.', 'United States|United States', 5, 'Algorithms|Area Under Curve|Databases, Genetic|Humans|Machine Learning|Metagenomics|Microbiota|Models, Theoretical|Neural Networks (Computer)|Phenotype|ROC Curve|Support Vector Machine', 'Journal Article', '10.1186/s12859-019-2833-2', '31216991', 'Microbiome profiles in the human body and environment niches have become publicly available due to recent advances in high-throughput sequencing technologies. Indeed, recent studies have already identified different microbiome profiles in healthy and sick individuals for a variety of diseases, this suggests that the microbiome profile can be used as a diagnostic tool in identifying the disease states of an individual. However, the high-dimensional nature of metagenomic data poses a significant challenge to existing machine learning models. Consequently, to enable personalized treatments, an efficient framework that can accurately and robustly differentiate between healthy and sick microbiome profiles is needed.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(99102, 2019, 'bmc', 'Selected research articles from the 2018 International Workshop on Computational Network Biology: Modeling, Analysis, and Control (CNB-MAC)', 'Yoon, Byung-Jun|Qian, Xiaoning|Kahveci, Tamer|Pal, Ranadip', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843-3128, USA. bjyoon@ece.tamu.edu.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843-3128, USA.|Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, 32611-6125, USA.|Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, 79409-3102, USA.', 'United States|United States|United States|United States', 1, '-', 'Editorial', '10.1186/s12859-019-2830-5', '31217001', '-'),
(99103, 2019, 'bmc', 'DCGR: feature extractions from protein sequences based on CGR via remodeling multiple information', 'Mu, Zengchao|Yu, Ting|Qi, Enfeng|Liu, Juntao|Li, Guojun', 'School of Mathematics, Shandong University, Jinan, 250100, Shandong Province, China.|School of Mathematics, Shandong University, Jinan, 250100, Shandong Province, China.|College of Mathematics and Statistics, Guangxi Normal University, Guilin, 541001, China.|School of Mathematics, Shandong University, Jinan, 250100, Shandong Province, China. juntaosdu@126.com.|School of Mathematics, Shandong University, Jinan, 250100, Shandong Province, China. guojunsdu@gmail.com.', 'China|China|China|China|China', 3, 'Algorithms|Amino Acid Sequence|Amino Acids|Nonlinear Dynamics|Phylogeny|Proteins|Transcription Factors|beta-Globins', 'Journal Article', '10.1186/s12859-019-2943-x', '31221087', 'Protein feature extraction plays an important role in the areas of similarity analysis of protein sequences and prediction of protein structures, functions and interactions. The feature extraction based on graphical representation is one of the most effective and efficient ways. However, most existing methods suffer limitations from their method design.'),
(99104, 2019, 'bmc', 'Comparison of pathway analysis and constraint-based methods for cell factory design', 'Vieira, Vítor|Maia, Paulo|Rocha, Miguel|Rocha, Isabel', 'Centro de Engenharia Biológica, Universidade do Minho, Braga, Portugal.|SilicoLife Lda, Braga, Portugal.|Centro de Engenharia Biológica, Universidade do Minho, Braga, Portugal. mrocha@di.uminho.pt.|Centro de Engenharia Biológica, Universidade do Minho, Braga, Portugal.', 'Portugal|Portugal|Portugal|Portugal', 0, 'Algorithms|Cells|Computational Biology|Models, Biological|Saccharomyces cerevisiae|Succinates', 'Comparative Study', '10.1186/s12859-019-2934-y', '31221092', 'Computational strain optimisation methods (CSOMs) have been successfully used to exploit genome-scale metabolic models, yielding strategies useful for allowing compound overproduction in metabolic cell factories. Minimal cut sets are particularly interesting since their definition allows searching for intervention strategies that impose strong growth-coupling phenotypes, and are not subject to optimality bias when compared with simulation-based CSOMs. However, since both types of methods have different underlying principles, they also imply different ways to formulate metabolic engineering problems, posing an obstacle when comparing their outputs.'),
(99105, 2019, 'bmc', 'Testing adequacy for DNA substitution models', 'Chen, Wei|Kenney, Toby|Bielawski, Joseph|Gu, Hong', 'Department of Mathematics and Statistics, Dalhousie University, Halifax, Canada.|Department of Mathematics and Statistics, Dalhousie University, Halifax, Canada.|Department of Mathematics and Statistics, Dalhousie University, Halifax, Canada.|Department of Mathematics and Statistics, Dalhousie University, Halifax, Canada. hgu@dal.ca.', 'Canada|Canada|Canada|Canada', 2, 'Base Sequence|Bias|Binding Sites|Computer Simulation|DNA|Databases, Genetic|Humans|Likelihood Functions|Models, Genetic|Phylogeny', 'Journal Article', '10.1186/s12859-019-2905-3', '31221105', 'Testing model adequacy is important before a DNA substitution model is chosen for phylogenetic inference. Using a mis-specified model can negatively impact phylogenetic inference, for example, the maximum likelihood method can be inconsistent when the DNA sequences are generated under a tree topology which is in the Felsentein Zone and analyzed with a mis-specified or inadequate model. However, model adequacy testing in phylogenetics is underdeveloped.'),
(99106, 2019, 'bmc', 'PaSS: a sequencing simulator for PacBio sequencing', 'Zhang, Wenmin|Jia, Ben|Wei, Chaochun', 'Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.|Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.|Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China. ccwei@sjtu.edu.cn.', 'China|China|China', 2, 'Animals|Arabidopsis|Caenorhabditis elegans|Computer Simulation|Escherichia coli|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-019-2901-7', '31226925', 'Third-generation sequencing platforms, such as PacBio sequencing, have been developed rapidly in recent years. PacBio sequencing generates much longer reads than the second-generation sequencing (or the next generation sequencing, NGS) technologies and it has unique sequencing error patterns. An effective read simulator is essential to evaluate and promote the development of new bioinformatics tools for PacBio sequencing data analysis.'),
(99107, 2019, 'bmc', 'Helitron distribution in Brassicaceae and whole Genome Helitron density as a character for distinguishing plant species', 'Hu, Kaining|Xu, Kai|Wen, Jing|Yi, Bin|Shen, Jinxiong|Ma, Chaozhi|Fu, Tingdong|Ouyang, Yidan|Tu, Jinxing', 'National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, Peoples Republic of China.|National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, Peoples Republic of China.|National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, Peoples Republic of China.|National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, Peoples Republic of China.|National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, Peoples Republic of China.|National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, Peoples Republic of China.|National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, Peoples Republic of China.|National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, Peoples Republic of China. diana1983941@mail.hzau.edu.cn.|National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, Peoples Republic of China. tujx@mail.hzau.edu.cn.', 'China|China|China|China|China|China|China|China|China', 1, 'Arabidopsis|Brassicaceae|DNA Transposable Elements|Discriminant Analysis|Evolution, Molecular|Genome, Plant|High-Throughput Nucleotide Sequencing|Phylogeny|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-019-2945-8', '31234777', 'Helitron is a rolling-circle DNA transposon, it plays an important role in plant evolution. However, Helitron distribution and contribution to evolution at the family level have not been previously investigated.'),
(99108, 2019, 'bmc', 'An iteration method for identifying yeast essential proteins from heterogeneous network', 'Zhao, Bihai|Zhao, Yulin|Zhang, Xiaoxia|Zhang, Zhihong|Zhang, Fan|Wang, Lei', 'College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, Hunan, 410022, Peoples Republic of China.|College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, Hunan, 410022, Peoples Republic of China.|College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, Hunan, 410022, Peoples Republic of China.|College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, Hunan, 410022, Peoples Republic of China.|College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, Hunan, 410022, Peoples Republic of China.|College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, Hunan, 410022, Peoples Republic of China. wanglei@xtu.edu.cn.', 'China|China|China|China|China|China', 9, 'Algorithms|Protein Domains|Protein Interaction Mapping|Protein Interaction Maps|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins', 'Journal Article', '10.1186/s12859-019-2930-2', '31234779', 'Essential proteins are distinctly important for an organisms survival and development and crucial to disease analysis and drug design as well. Large-scale protein-protein interaction (PPI) data sets exist in Saccharomyces cerevisiae, which provides us with a valuable opportunity to predict identify essential proteins from PPI networks. Many network topology-based computational methods have been designed to detect essential proteins. However, these methods are limited by the completeness of available PPI data. To break out of these restraints, some computational methods have been proposed by integrating PPI networks and multi-source biological data. Despite the progress in the research of multiple data fusion, it is still challenging to improve the prediction accuracy of the computational methods.'),
(99109, 2019, 'bmc', 'NPCMF: Nearest Profile-based Collaborative Matrix Factorization method for predicting miRNA-disease associations', 'Gao, Ying-Lian|Cui, Zhen|Liu, Jin-Xing|Wang, Juan|Zheng, Chun-Hou', 'Library of Qufu Normal University, Qufu Normal University, Rizhao, China.|School of Information Science and Engineering, Qufu Normal University, Rizhao, China.|School of Information Science and Engineering, Qufu Normal University, Rizhao, China. sdcavell@126.com.|School of Information Science and Engineering, Qufu Normal University, Rizhao, China.|Co-Innovation Center for Information Supply and Assurance Technology, Anhui University, Hefei, China.', 'China|China|China|China|China', 6, 'Algorithms|Area Under Curve|Colonic Neoplasms|Computational Biology|Databases, Genetic|Genetic Predisposition to Disease|Humans|MicroRNAs|ROC Curve|Rectal Neoplasms|Stomach Neoplasms', 'Journal Article', '10.1186/s12859-019-2956-5', '31234797', 'Predicting meaningful miRNA-disease associations (MDAs) is costly. Therefore, an increasing number of researchers are beginning to focus on methods to predict potential MDAs. Thus, prediction methods with improved accuracy are under development. An efficient computational method is proposed to be crucial for predicting novel MDAs. For improved experimental productivity, large biological datasets are used by researchers. Although there are many effective and feasible methods to predict potential MDAs, the possibility remains that these methods are flawed.'),
(99110, 2019, 'bmc', 'edge2vec: Representation learning using edge semantics for biomedical knowledge discovery', 'Gao, Zheng|Fu, Gang|Ouyang, Chunping|Tsutsui, Satoshi|Liu, Xiaozhong|Yang, Jeremy|Gessner, Christopher|Foote, Brian|Wild, David|Ding, Ying|Yu, Qi', 'School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA.|Microsoft Corporation, Seattle, Washington, USA.|University of South China, Hengyang, Hunan, China.|School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA.|School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA.|School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA.|School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA.|Data2Discovery, Inc., Bloomington, IN, USA.|School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA.|School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA.|School of Management, Shanxi Medical University, Taiyuan, Shanxi, China. yuqi@sxmu.edu.cn.', 'India|United States|China|India|India|India|India|United States|India|India|China', 2, 'Algorithms|Biomedical Research|Humans|Informatics|Knowledge|Learning|Neural Networks (Computer)|Semantics', 'Journal Article', '10.1186/s12859-019-2914-2', '31238875', 'Representation learning provides new and powerful graph analytical approaches and tools for the highly valued data science challenge of mining knowledge graphs. Since previous graph analytical methods have mostly focused on homogeneous graphs, an important current challenge is extending this methodology for richly heterogeneous graphs and knowledge domains. The biomedical sciences are such a domain, reflecting the complexity of biology, with entities such as genes, proteins, drugs, diseases, and phenotypes, and relationships such as gene co-expression, biochemical regulation, and biomolecular inhibition or activation. Therefore, the semantics of edges and nodes are critical for representation learning and knowledge discovery in real world biomedical problems.'),
(99111, 2019, 'bmc', 'Twiner: correlation-based regularization for identifying common cancer gene signatures', 'Lopes, Marta B|Casimiro, Sandra|Vinga, Susana', 'Instituto de Telecomunicações, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, Lisboa, 1049-001, Portugal. marta.lopes@tecnico.ulisboa.pt.|Luis Costa Lab, Instituto de Medicina Molecular, Faculdade de Medicina da Universidade de Lisboa, Avenida Professor Egas Moniz, Lisboa, 1649-028, Portugal.|INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Rua Alves Redol 9, Lisboa, 1000-029, Portugal.', 'Portugal|Portugal|Portugal', 2, 'Estrogens|Female|Gene Expression Profiling|Gene Regulatory Networks|Humans|Logistic Models|Male|Principal Component Analysis|Prostatic Neoplasms|Receptors, Androgen|Survival Analysis|Transcriptome|Triple Negative Breast Neoplasms', 'Journal Article', '10.1186/s12859-019-2937-8', '31238876', 'Breast and prostate cancers are typical examples of hormone-dependent cancers, showing remarkable similarities at the hormone-related signaling pathways level, and exhibiting a high tropism to bone. While the identification of genes playing a specific role in each cancer type brings invaluable insights for gene therapy research by targeting disease-specific cell functions not accounted so far, identifying a common gene signature to breast and prostate cancers could unravel new targets to tackle shared hormone-dependent disease features, like bone relapse. This would potentially allow the development of new targeted therapies directed to genes regulating both cancer types, with a consequent positive impact in cancer management and health economics.'),
(99112, 2019, 'bmc', 'FunSet: an open-source software and web server for performing and displaying Gene Ontology enrichment analysis', 'Hale, Matthew L|Thapa, Ishwor|Ghersi, Dario', 'School of Interdisciplinary Informatics, College of Information Science & Technology, University of Nebraska at Omaha, 1110 S 67TH, Omaha, 68182, NE, USA.|School of Interdisciplinary Informatics, College of Information Science & Technology, University of Nebraska at Omaha, 1110 S 67TH, Omaha, 68182, NE, USA.|School of Interdisciplinary Informatics, College of Information Science & Technology, University of Nebraska at Omaha, 1110 S 67TH, Omaha, 68182, NE, USA. dghersi@unomaha.edu.', 'United States|United States|United States', 4, 'Cluster Analysis|Gene Ontology|Internet|Software', 'Journal Article', '10.1186/s12859-019-2960-9', '31248361', 'Gene Ontology enrichment analysis provides an effective way to extract meaningful information from complex biological datasets. By identifying terms that are significantly overrepresented in a gene set, researchers can uncover biological features shared by genes. In addition to extracting enriched terms, it is also important to visualize the results in a way that is conducive to biological interpretation.'),
(99113, 2019, 'bmc', 'Block Forests: random forests for blocks of clinical and omics covariate data', 'Hornung, Roman|Wright, Marvin N', 'Institute for Medical Information Processing, Biometry and Epidemiology, University of Munich, Marchioninistr. 15, Munich, 81377, Germany. hornung@ibe.med.uni-muenchen.de.|Leibniz Institute for Prevention Research and Epidemiology - BIPS, Achterstr. 30, Bremen, 28359, Germany.', 'Germany|Germany', 0, 'Genomics|Humans|Machine Learning|Survival Analysis', 'Journal Article', '10.1186/s12859-019-2942-y', '31248362', 'In the last years more and more multi-omics data are becoming available, that is, data featuring measurements of several types of omics data for each patient. Using multi-omics data as covariate data in outcome prediction is both promising and challenging due to the complex structure of such data. Random forest is a prediction method known for its ability to render complex dependency patterns between the outcome and the covariates. Against this background we developed five candidate random forest variants tailored to multi-omics covariate data. These variants modify the split point selection of random forest to incorporate the block structure of multi-omics data and can be applied to any outcome type for which a random forest variant exists, such as categorical, continuous and survival outcomes. Using 20 publicly available multi-omics data sets with survival outcome we compared the prediction performances of the block forest variants with alternatives. We also considered the common special case of having clinical covariates and measurements of a single omics data type available.'),
(99114, 2019, 'bmc', 'A mass and charge balanced metabolic model of Setaria viridis revealed mechanisms of proton balancing in C4 plants', 'Shaw, Rahul|Cheung, C Y Maurice', 'Yale-NUS College, 16 College Avenue West, Singapore, 138527, Singapore.|Yale-NUS College, 16 College Avenue West, Singapore, 138527, Singapore. maurice.cheung@yale-nus.edu.sg.', 'Singapore|Singapore', 0, 'Biomass|Cellulose|Genome, Plant|Lignin|Metabolic Networks and Pathways|Models, Biological|Photosynthesis|Polysaccharides|Protons|Setaria Plant', 'Journal Article', '10.1186/s12859-019-2941-z', '31248364', 'C4 photosynthesis is a key domain of plant research with outcomes ranging from crop quality improvement, biofuel production and efficient use of water and nutrients. A metabolic network model of C4 lab organism Setaria viridis with extensive gene-reaction associations can accelerate target identification for desired metabolic manipulations and thereafter in vivo validation. Moreover, metabolic reconstructions have also been shown to be a significant tool to investigate fundamental metabolic traits.'),
(99115, 2019, 'bmc', 'JCD-DEA: a joint covariate detection tool for differential expression analysis on tumor expression profiles', 'Li, Yi|Liu, Yanan|Wu, Yiming|Zhao, Xudong', 'College of Information and Computer Engineering, Northeast Forestry University, No.26 Hexing Road, Harbin, 150040, China.|College of Information and Computer Engineering, Northeast Forestry University, No.26 Hexing Road, Harbin, 150040, China.|College of Information and Computer Engineering, Northeast Forestry University, No.26 Hexing Road, Harbin, 150040, China.|College of Information and Computer Engineering, Northeast Forestry University, No.26 Hexing Road, Harbin, 150040, China. zhaoxudong@nefu.edu.cn.', 'China|China|China|China', 0, 'Algorithms|Gene Expression Profiling|Humans|Multivariate Analysis|Neoplasms|Reproducibility of Results|Software', 'Journal Article', '10.1186/s12859-019-2893-3', '31253075', 'Differential expression analysis on tumor expression profiles has always been a key issue for subsequent biological experimental validation. It is important how to select features which best discriminate between different groups of patients. Despite the emergence of multivariate analysis approaches, prevailing feature selection methods primarily focus on multiple hypothesis testing on individual variables, and then combine them for an explanatory result. Besides, these methods, which are commonly based on hypothesis testing, view classification as a posterior validation of the selected variables.'),
(99116, 2019, 'bmc', 'FastqCleaner: an interactive Bioconductor application for quality-control, filtering and trimming of FASTQ files', 'Roser, Leandro Gabriel|Agüero, Fernán|Sánchez, Daniel Oscar', 'Instituto de Investigaciones Biotecnológicas (IIB), Universidad Nacional de San Martín - CONICET, 25 de Mayo 1401, B1650HMP, San Martín, Buenos Aires, Argentina. learoser@gmail.com.|Instituto de Investigaciones Biotecnológicas (IIB), Universidad Nacional de San Martín - CONICET, 25 de Mayo 1401, B1650HMP, San Martín, Buenos Aires, Argentina.|Instituto de Investigaciones Biotecnológicas (IIB), Universidad Nacional de San Martín - CONICET, 25 de Mayo 1401, B1650HMP, San Martín, Buenos Aires, Argentina. dsanchez21@gmail.com.', 'Argentina|Argentina|Argentina', 1, 'Computational Biology|High-Throughput Nucleotide Sequencing|Humans|Quality Control|Software|User-Computer Interface|Workflow', 'Journal Article', '10.1186/s12859-019-2961-8', '31253077', 'Exploration and processing of FASTQ files are the first steps in state-of-the-art data analysis workflows of Next Generation Sequencing (NGS) platforms. The large amount of data generated by these technologies has put a challenge in terms of rapid analysis and visualization of sequencing information. Recent integration of the R data analysis platform with web visual frameworks has stimulated the development of user-friendly, powerful, and dynamic NGS data analysis applications.'),
(99117, 2019, 'bmc', 'Cell segmentation methods for label-free contrast microscopy: review and comprehensive comparison', 'Vicar, Tomas|Balvan, Jan|Jaros, Josef|Jug, Florian|Kolar, Radim|Masarik, Michal|Gumulec, Jaromir', 'Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 3058/10, Brno, CZ-61600, Czech Republic.|Department of Pathological Physiology, Faculty of Medicine, Masaryk University, Kamenice 5, Brno, CZ-62500, Czech Republic.|Department of Histology and Embryology, Faculty of Medicine, Masaryk University, Kamenice 5, Brno, CZ-62500, Czech Republic.|Max Planck Institute of Molecular Cell Biology and Genetics, Pfotenhauerstr. 108, Dresden, DE-01307, Germany.|Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 3058/10, Brno, CZ-61600, Czech Republic.|Department of Pathological Physiology, Faculty of Medicine, Masaryk University, Kamenice 5, Brno, CZ-62500, Czech Republic.|Department of Physiology, Faculty of Medicine, Masaryk University, Kamenice 5, Brno, CZ-62500, Czech Republic. j.gumulec@med.muni.cz.', '-|-|-|Germany|-|-|-', 0, 'Algorithms|Cell Fractionation|Humans|Image Processing, Computer-Assisted|Microscopy', 'Journal Article', '10.1186/s12859-019-2880-8', '31253078', 'Because of its non-destructive nature, label-free imaging is an important strategy for studying biological processes. However, routine microscopic techniques like phase contrast or DIC suffer from shadow-cast artifacts making automatic segmentation challenging. The aim of this study was to compare the segmentation efficacy of published steps of segmentation work-flow (image reconstruction, foreground segmentation, cell detection (seed-point extraction) and cell (instance) segmentation) on a dataset of the same cells from multiple contrast microscopic modalities.'),
(99118, 2019, 'bmc', 'Polymorphic edge detection (PED): two efficient methods of polymorphism detection from next-generation sequencing data', 'Miyao, Akio|Kiyomiya, Jianyu Song|Iida, Keiko|Doi, Koji|Yasue, Hiroshi', 'Institute of Crop Science, National Agriculture and Food Research Organization, 2-1-2, Kannondai, Tsukuba, Ibaraki, 305-8518, Japan. miyao@affrc.go.jp.|Institute of Crop Science, National Agriculture and Food Research Organization, 2-1-2, Kannondai, Tsukuba, Ibaraki, 305-8518, Japan.|Institute of Crop Science, National Agriculture and Food Research Organization, 2-1-2, Kannondai, Tsukuba, Ibaraki, 305-8518, Japan.|Tsukuba Gene Technology Laboratories Inc, 6-320, Arakawaoki, Tsuchiura, Ibaraki, 300-0873, Japan.|Tsukuba Gene Technology Laboratories Inc, 6-320, Arakawaoki, Tsuchiura, Ibaraki, 300-0873, Japan.', 'Japan|Japan|Japan|Japan|Japan', 0, 'Computational Biology|High-Throughput Nucleotide Sequencing|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-019-2955-6', '31253084', 'Accurate detection of polymorphisms with a next generation sequencer data is an important element of current genetic analysis. However, there is still no detection pipeline that is completely reliable.'),
(99119, 2019, 'bmc', 'In silico analysis of missense mutations in exons 1-5 of the F9 gene that cause hemophilia B', 'Meléndez-Aranda, Lennon|Jaloma-Cruz, Ana Rebeca|Pastor, Nina|Romero-Prado, Marina María de Jesús', 'Doctorado en Genética Humana, Centro Universitario de Ciencias de la Salud, Universidad de Guadalajara, C.P, 44340, Guadalajara, Jalisco, México.|División de Genética, Centro de Investigación Biomédica de Occidente, Instituto Mexicano del Seguro Social (IMSS), Jalisco, C.P, 44340, Guadalajara, Mexico.|Centro de Investigación en Dinámica Celular, CIDC, Universidad Autónoma del Estado de Morelos, Cuernavaca, Mexico.|Departamento de Fisiología, Centro Universitario de Ciencias de la Salud, Universidad de Guadalajara, C.P, 44340, Guadalajara, Jalisco, México. marina.rprado@academicos.udg.mx.', '-|Mexico|Mexico|-', 0, 'Algorithms|Computational Biology|Computer Simulation|Exons|Factor IX|Genotype|Hemophilia B|Humans|Mutation, Missense|Phenotype', 'Journal Article', '10.1186/s12859-019-2919-x', '31253089', 'Missense mutations in the first five exons of F9, which encodes factor FIX, represent 40% of all mutations that cause hemophilia B. To address the ongoing debate regarding in silico identification of disease-causing mutations at these exons, we analyzed 215 missense mutations from www.factorix.org using six in silico prediction tools, which are the most common used programs for analysis prediction of impact of mutations on the protein structure and function, with further advantage of using similar approaches. We developed different algorithms to integrate multiple predictions from such tools. In order to approach a structural analysis on FIX we performed a modeling of five selected pathogenic mutations.'),
(99120, 2019, 'bmc', 'Odyssey: a semi-automated pipeline for phasing, imputation, and analysis of genome-wide genetic data', 'Eller, Ryan J|Janga, Sarath C|Walsh, Susan', 'Department of Biology, Indiana University-Purdue University Indianapolis, 723 W. Michigan Street, Indianapolis, IN, USA. ryeller@iupui.edu.|Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, 5021 Health Information and Translational Sciences (HITS), 410 West 10th Street, Indianapolis, IN, USA.|Department of Biology, Indiana University-Purdue University Indianapolis, 723 W. Michigan Street, Indianapolis, IN, USA.', 'India|India|India', 0, 'Automation|Computational Biology|Data Interpretation, Statistical|Genome-Wide Association Study|Software', 'Journal Article', '10.1186/s12859-019-2964-5', '31253090', 'Genome imputation, admixture resolution and genome-wide association analyses are timely and computationally intensive processes with many composite and requisite steps. Analysis time increases further when building and installing the run programs required for these analyses. For scientists that may not be as versed in programing language, but want to perform these operations hands on, there is a lengthy learning curve to utilize the vast number of programs available for these analyses.'),
(99121, 2019, 'bmc', 'MassComp, a lossless compressor for mass spectrometry data', 'Yang, Ruochen|Chen, Xi|Ochoa, Idoia', 'Electrical Engineering Department, University of Southern California, Los Angeles, CA, USA.|Electrical and Computer Engineering Department, University of Illinois at Urbana-Champaign, Urbana, IL, USA.|Electrical and Computer Engineering Department, University of Illinois at Urbana-Champaign, Urbana, IL, USA. idoia@illinois.edu.', 'United States|United States|United States', 0, 'Algorithms|Data Compression|Mass Spectrometry|Metabolomics|Proteomics', 'Journal Article', '10.1186/s12859-019-2962-7', '31262247', 'Mass Spectrometry (MS) is a widely used technique in biology research, and has become key in proteomics and metabolomics analyses. As a result, the amount of MS data has significantly increased in recent years. For example, the MS repository MassIVE contains more than 123TB of data. Somehow surprisingly, these data are stored uncompressed, hence incurring a significant storage cost. Efficient representation of these data is therefore paramount to lessen the burden of storage and facilitate its dissemination.'),
(99122, 2019, 'bmc', 'Polled Digital Cell Sorter (p-DCS): Automatic identification of hematological cell types from single cell RNA-sequencing clusters', 'Domanskyi, Sergii|Szedlak, Anthony|Hawkins, Nathaniel T|Wang, Jiayin|Paternostro, Giovanni|Piermarocchi, Carlo', 'Department of Physics and Astronomy, Michigan State University, East Lansing, MI, 48824, USA. domansk6@msu.edu.|Department of Physics and Astronomy, Michigan State University, East Lansing, MI, 48824, USA.|Department of Physics and Astronomy, Michigan State University, East Lansing, MI, 48824, USA.|Salgomed, Inc., Del Mar, CA, 92014, USA.|Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, 92037, USA.|Department of Physics and Astronomy, Michigan State University, East Lansing, MI, 48824, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Bone Marrow Cells|Cluster Analysis|Gene Expression Profiling|Humans|Principal Component Analysis|Sequence Analysis, RNA|Single-Cell Analysis|Software', 'Journal Article', '10.1186/s12859-019-2951-x', '31262249', 'Single cell RNA sequencing (scRNA-seq) brings unprecedented opportunities for mapping the heterogeneity of complex cellular environments such as bone marrow, and provides insight into many cellular processes. Single cell RNA-seq has a far larger fraction of missing data reported as zeros (dropouts) than traditional bulk RNA-seq, and unsupervised clustering combined with Principal Component Analysis (PCA) can be used to overcome this limitation. After clustering, however, one has to interpret the average expression of markers on each cluster to identify the corresponding cell types, and this is normally done by hand by an expert curator.'),
(99123, 2019, 'bmc', 'SRAssembler: Selective Recursive local Assembly of homologous genomic regions', 'McCarthy, Thomas W|Chou, Hsien-Chao|Brendel, Volker P', 'Department of Biology, Indiana University, Bloomington, 47405, Indiana, USA.|Department of Oncology, St Jude Childrens Research Hospital, Memphis, 38105, Tennessee, USA.|Department of Biology, Indiana University, Bloomington, 47405, Indiana, USA. vbrendel@indiana.edu.', 'India|United States|India', 0, 'Arabidopsis|Genetic Loci|Genome, Plant|Genomics|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-019-2949-4', '31266441', 'The falling cost of next-generation sequencing technology has allowed deep sequencing across related species and of individuals within species. Whole genome assemblies from these data remain high time- and resource-consuming computational tasks, particularly if best solutions are sought using different assembly strategies and parameter sets. However, in many cases, the underlying research questions are not genome-wide but rather target specific genes or sets of genes. We describe a novel assembly tool, SRAssembler, that efficiently assembles only contigs containing potential homologs of a gene or protein query, thus enabling gene-specific genome studies over large numbers of short read samples.'),
(99124, 2019, 'bmc', 'Analysis of common targets for circular RNAs', 'Lin, Ya-Chi|Lee, Yueh-Chun|Chang, Kai-Li|Hsiao, Kuei-Yang', 'Department of Plant Pathology, College of Agriculture and Natural Resources, National Chung Hsing University, Taichung, 40227, Taiwan.|Department of Radiation Oncology, Chung Shan Medical University Hospital, Taichung, 40201, Taiwan.|Department of Physiology, National Cheng Kung University, Tainan, 70101, Taiwan.|Institute of Biochemistry, College of Life Sciences, National Chung Hsing University, Taichung, 40227, Taiwan. ky.hsiao@nchu.edu.tw.', '-|-|-|-', 3, 'Animals|Energy Metabolism|Humans|MicroRNAs|RNA|User-Computer Interface', 'Journal Article', '10.1186/s12859-019-2966-3', '31266442', 'The studies of functions of circular RNAs (circRNAs) are heavily focused on the regulation of gene expression through interactions with multiple miRNAs. However, the number of predicted target genes is typically overwhelming due to the synergistic complexity caused by two factors xe2x94x80 the binding of multiple miRNAs to a circRNA and the existence of multiple targets for each miRNA. Analysis of common targets (ACT) was designed to facilitate the identification of potential circRNA targets.'),
(99125, 2019, 'bmc', 'Exploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models', 'Esteban-Medina, Marina|Peña-Chilet, María|Loucera, Carlos|Dopazo, Joaquín', 'Clinical Bioinformatics Area. Fundación Progreso y Salud (FPS). CDCA, Hospital Virgen del Rocio, 41013, Sevilla, Spain.|Clinical Bioinformatics Area. Fundación Progreso y Salud (FPS). CDCA, Hospital Virgen del Rocio, 41013, Sevilla, Spain.|Clinical Bioinformatics Area. Fundación Progreso y Salud (FPS). CDCA, Hospital Virgen del Rocio, 41013, Sevilla, Spain.|Clinical Bioinformatics Area. Fundación Progreso y Salud (FPS). CDCA, Hospital Virgen del Rocio, 41013, Sevilla, Spain. joaquin.dopazo@juntadeandalucia.es.', 'Spain|Spain|Spain|Spain', 2, 'Databases, Factual|Fanconi Anemia|Genomics|Humans|Machine Learning|Phenotype|Proteins|Signal Transduction', 'Journal Article', '10.1186/s12859-019-2969-0', '31266445', 'In spite of the abundance of genomic data, predictive models that describe phenotypes as a function of gene expression or mutations are difficult to obtain because they are affected by the curse of dimensionality, given the disbalance between samples and candidate genes. And this is especially dramatic in scenarios in which the availability of samples is difficult, such as the case of rare diseases.'),
(99126, 2019, 'bmc', 'DDGun: an untrained method for the prediction of protein stability changes upon single and multiple point variations', 'Montanucci, Ludovica|Capriotti, Emidio|Frank, Yotam|Ben-Tal, Nir|Fariselli, Piero', 'Department of Comparative Biomedicine and Food Science (BCA), University of Padova, Viale dellUniversitxc3xa0 16, 35020, Legnaro, Italy.|BioFolD Unit, Department of Pharmacy and Biotechnology (FaBiT), University of Bologna, Via Selmi 3, 40126, Bologna, Italy. emidio.capriotti@unibo.it.|Department of Biochemistry and Molecular Biology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Ramat Aviv, 69978, Tel Aviv, Israel.|Department of Biochemistry and Molecular Biology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Ramat Aviv, 69978, Tel Aviv, Israel.|Department of Comparative Biomedicine and Food Science (BCA), University of Padova, Viale dellUniversitxc3xa0 16, 35020, Legnaro, Italy. piero.fariselli@unito.it.', 'Italy|Italy|Israel|Israel|Italy', 7, 'Algorithms|Amino Acid Sequence|Evolution, Molecular|Humans|Point Mutation|Protein Stability|Proteins|Thermodynamics', 'Journal Article', '10.1186/s12859-019-2923-1', '31266447', 'Predicting the effect of single point variations on protein stability constitutes a crucial step toward understanding the relationship between protein structure and function. To this end, several methods have been developed to predict changes in the Gibbs free energy of unfolding (xe2x88x86xe2x88x86G) between wild type and variant proteins, using sequence and structure information. Most of the available methods however do not exhibit the anti-symmetric prediction property, which guarantees that the predicted xe2x88x86xe2x88x86G value for a variation is the exact opposite of that predicted for the reverse variation, i.e., xe2x88x86xe2x88x86G(Axe2x80x89xe2x86x92xe2x80x89B)xe2x80x89=xe2x80x89-xe2x88x86xe2x88x86G(Bxe2x80x89xe2x86x92xe2x80x89A), where A and B are amino acids.'),
(99127, 2019, 'bmc', 'ENTRNA: a framework to predict RNA foldability', 'Su, Congzhe|Weir, Jeffery D|Zhang, Fei|Yan, Hao|Wu, Teresa', 'School of Computing, Informatics, Decision Systems Engineering, Arizona State University, Tempe, AZ, 85281, USA.|Department of Operational Sciences, Graduate School of Engineering and Management, Air Force Institute of Technology, Wright-Patterson AFB, Dayton, OH, 45433, USA.|Biodesign Center for Molecular Design and Biomimetics, The Biodesign Institute & School of Molecular Sciences, Arizona State University, Tempe, AZ, 85281, USA.|Biodesign Center for Molecular Design and Biomimetics, The Biodesign Institute & School of Molecular Sciences, Arizona State University, Tempe, AZ, 85281, USA.|School of Computing, Informatics, Decision Systems Engineering, Arizona State University, Tempe, AZ, 85281, USA. Teresa.Wu@asu.edu.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Databases, Genetic|RNA|RNA Folding|Thermodynamics', 'Journal Article', '10.1186/s12859-019-2948-5', '31269893', 'RNA molecules play many crucial roles in living systems. The spatial complexity that exists in RNA structures determines their cellular functions. Therefore, understanding RNA folding conformations, in particular, RNA secondary structures, is critical for elucidating biological functions. Existing literature has focused on RNA design as either an RNA structure prediction problem or an RNA inverse folding problem where free energy has played a key role.'),
(99128, 2019, 'bmc', 'iMAP: an integrated bioinformatics and visualization pipeline for microbiome data analysis', 'Buza, Teresia M|Tonui, Triza|Stomeo, Francesca|Tiambo, Christian|Katani, Robab|Schilling, Megan|Lyimo, Beatus|Gwakisa, Paul|Cattadori, Isabella M|Buza, Joram|Kapur, Vivek', 'The Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, State College, PA, USA. ndelly@gmail.com.|Biosciences eastern and central Africa-International Livestock Research Institute (BecA-ILRI) Hub, Nairobi, Kenya.|Biosciences eastern and central Africa-International Livestock Research Institute (BecA-ILRI) Hub, Nairobi, Kenya.|Biosciences eastern and central Africa-International Livestock Research Institute (BecA-ILRI) Hub, Nairobi, Kenya.|The Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, State College, PA, USA.|The Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, State College, PA, USA.|Nelson Mandela African Institute of Science and Technology, Arusha, Tanzania.|Sokoine University of Agriculture, Morogoro, Tanzania.|The Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, State College, PA, USA.|Nelson Mandela African Institute of Science and Technology, Arusha, Tanzania.|The Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, State College, PA, USA.', 'United States|Kenya|Kenya|Kenya|United States|United States|United States|United States|United States|United States|United States', 1, 'Bacteria|Base Sequence|Computational Biology|Microbiota|Phylogeny|RNA, Ribosomal, 16S|Software', 'Journal Article', '10.1186/s12859-019-2965-4', '31269897', 'One of the major challenges facing investigators in the microbiome field is turning large numbers of reads generated by next-generation sequencing (NGS) platforms into biological knowledge. Effective analytical workflows that guarantee reproducibility, repeatability, and result provenance are essential requirements of modern microbiome research. For nearly a decade, several state-of-the-art bioinformatics tools have been developed for understanding microbial communities living in a given sample. However, most of these tools are built with many functions that require an in-depth understanding of their implementation and the choice of additional tools for visualizing the final output. Furthermore, microbiome analysis can be time-consuming and may even require more advanced programming skills which some investigators may be lacking.'),
(99129, 2019, 'bmc', 'The cell line ontology-based representation, integration and analysis of cell lines used in China', 'Pan, Hongjie|Bian, Xiaocui|Yang, Sheng|He, Yongqun|Yang, Xiaolin|Liu, Yuqin', 'Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Beijing, China.|Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Beijing, China.|Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Beijing, China.|University of Michigan Medical School, Ann Arbor, MI 48109, USA.|Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Beijing, China. yangxl@pumc.edu.cn.|Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Beijing, China. liuyuqin@pumc.edu.cn.', 'China|China|China|United States|China|China', 0, 'Biological Ontologies|Biomedical Research|Cell Line|China|Databases, Factual|Humans|User-Computer Interface', 'Journal Article', '10.1186/s12859-019-2724-6', '31272367', 'The Chinese National Infrastructure of Cell Line stores and distributes cell lines for biomedical research in China. This study aims to represent and integrate the information of NICR cell lines into the community-based Cell Line Ontology (CLO).'),
(99130, 2019, 'bmc', 'Exploring the information transmission properties of noise-induced dynamics: application to glioma differentiation', 'Sai, Aditya|Kong, Nan', 'Weldon School of Biomedical Engineering, Purdue University, 206 S Martin Jischke Drive, West Lafayette, 47907, IN, USA. asai@purdue.edu.|Weldon School of Biomedical Engineering, Purdue University, 206 S Martin Jischke Drive, West Lafayette, 47907, IN, USA.', 'United States|United States', 0, 'Cell Differentiation|Glioma|Humans|Information Theory|Models, Biological|Signal Transduction|Stochastic Processes', 'Journal Article', '10.1186/s12859-019-2970-7', '31272368', 'Cells operate in an uncertain environment, where critical cell decisions must be enacted in the presence of biochemical noise. Information theory can measure the extent to which such noise perturbs normal cellular function, in which cells must perceive environmental cues and relay signals accurately to make timely and informed decisions. Using multivariate response data can greatly improve estimates of the latent information content underlying important cell fates, like differentiation.'),
(99131, 2019, 'bmc', 'An ontology for representing hematologic malignancies: the cancer cell ontology', 'Serra, Lucas M|Duncan, William D|Diehl, Alexander D', 'Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA. lucasser@buffalo.edu.|Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA.|Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA.', 'United States|United States|United States', 2, 'Biological Ontologies|Cell Line, Tumor|Hematologic Neoplasms|Humans|Immunophenotyping|Machine Learning|Neoplastic Stem Cells', 'Journal Article', '10.1186/s12859-019-2722-8', '31272372', 'Within the cancer domain, ontologies play an important role in the integration and annotation of data in order to support numerous biomedical tools and applications. This work seeks to leverage existing standards in immunophenotyping cell types found in hematologic malignancies to provide an ontological representation of them to aid in data annotation and analysis for patient data.'),
(99132, 2019, 'bmc', 'Cells in ExperimentaL Life Sciences (CELLS-2018): capturing the knowledge of normal and diseased cells with ontologies', 'Sarntivijai, Sirarat|He, Yongqun|Diehl, Alexander D', 'ELIXIR, Wellcome Genome Campus, Hinxton, Cambridge, UK.|University of Michigan Medical School, Ann Arbor, MI, USA. yongqunh@med.umich.edu.|Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Biological Ontologies|Biological Science Disciplines|Congresses as Topic|Humans|Research', 'Editorial', '10.1186/s12859-019-2721-9', '31272374', 'Cell cultures and cell lines are widely used in life science experiments. In conjunction with the 2018 International Conference on Biomedical Ontology (ICBO-2018), the 2nd International Workshop on Cells in ExperimentaL Life Science (CELLS-2018) focused on two themes of knowledge representation, for newly-discovered cell types and for cells in disease states. This workshop included five oral presentations and a general discussion session. Two new ontologies, including the Cancer Cell Ontology (CCL) and the Ontology for Stem Cell Investigations (OSCI), were reported in the workshop. In another representation, the Cell Line Ontology (CLO) framework was applied and extended to represent cell line cells used in China and their Chinese representation. Other presentations included a report on the application of ontologies to cross-compare cell types and marker patterns used in flow cytometry studies, and a presentation on new experimental findings about novel cell types based on single cell RNA sequencing assay and their corresponding ontological representation. The general discussion session focused on the ontology design patterns in representing newly-discovered cell types and cells in disease states.'),
(99133, 2019, 'bmc', 'OSCI: standardized stem cell ontology representation and use cases for stem cell investigation', 'He, Yongqun|Duncan, William D|Cooper, Daniel J|Hansen, Jens|Iyengar, Ravi|Ong, Edison|Walker, Kendal|Tibi, Omar|Smith, Sam|Serra, Lucas M|Zheng, Jie|Sarntivijai, Sirarat|Schürer, Stephan|OShea, K Sue|Diehl, Alexander D', 'University of Michigan Medical School, Ann Arbor, MI, USA. yongqunh@med.umich.edu.|Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.|University of Miami, Miami, FL, USA.|Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.|Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.|University of Michigan Medical School, Ann Arbor, MI, USA.|University of Michigan Medical School, Ann Arbor, MI, USA.|John Hopkins Unversity, Baltimore, MD, USA.|DataSmith LLC, Detroit, MI, USA.|Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA.|University of Pennsylvania, Philadelphia, PA, USA.|ELIXIR, Wellcome Genome Campus, Hinxton, Cambridge, UK.|University of Miami, Miami, FL, USA.|University of Michigan Medical School, Ann Arbor, MI, USA.|Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA. addiehl@buffalo.edu.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Animals|Biological Ontologies|Biomedical Research|Humans|Stem Cells', 'Journal Article', '10.1186/s12859-019-2723-7', '31272389', 'Stem cells and stem cell lines are widely used in biomedical research. The Cell Ontology (CL) and Cell Line Ontology (CLO) are two community-based OBO Foundry ontologies in the domains of in vivo cells and in vitro cell line cells, respectively.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(99134, 2019, 'bmc', 'Reporting and connecting cell type names and gating definitions through ontologies', 'Overton, James A|Vita, Randi|Dunn, Patrick|Burel, Julie G|Bukhari, Syed Ahmad Chan|Cheung, Kei-Hoi|Kleinstein, Steven H|Diehl, Alexander D|Peters, Bjoern', 'Knocean Inc., Toronto, Ontario, Canada.|Division for Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA, USA.|ImmPort Curation Team, NG Health Solutions, Rockville, MD, USA.|Division for Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA, USA.|Department of Pathology, Yale School of Medicine, New Haven, Connecticut, USA.|Department of Emergency Medicine and Yale Center for Medical Informatics, Yale School of Medicine, New Haven, CT, USA.|Department of Pathology, Yale School of Medicine, New Haven, Connecticut, USA.|Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA.|Division for Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA, USA. bpeters@lji.org.', 'Canada|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Biological Ontologies|Databases, Factual|Humans|Immune System|Protein Subunits|Proteins', 'Journal Article', '10.1186/s12859-019-2725-5', '31272390', 'Human immunology studies often rely on the isolation and quantification of cell populations from an input sample based on flow cytometry and related techniques. Such techniques classify cells into populations based on the detection of a pattern of markers. The description of the cell populations targeted in such experiments typically have two complementary components: the description of the cell type targeted (e.g. T cells), and the description of the marker pattern utilized (e.g. CD14-, CD3+).'),
(99135, 2019, 'bmc', 'Improving MetFrag with statistical learning of fragment annotations', 'Ruttkies, Christoph|Neumann, Steffen|Posch, Stefan', 'Department Biochemistry of Plant Interactions, Leibniz Institute of Plant Biochemistry, Weinberg 3, Halle (Saale), 06120, Germany. christoph.ruttkies@ipb-halle.de.|Department Biochemistry of Plant Interactions, Leibniz Institute of Plant Biochemistry, Weinberg 3, Halle (Saale), 06120, Germany.|Institute of Computer Science, Martin Luther University Halle-Wittenberg, Von-Seckendorff-Platz 1, Halle (Saale), 06099, Germany.', 'Germany|Germany|Germany', 4, 'Bayes Theorem|Computer Simulation|Metabolomics|Molecular Structure|Tandem Mass Spectrometry', 'Evaluation Studies', '10.1186/s12859-019-2954-7', '31277571', 'Molecule identification is a crucial step in metabolomics and environmental sciences. Besides in silico fragmentation, as performed by MetFrag, also machine learning and statistical methods evolved, showing an improvement in molecule annotation based on MS/MS data. In this work we present a new statistical scoring method where annotations of m/z fragment peaks to fragment-structures are learned in a training step. Based on a Bayesian model, two additional scoring terms are integrated into the new MetFrag2.4.5 and evaluated on the test data set of the CASMI 2016 contest.'),
(99136, 2019, 'bmc', 'ET-GRU: using multi-layer gated recurrent units to identify electron transport proteins', 'Le, Nguyen Quoc Khanh|Yapp, Edward Kien Yee|Yeh, Hui-Yuan', 'Medical Humanities Research Cluster, School of Humanities, Nanyang Technological University, 48 Nanyang Ave, Singapore, 639798, Singapore. khanhlee87@gmail.com.|Singapore Institute of Manufacturing Technology, 2 Fusionopolis Way, #08-04, Innovis, Singapore, 138634, Singapore.|Medical Humanities Research Cluster, School of Humanities, Nanyang Technological University, 48 Nanyang Ave, Singapore, 639798, Singapore. hyyeh@ntu.edu.sg.', 'Singapore|Singapore|Singapore', 0, 'Electron Transport|Electron Transport Chain Complex Proteins|Humans|Neural Networks (Computer)|Position-Specific Scoring Matrices|Software', 'Journal Article', '10.1186/s12859-019-2972-5', '31277574', 'Electron transport chain is a series of protein complexes embedded in the process of cellular respiration, which is an important process to transfer electrons and other macromolecules throughout the cell. It is also the major process to extract energy via redox reactions in the case of oxidation of sugars. Many studies have determined that the electron transport protein has been implicated in a variety of human diseases, i.e. diabetes, Parkinson, Alzheimers disease and so on. Few bioinformatics studies have been conducted to identify the electron transport proteins with high accuracy, however, their performance results require a lot of improvements. Here, we present a novel deep neural network architecture to address this problem.'),
(99137, 2019, 'bmc', 'Deconvolution of autoencoders to learn biological regulatory modules from single cell mRNA sequencing data', 'Kinalis, Savvas|Nielsen, Finn Cilius|Winther, Ole|Bagger, Frederik Otzen', 'Centre for Genomic Medicine Rigshospitalet, University of Copenhagen, Copenhagen, Denmark.|Centre for Genomic Medicine Rigshospitalet, University of Copenhagen, Copenhagen, Denmark.|Centre for Genomic Medicine Rigshospitalet, University of Copenhagen, Copenhagen, Denmark.|Centre for Genomic Medicine Rigshospitalet, University of Copenhagen, Copenhagen, Denmark. frederik@binf.ku.dk.', 'Denmark|Denmark|Denmark|Denmark', 0, 'Cluster Analysis|Gene Expression Profiling|Neural Networks (Computer)|RNA, Messenger|Sequence Analysis, RNA|Single-Cell Analysis|Unsupervised Machine Learning', 'Journal Article', '10.1186/s12859-019-2952-9', '31286861', 'Unsupervised machine learning methods (deep learning) have shown their usefulness with noisy single cell mRNA-sequencing data (scRNA-seq), where the models generalize well, despite the zero-inflation of the data. A class of neural networks, namely autoencoders, has been useful for denoising of single cell data, imputation of missing values and dimensionality reduction.'),
(99138, 2019, 'bmc', 'Analyzing magnetic bead QuantiGene® Plex 20 gene expression data in high throughput mode using QGprofiler', 'Verbist, Bie|Adriaensen, Eva|Keersmaekers, Vikki|Putri, Dea|Crabbe, Marjolein|Derks, Maarten|Bagdziunas, Rytis|Laenen, Griet|De Wolf, Hans', 'Janssen R&D, TMEDS, Turnhoutseweg 30, 2340, Beerse, BE, Belgium.|Karel de Grote Hogeschool, Groenplaats, 2020, Antwerpen, BE, Belgium.|Janssen R&D, Discovery Biology, Oncology Heme, Turnhoutseweg 30, 2340, Beerse, BE, Belgium.|Janssen R&D, TMEDS, Turnhoutseweg 30, 2340, Beerse, BE, Belgium.|Janssen R&D, TMEDS, Turnhoutseweg 30, 2340, Beerse, BE, Belgium.|Janssen R&D, Discovery Biology, Oncology Heme, Turnhoutseweg 30, 2340, Beerse, BE, Belgium.|Dataroots, Tiensevest 132, 3000, Leuven, BE, Belgium.|Open Analytics, Jupiterstraat 20, 2600, Antwerpen, BE, Belgium.|Janssen R&D, DS, Turnhoutseweg 30, 2340, Beerse, BE, Belgium. hdwolf@its.jnj.com.', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 0, 'Drug Discovery|Gene Expression Profiling|Software', 'Journal Article', '10.1186/s12859-019-2975-2', '31286864', 'The QuantiGene® Plex 2.0 platform (ThermoFisher Scientific) combines bDNA with the Luminex/xMAP magnetic bead capturing technology to assess differential gene expression in a compound exposure setting. This technology allows multiplexing in a single well of a 96 or 384 multi-well plate and can thus be used in high throughput drug discovery mode. Data interpretation follows a three-step normalization/transformation flow in which raw median fluorescent gene signals are transformed to fold change values with the use of proper housekeeping genes and negative controls. Clear instructions on how to assess the data quality and tools to perform this analysis in high throughput mode are, however, currently lacking.'),
(99139, 2019, 'bmc', 'Classification of alkaloids according to the starting substances of their biosynthetic pathways using graph convolutional neural networks', 'Eguchi, Ryohei|Ono, Naoaki|Hirai Morita, Aki|Katsuragi, Tetsuo|Nakamura, Satoshi|Huang, Ming|Altaf-Ul-Amin, Md|Kanaya, Shigehiko', 'Division of Science and Technology, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, 630-0192, Japan.|Division of Science and Technology, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, 630-0192, Japan. nono@is.naist.jp.|Division of Science and Technology, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, 630-0192, Japan.|Department of Computer Science and Engineering, Toyohashi University of Technology, Hibarigaoka, Tempaku-cho, Toyohashi, Aichi, 441-8580, Japan.|Division of Science and Technology, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, 630-0192, Japan.|Division of Science and Technology, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, 630-0192, Japan.|Division of Science and Technology, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, 630-0192, Japan.|Division of Science and Technology, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, 630-0192, Japan.', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan', 0, 'Algorithms|Alkaloids|Biosynthetic Pathways|Metabolome|Models, Theoretical|Neural Networks (Computer)', 'Journal Article', '10.1186/s12859-019-2963-6', '31288752', 'Alkaloids, a class of organic compounds that contain nitrogen bases, are mainly synthesized as secondary metabolites in plants and fungi, and they have a wide range of bioactivities. Although there are thousands of compounds in this class, few of their biosynthesis pathways are fully identified. In this study, we constructed a model to predict their precursors based on a novel kind of neural network called the molecular graph convolutional neural network. Molecular similarity is a crucial metric in the analysis of qualitative structure-activity relationships. However, it is sometimes difficult for current fingerprint representations to emphasize specific features for the target problems efficiently. It is advantageous to allow the model to select the appropriate features according to data-driven decisions for extracting more useful information, which influences a classification or regression problem substantially.'),
(99140, 2019, 'bmc', 'CRA toolbox: software package for conditional robustness analysis of cancer systems biology models in MATLAB', 'Bianconi, Fortunato|Antonini, Chiara|Tomassoni, Lorenzo|Valigi, Paolo', 'Independent Researcher, Belvedere 44, Montefalco, Perugia, 06036, Italy. fortunato.bianconi@gmail.com.|Department of Engineering, University of Perugia, G. Duranti 95, Perugia, 06132, Italy.|Department of Engineering, University of Perugia, G. Duranti 95, Perugia, 06132, Italy.|Department of Engineering, University of Perugia, G. Duranti 95, Perugia, 06132, Italy.', 'Italy|Italy|Italy|Italy', 0, 'Algorithms|Animals|Computer Simulation|Disease Models, Animal|ErbB Receptors|Humans|Kinetics|Male|Mice|Models, Biological|Neoplasms|Organ Specificity|PTEN Phosphohydrolase|Prostate|Receptor, IGF Type 1|Signal Transduction|Software|Systems Biology', 'Journal Article', '10.1186/s12859-019-2933-z', '31288758', 'In cancer research, robustness of a complex biochemical network is one of the most relevant properties to investigate for the development of novel targeted therapies. In cancer systems biology, biological networks are typically modeled through Ordinary Differential Equation (ODE) models. Hence, robustness analysis consists in quantifying how much the temporal behavior of a specific node is influenced by the perturbation of model parameters. The Conditional Robustness Algorithm (CRA) is a valuable methodology to perform robustness analysis on a selected output variable, representative of the proliferation activity of cancer disease.'),
(99141, 2019, 'bmc', 'Integration of probabilistic regulatory networks into constraint-based models of metabolism with applications to Alzheimers disease', 'Yu, Han|Blair, Rachael Hageman', 'State University of New York at Buffalo, 3435 Main Street, Buffalo, 14214, US.|State University of New York at Buffalo, 3435 Main Street, Buffalo, 14214, US. hageman@buffalo.edu.', '-|-', 0, 'Alzheimer Disease|Bayes Theorem|Enzymes|Gene Regulatory Networks|Humans|Metabolic Networks and Pathways|Models, Biological|Models, Statistical|Phenotype|Signal Transduction', 'Journal Article', '10.1186/s12859-019-2872-8', '31291905', 'Mathematical models of biological networks can provide important predictions and insights into complex disease. Constraint-based models of cellular metabolism and probabilistic models of gene regulatory networks are two distinct areas that have progressed rapidly in parallel over the past decade. In principle, gene regulatory networks and metabolic networks underly the same complex phenotypes and diseases. However, systematic integration of these two model systems remains a fundamental challenge.'),
(99142, 2019, 'bmc', 'PEPOP 20: new approaches to mimic non-continuous epitopes', 'Demolombe, Vincent|de Brevern, Alexandre G|Felicori, Liza|NGuyen, Christophe|Machado de Avila, Ricardo Andrez|Valera, Lionel|Jardin-Watelet, Bénédicte|Lavigne, Géraldine|Lebreton, Aurélien|Molina, Franck|Moreau, Violaine', 'BPMP, CNRS, INRA, Montpellier SupAgro, Univ Montpellier, Montpellier, France.|INSERM UMR-S 1134, DSIMB, F-75739, Paris, France.|Departamento de Bioquímica e Imunologia, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil.|Sys2Diag UMR 9005 CNRS/ALCEDIAG, Complex System Modeling and Engineering for Diagnosis, Cap delta/Parc Euromédecine, 1682 rue de la Valsière CS 61003, 34184, Montpellier Cedex 4, France.|Programa de Pós-Graduação em Ciências da Saúde, Universidade do Extremo Sul Catarinense, Criciúma, Santa Catarina, 88806-000, Brazil.|Bio-Rad Laboratories, 1682 Rue de la Valsière CS 61003, 34184, Montpellier CEDEX 04, France.|Bio-Rad Laboratories, 1682 Rue de la Valsière CS 61003, 34184, Montpellier CEDEX 04, France.|Department of Haematology, University Hospital, Nîmes, France.|Service dhématologie biologique, CHU Clermont-Ferrand, Clermont-Ferrand, France.|Sys2Diag UMR 9005 CNRS/ALCEDIAG, Complex System Modeling and Engineering for Diagnosis, Cap delta/Parc Euromédecine, 1682 rue de la Valsière CS 61003, 34184, Montpellier Cedex 4, France.|Centre de Biochimie Structurale (CBS), INSERM, CNRS, Univ Montpellier, 29, route de Navacelles, 34090, Montpellier, France. violaine.moreau@cbs.cnrs.fr.', 'France|France|Brazil|France|Brazil|France|France|France|France|France|France', 2, 'Amino Acid Sequence|Animals|Antibodies, Monoclonal|Computational Biology|Epitopes|Immune Sera|Internet|Mice|Peptides|Protein Domains|Proteins|Software', 'Journal Article', '10.1186/s12859-019-2867-5', '31296178', 'Bioinformatics methods are helpful to identify new molecules for diagnostic or therapeutic applications. For example, the use of peptides capable of mimicking binding sites has several benefits in replacing a protein which is difficult to produce, or toxic. Using peptides is less expensive. Peptides are easier to manipulate, and can be used as drugs. Continuous epitopes predicted by bioinformatics tools are commonly used and these sequential epitopes are used as is in further experiments. Numerous discontinuous epitope predictors have been developed but only two bioinformatics tools have been proposed so far to predict peptide sequences: Superficial and PEPOP 2.0. PEPOP 2.0 can generate series of peptide sequences that can replace continuous or discontinuous epitopes in their interaction with their cognate antibody.'),
(99143, 2019, 'bmc', 'RESCUE: imputing dropout events in single-cell RNA-sequencing data', 'Tracy, Sam|Yuan, Guo-Cheng|Dries, Ruben', 'Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.|Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA. rdries@jimmy.harvard.edu.', 'United States|United States|United States', 0, 'Bias|Cells|Computer Simulation|Gene Expression Regulation|Humans|RNA|Sequence Analysis, RNA|Single-Cell Analysis|Software', 'Journal Article', '10.1186/s12859-019-2977-0', '31299886', 'Single-cell RNA-sequencing technologies provide a powerful tool for systematic dissection of cellular heterogeneity. However, the prevalence of dropout events imposes complications during data analysis and, despite numerous efforts from the community, this challenge has yet to be solved.'),
(99144, 2019, 'bmc', 'Viral coinfection analysis using a MinHash toolkit', 'Dawson, Eric T|Wagner, Sarah|Roberson, David|Yeager, Meredith|Boland, Joseph|Garrison, Erik|Chanock, Stephen|Schiffman, Mark|Raine-Bennett, Tina|Lorey, Thomas|Castle, Phillip E|Mirabello, Lisa|Durbin, Richard', 'Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA.|Cancer Genomics Research Laboratory, Leidos Biomedical Research Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA.|Cancer Genomics Research Laboratory, Leidos Biomedical Research Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA.|Cancer Genomics Research Laboratory, Leidos Biomedical Research Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA.|Cancer Genomics Research Laboratory, Leidos Biomedical Research Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA.|Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.|Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA.|Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA.|Womens Health Research Institute, Kaiser Permanente Northern California, Oakland, California, USA.|Regional Laboratory, Kaiser Permanente Northern California, Oakland, California, USA.|Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, USA.|Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA.|Department of Genetics, University of Cambridge, Cambridge, UK. rd109@cam.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Coinfection|Computational Biology|DNA, Viral|Human papillomavirus 16|Humans|Papillomavirus Infections|Phylogeny|Sequence Analysis, DNA|Software|Time Factors', 'Journal Article', '10.1186/s12859-019-2918-y', '31299914', 'Human papillomavirus (HPV) is a common sexually transmitted infection associated with cervical cancer that frequently occurs as a coinfection of types and subtypes. Highly similar sublineages that show over 100-fold differences in cancer risk are not distinguishable in coinfections with current typing methods.'),
(99145, 2019, 'bmc', 'RAFTS3G: an efficient and versatile clustering software to analyses in large protein datasets', 'de Lima Nichio, Bruno Thiago|de Oliveira, Aryel Marlus Repula|de Pierri, Camilla Reginatto|Santos, Leticia Graziela Costa|Lejambre, Alexandre Quadros|Vialle, Ricardo Assunção|da Rocha Coimbra, Nilson Antônio|Guizelini, Dieval|Marchaukoski, Jeroniza Nunes|de Oliveira Pedrosa, Fabio|Raittz, Roberto Tadeu', 'Laboratory of Bioinformatics, Professional and Technical Education Sector from the Federal University of Paraná, Curitiba, PR, Brazil.|Laboratory of Bioinformatics, Professional and Technical Education Sector from the Federal University of Paraná, Curitiba, PR, Brazil.|Laboratory of Bioinformatics, Professional and Technical Education Sector from the Federal University of Paraná, Curitiba, PR, Brazil.|Laboratory of Bioinformatics, Professional and Technical Education Sector from the Federal University of Paraná, Curitiba, PR, Brazil.|Laboratory of Bioinformatics, Professional and Technical Education Sector from the Federal University of Paraná, Curitiba, PR, Brazil.|Laboratory of Bioinformatics, Professional and Technical Education Sector from the Federal University of Paraná, Curitiba, PR, Brazil.|Laboratory of Bioinformatics, Professional and Technical Education Sector from the Federal University of Paraná, Curitiba, PR, Brazil.|Laboratory of Bioinformatics, Professional and Technical Education Sector from the Federal University of Paraná, Curitiba, PR, Brazil.|Laboratory of Bioinformatics, Professional and Technical Education Sector from the Federal University of Paraná, Curitiba, PR, Brazil.|Laboratory of Bioinformatics, Professional and Technical Education Sector from the Federal University of Paraná, Curitiba, PR, Brazil.|Laboratory of Bioinformatics, Professional and Technical Education Sector from the Federal University of Paraná, Curitiba, PR, Brazil. raittz@ufpr.br.', 'Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil', 0, 'Algorithms|Cluster Analysis|Data Mining|Databases, Protein|Proteins|Software', 'Journal Article', '10.1186/s12859-019-2973-4', '31307371', 'Clustering methods are essential to partitioning biological samples being useful to minimize the information complexity in large datasets. Tools in this context usually generates data with greed algorithms that solves some Data Mining difficulties which can degrade biological relevant information during the clustering process. The lack of standardization of metrics and consistent bases also raises questions about the clustering efficiency of some methods. Benchmarks are needed to explore the full potential of clustering methods - in which alignment-free methods stand out - and the good choice of dataset makes it essentials.'),
(99146, 2019, 'bmc', 'Stochastic modeling of aging cells reveals how damage accumulation, repair, and cell-division asymmetry affect clonal senescence and population fitness', 'Song, Ruijie|Acar, Murat', 'Systems Biology Institute, Yale University, 850 West Campus Drive, West Haven, CT, 06516, USA.|Systems Biology Institute, Yale University, 850 West Campus Drive, West Haven, CT, 06516, USA. murat.acar@yale.edu.', 'United States|United States', 3, 'Aging|Animals|Cell Division|DNA Damage|DNA Repair|Germ Cells|Humans|Models, Biological|Stochastic Processes', 'Journal Article', '10.1186/s12859-019-2921-3', '31307385', 'Asymmetry during cellular division, both in the uneven partitioning of damaged cellular components and of cell volume, is a cell biological phenomenon experienced by many unicellular organisms. Previous work based on a deterministic model claimed that such asymmetry in the partitioning of cell volume and of aging-associated damage confers a fitness benefit in avoiding clonal senescence, primarily by diversifying the cellular population. However, clonal populations of unicellular organisms are already naturally diversified due to the inherent stochasticity of biological processes.'),
(99147, 2019, 'bmc', 'A multivariate Poisson-log normal mixture model for clustering transcriptome sequencing data', 'Silva, Anjali|Rothstein, Steven J|McNicholas, Paul D|Subedi, Sanjeena', 'Department of Mathematics and Statistics, University of Guelph, Guelph, N1G 2W1, Canada.|Department of Molecular and Cellular Biology, University of Guelph, Guelph, N1G 2W1, Ontario, Canada.|Department of Mathematics and Statistics, McMaster University, Hamilton, L8S 4K1, Ontario, Canada.|Department of Mathematical Sciences, Binghamton University, Binghamton, 13902, New York, USA. sdang@binghamton.edu.', 'Canada|Canada|Canada|United States', 0, 'Algorithms|Cluster Analysis|High-Throughput Nucleotide Sequencing|Markov Chains|Models, Theoretical|Monte Carlo Method|RNA|Sequence Analysis, RNA|User-Computer Interface', 'Journal Article', '10.1186/s12859-019-2916-0', '31311497', 'High-dimensional data of discrete and skewed nature is commonly encountered in high-throughput sequencing studies. Analyzing the network itself or the interplay between genes in this type of data continues to present many challenges. As data visualization techniques become cumbersome for higher dimensions and unconvincing when there is no clear separation between homogeneous subgroups within the data, cluster analysis provides an intuitive alternative. The aim of applying mixture model-based clustering in this context is to discover groups of co-expressed genes, which can shed light on biological functions and pathways of gene products.'),
(99148, 2019, 'bmc', 'miRFA: an automated pipeline for microRNA functional analysis with correlation support from TCGA and TCPA expression data in pancreatic cancer', 'Borgmästars, Emmy|de Weerd, Hendrik Arnold|Lubovac-Pilav, Zelmina|Sund, Malin', 'Department of Surgical and Perioperative Sciences, Umeå University, Umeå, Sweden. emmy.borgmastars@umu.se.|School of bioscience, Systems Biology Research Centre, University of Skövde, Skövde, Sweden.|School of bioscience, Systems Biology Research Centre, University of Skövde, Skövde, Sweden.|Department of Surgical and Perioperative Sciences, Umeå University, Umeå, Sweden.', 'Sweden|Sweden|Sweden|Sweden', 1, 'Automation|Humans|Kaplan-Meier Estimate|MicroRNAs|Pancreatic Neoplasms|Protein Interaction Maps|Proteins|RNA, Messenger|User-Computer Interface', 'Journal Article', '10.1186/s12859-019-2974-3', '31311505', 'MicroRNAs (miRNAs) are small RNAs that regulate gene expression at a post-transcriptional level and are emerging as potentially important biomarkers for various disease states, including pancreatic cancer. In silico-based functional analysis of miRNAs usually consists of miRNA target prediction and functional enrichment analysis of miRNA targets. Since miRNA target prediction methods generate a large number of false positive target genes, further validation to narrow down interesting candidate miRNA targets is needed. One commonly used method correlates miRNA and mRNA expression to assess the regulatory effect of a particular miRNA. The aim of this study was to build a bioinformatics pipeline in R for miRNA functional analysis including correlation analyses between miRNA expression levels and its targets on mRNA and protein expression levels available from the cancer genome atlas (TCGA) and the cancer proteome atlas (TCPA). TCGA-derived expression data of specific mature miRNA isoforms from pancreatic cancer tissue was used.'),
(99149, 2019, 'bmc', 'Extensions of ?1 regularization increase detection specificity for cell-type specific parameters in dynamic models', 'Dolejsch, Pascal|Hass, Helge|Timmer, Jens', 'Institute of Physics, University of Freiburg, Hermann-Herder-Str. 3, Freiburg, 79104, Germany. pascal.dolejsch@merkur.uni-freiburg.de.|Institute of Physics, University of Freiburg, Hermann-Herder-Str. 3, Freiburg, 79104, Germany.|Institute of Physics, University of Freiburg, Hermann-Herder-Str. 3, Freiburg, 79104, Germany.', 'Germany|Germany|Germany', 0, 'Erythropoietin|Humans|Janus Kinase 2|Likelihood Functions|Models, Biological|STAT5 Transcription Factor|Signal Transduction|Systems Biology', 'Journal Article', '10.1186/s12859-019-2976-1', '31311516', 'Background Ordinary differential equation systems are frequently utilized to model biological systems and to infer knowledge about underlying properties. For instance, the development of drugs requires the knowledge to which extent malign cells differ from healthy ones to provide a specific treatment with least side effects. As these cell-type specific properties may stem from any part of biochemical cell processes, systematic quantitative approaches are necessary to identify the relevant potential drug targets. An ?1 regularization for the maximum likelihood parameter estimation proved to be successful, but falsely predicted cell-type dependent behaviour had to be corrected manually by using a Profile Likelihood approach.  Results The choice of extended ?1 penalty functions significantly decreased the number of falsely detected cell-type specific parameters. Thus, the total accuracy of the prediction could be increased. This was tested on a realistic dynamical benchmark model used for the DREAM6 challenge. Among Elastic Net, Adaptive Lasso and a non-convex ?q penalty, the latter one showed the best predictions whilst also requiring least computation time. All extended methods include a hyper-parameter in the regularization function. For an Erythropoietin (EPO) induced signalling pathway, the extended methods ?q and Adaptive Lasso revealed an unpublished alternative parsimonious model when varying the respective hyper-parameters.  Conclusions Using ?q or Adaptive Lasso with an a-priori choice for the hyper-parameter can lead to a more specific and accurate result than ?1. Scanning different hyper-parameters can yield additional pieces of information about the system.'),
(99150, 2019, 'bmc', 'Analysis of networks of host proteins in the early time points following HIV transduction', 'Csxc5x91sz, xc3x89va|Txc3xb3th, Ferenc|Mahdi, Mohamed|Tsaprailis, George|Emri, Miklxc3xb3s|Txc5x91zsxc3xa9r, Jxc3xb3zsef', 'Proteomics Core Facility, Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Debrecen, Egyetem ter 1., Debrecen, 4032, Hungary. cseva@med.unideb.hu.|Laboratory of Retroviral Biochemistry, Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Debrecen, Egyetem ter 1., Debrecen, 4032, Hungary.|Laboratory of Retroviral Biochemistry, Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Debrecen, Egyetem ter 1., Debrecen, 4032, Hungary.|Arizona Research Labs, University of Arizona, PO Box 210066, Administration Building, Room 601, Tucson, AZ, 85721-0066, USA.|Department of Medical Imaging, Division of Nuclear Medicine and Translational Imaging, Faculty of Medicine, University of Debrecen, Nagyerdei krt. 98., Debrecen, 4032, Hungary.|Proteomics Core Facility, Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Debrecen, Egyetem ter 1., Debrecen, 4032, Hungary. tozser@med.unideb.hu.', 'Hungary|Hungary|Hungary|United States|Hungary|Hungary', 3, 'HIV-1|Humans|Mass Spectrometry|Protein Interaction Mapping|Proteome|Proteomics|Transduction, Genetic', 'Journal Article', '10.1186/s12859-019-2990-3', '31315557', 'Utilization of quantitative proteomics data on the network level is still a challenge in proteomics data analysis. Currently existing models use sophisticated, sometimes hard to implement analysis techniques. Our aim was to generate a relatively simple strategy for quantitative proteomics data analysis in order to utilize as much of the data generated in a proteomics experiment as possible.'),
(99151, 2019, 'bmc', 'A novel collaborative filtering model for LncRNA-disease association prediction based on the Naïve Bayesian classifier', 'Yu, Jingwen|Xuan, Zhanwei|Feng, Xiang|Zou, Quan|Wang, Lei', 'College of Computer Engineering & Applied Mathematics, Changsha University, Changsha, Hunan, Peoples Republic of China.|College of Computer Engineering & Applied Mathematics, Changsha University, Changsha, Hunan, Peoples Republic of China.|College of Computer Engineering & Applied Mathematics, Changsha University, Changsha, Hunan, Peoples Republic of China.|Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, Peoples Republic of China.|College of Computer Engineering & Applied Mathematics, Changsha University, Changsha, Hunan, Peoples Republic of China. wanglei@xtu.edu.cn.', 'China|China|China|China|China', 11, 'Algorithms|Bayes Theorem|Colorectal Neoplasms|Computer Simulation|Disease|Glioma|Humans|MicroRNAs|RNA, Long Noncoding|Stomach Neoplasms', 'Journal Article', '10.1186/s12859-019-2985-0', '31315558', 'Since the number of known lncRNA-disease associations verified by biological experiments is quite limited, it has been a challenging task to uncover human disease-related lncRNAs in recent years. Moreover, considering the fact that biological experiments are very expensive and time-consuming, it is important to develop efficient computational models to discover potential lncRNA-disease associations.'),
(99152, 2019, 'bmc', 'MCtandem: an efficient tool for large-scale peptide identification on many integrated core (MIC) architecture', 'Li, Chuang|Li, Kenli|Li, Keqin|Lin, Feng', 'College of Computer Science and Electronic Engineering, Hunan University, Lushannan Road, Changsha, 410082, China.|College of Computer Science and Electronic Engineering, Hunan University, Lushannan Road, Changsha, 410082, China. lkl@hnu.edu.cn.|College of Computer Science and Electronic Engineering, Hunan University, Lushannan Road, Changsha, 410082, China.|School of Computer Science and Engineering, Nanyang Technological University, Nangyang Road, Singapore, 639798, Singapore.', 'China|China|China|Singapore', 0, 'Algorithms|Databases, Protein|Humans|Peptides|Proteomics|Software|Tandem Mass Spectrometry', 'Journal Article', '10.1186/s12859-019-2980-5', '31315562', 'Tandem mass spectrometry (MS/MS)-based database searching is a widely acknowledged and widely used method for peptide identification in shotgun proteomics. However, due to the rapid growth of spectra data produced by advanced mass spectrometry and the greatly increased number of modified and digested peptides identified in recent years, the current methods for peptide database searching cannot rapidly and thoroughly process large MS/MS spectra datasets. A breakthrough in efficient database search algorithms is crucial for peptide identification in computational proteomics.'),
(99153, 2019, 'bmc', 'FunFam protein families improve residue level molecular function prediction', 'Scheibenreif, Linus|Littmann, Maria|Orengo, Christine|Rost, Burkhard', 'Department of Informatics, Bioinformatics & Computational Biology - i12, TUM (Technical University of Munich), Boltzmannstr. 3, 85748, Garching/Munich, Germany. scheibenreif@rostlab.org.|Department of Informatics, Bioinformatics & Computational Biology - i12, TUM (Technical University of Munich), Boltzmannstr. 3, 85748, Garching/Munich, Germany. littmann@rostlab.org.|Department of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK.|Department of Informatics, Bioinformatics & Computational Biology - i12, TUM (Technical University of Munich), Boltzmannstr. 3, 85748, Garching/Munich, Germany.', 'Germany|Germany|United Kingdom|Germany', 1, 'Binding Sites|Databases, Protein|Protein Binding|Protein Domains|Proteins', 'Journal Article', '10.1186/s12859-019-2988-x', '31319797', 'The CATH database provides a hierarchical classification of protein domain structures including a sub-classification of superfamilies into functional families (FunFams). We analyzed the similarity of binding site annotations in these FunFams and incorporated FunFams into the prediction of protein binding residues.'),
(99154, 2019, 'bmc', 'The MultiOmics Explainer: explaining omics results in the context of a pathway/genome database', 'Paley, Suzanne|Karp, Peter D', 'Bioinformatics Research Group, SRI International, 333 Ravenswood Ave, Menlo Park, 94025, CA, USA. paley@ai.sri.com.|Bioinformatics Research Group, SRI International, 333 Ravenswood Ave, Menlo Park, 94025, CA, USA.', 'United States|United States', 2, 'Databases, Factual|Databases, Genetic|Escherichia coli|Gene Expression Profiling|Genome|Metabolic Networks and Pathways|Metabolomics|Proteomics|Software', 'Journal Article', '10.1186/s12859-019-2971-6', '31319812', 'High-throughput experiments can bring to light associations between genes, proteins and/or metabolites, many of which will be explainable by existing knowledge. Our aim is to speed elucidation of such explanations and, in some cases, find explanations that scientists might otherwise overlook.'),
(99155, 2019, 'bmc', 'Visualizing complex feature interactions and feature sharing in genomic deep neural networks', 'Liu, Ge|Zeng, Haoyang|Gifford, David K', 'Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.|Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.|Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. gifford@mit.edu.', 'United States|United States|United States', 2, 'Algorithms|Base Sequence|Databases, Genetic|Deep Learning|Genomics|Histone Code|Histones|Neural Networks (Computer)|Transcription Factors', 'Journal Article', '10.1186/s12859-019-2957-4', '31324140', 'Visualization tools for deep learning models typically focus on discovering key input features without considering how such low level features are combined in intermediate layers to make decisions. Moreover, many of these methods examine a networks response to specific input examples that may be insufficient to reveal the complexity of model decision making.'),
(99156, 2019, 'bmc', 'Chemical-induced disease relation extraction via attention-based distant supervision', 'Gu, Jinghang|Sun, Fuqing|Qian, Longhua|Zhou, Guodong', 'Natural Language Processing Lab, School of Computer Science and Technology, Soochow University, 1 Shizi Street, Suzhou, China.|Department of Gynecology Minimally Invasive Center, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, China.|Natural Language Processing Lab, School of Computer Science and Technology, Soochow University, 1 Shizi Street, Suzhou, China. qianlonghua@suda.edu.cn.|Natural Language Processing Lab, School of Computer Science and Technology, Soochow University, 1 Shizi Street, Suzhou, China.', 'China|China|China|China', 1, 'Algorithms|Databases as Topic|Databases, Factual|Disease|Humans|Neural Networks (Computer)|Supervised Machine Learning|Toxicogenetics|Workflow', 'Journal Article', '10.1186/s12859-019-2884-4', '31331263', 'Automatically understanding chemical-disease relations (CDRs) is crucial in various areas of biomedical research and health care. Supervised machine learning provides a feasible solution to automatically extract relations between biomedical entities from scientific literature, its success, however, heavily depends on large-scale biomedical corpora manually annotated with intensive labor and tremendous investment.'),
(99157, 2019, 'bmc', 'JPhyloIO: a Java library for event-based reading and writing of different phylogenetic file formats through a common interface', 'Stöver, Ben C|Wiechers, Sarah|Müller, Kai F', 'Institute for Evolution and Biodiversity, WWU Münster, Hüfferstraße 1, 48149, Münster, Germany. stoever@bioinfweb.info.|Institute for Evolution and Biodiversity, WWU Münster, Hüfferstraße 1, 48149, Münster, Germany.|Institute for Evolution and Biodiversity, WWU Münster, Hüfferstraße 1, 48149, Münster, Germany.', 'Germany|Germany|Germany', 0, 'Documentation|Metadata|Phylogeny|Programming Languages|Software|User-Computer Interface|Writing', 'Journal Article', '10.1186/s12859-019-2982-3', '31331268', 'Today a variety of phylogenetic file formats exists, some of which are well-established but limited in their data model, while other more recently introduced ones offer advanced features for metadata representation. Although most currently available software only supports the classical formats with a limited metadata model, it would be desirable to have support for the more advanced formats. This is necessary for users to produce richly annotated data that can be efficiently reused and make underlying workflows easily reproducible. A programming library that abstracts over the data and metadata models of the different formats and allows supporting all of them in one step would significantly simplify the development of new and the extension of existing software to address the need for better metadata annotation.'),
(99158, 2019, 'bmc', 'PS-MCL: parallel shotgun coarsened Markov clustering of protein interaction networks', 'Lim, Yongsub|Yu, Injae|Seo, Dongmin|Kang, U|Sael, Lee', 'Data R&D Center, SK Telecom, Gyeonggi, Korea.|School of Computing, KAIST, Daejeon, Korea.|KISTI, Daejeon, Korea.|Department of Computer Science and Engineering, Seoul National University, Seoul, Korea.|Department of Computer Science and Engineering, Seoul National University, Seoul, Korea. saellee@snu.ac.kr.', '-|-|-|-|-', 0, 'Algorithms|Cluster Analysis|Markov Chains|Protein Interaction Maps|Proteins', 'Journal Article', '10.1186/s12859-019-2856-8', '31337329', 'How can we obtain fast and high-quality clusters in genome scale bio-networks? Graph clustering is a powerful tool applied on bio-networks to solve various biological problems such as protein complexes detection, disease module detection, and gene function prediction. Especially, MCL (Markov Clustering) has been spotlighted due to its superior performance on bio-networks. MCL, however, is skewed towards finding a large number of very small clusters (size 1-3) and fails to detect many larger clusters (size 10+). To resolve this fragmentation problem, MLR-MCL (Multi-level Regularized MCL) has been developed. MLR-MCL still suffers from the fragmentation and, in cases, unrealistically large clusters are generated.'),
(99159, 2019, 'bmc', 'Soybean-VCF2Genomes: a database to identify the closest accession in soybean germplasm collection', 'Ha, Jungmin|Jeon, Ho Hwi|Woo, Dong U|Lee, Yejin|Park, Halim|Lee, Joohyeong|Kang, Yang Jae', 'Department of Plant Science and Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul, Republic of Korea.|Division of Applied Life Science Department at Gyeongsang National University, PMBBRC, Jinju, Republic of Korea.|Division of Applied Life Science Department at Gyeongsang National University, PMBBRC, Jinju, Republic of Korea.|Division of Life Science Department at Gyeongsang National University, Jinju, Republic of Korea.|Division of Life Science Department at Gyeongsang National University, Jinju, Republic of Korea.|College of Pharmacy and Research Institute of Pharmaceutical Sciences, Gyeongsang National University, Jinju, South Korea.|Division of Applied Life Science Department at Gyeongsang National University, PMBBRC, Jinju, Republic of Korea. kangyangjae@gnu.ac.kr.', '-|-|-|-|-|-|-', 0, 'Cluster Analysis|Databases, Factual|Genome, Plant|Genotype|High-Throughput Nucleotide Sequencing|Machine Learning|Phenotype|Phylogeny|Principal Component Analysis|Seeds|Soybeans|User-Computer Interface', 'Journal Article', '10.1186/s12859-019-2859-5', '31337332', 'The development of next generation sequencer (NGS) and the analytical methods allowed the researchers to profile their samples more precisely and easier than before. Especially for agriculture, the certification of the genomic background of their plant materials would be important for the reliability of seed market and stable yield as well as for quarantine procedure. However, the analysis of NGS data is still difficult for non-computational researchers or breeders to verify their samples because majority of current softwares for NGS analysis require users to access unfamiliar Linux environment.'),
(99160, 2019, 'bmc', 'Drug repurposing with network reinforcement', 'Nam, Yonghyun|Kim, Myungjun|Chang, Hang-Seok|Shin, Hyunjung', 'Department of Industrial Engineering, Ajou University, 206, World cup-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, 16499, Republic of Korea.|Department of Industrial Engineering, Ajou University, 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 Eonjuro, Gangnam-gu, Seoul, 06273, 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.', '-|-|-|-', 3, 'Area Under Curve|Drug Interactions|Drug Repositioning|Humans|Pharmaceutical Preparations|Proteins|ROC Curve|Supervised Machine Learning', 'Journal Article', '10.1186/s12859-019-2858-6', '31337333', 'Drug repurposing has been motivated to ameliorate low probability of success in drug discovery. For the recent decade, many in silico attempts have received primary attention as a first step to alleviate the high cost and longevity. Such study has taken benefits of abundance, variety, and easy accessibility of pharmaceutical and biomedical data. Utilizing the research friendly environment, in this study, we propose a network-based machine learning algorithm for drug repurposing. Particularly, we show a framework on how to construct a drug network, and how to strengthen the network by employing multiple/heterogeneous types of data.'),
(99161, 2019, 'bmc', 'Pathogenic Protist Transmembranome database (PPTdb): a web-based platform for searching and analysis of protist transmembrane proteins', 'Lee, Chi-Ching|Huang, Po-Jung|Yeh, Yuan-Ming|Chen, Sin-You|Chiu, Cheng-Hsun|Cheng, Wei-Hung|Tang, Petrus', 'Department and Graduate Institute of Computer Science and Information Engineering, Chang Gung University, Taoyuan, Taiwan.|Genomic Medicine Core Laboratory, Chang Gung Memorial Hospital, Linkou, Taiwan.|Genomic Medicine Core Laboratory, Chang Gung Memorial Hospital, Linkou, Taiwan.|Department and Graduate Institute of Computer Science and Information Engineering, Chang Gung University, Taoyuan, Taiwan.|Genomic Medicine Core Laboratory, Chang Gung Memorial Hospital, Linkou, Taiwan.|Department of Parasitology, College of Medicine, Chang Gung University, Taoyuan, Taiwan. whcheng@mail.cgu.edu.tw.|Molecular Infectious Disease Research Center, Chang Gung Memorial Hospital, Linkou, Taiwan. petang@mail.cgu.edu.tw.', '-|-|-|-|-|-|-', 0, 'Databases, Factual|Fungi|Humans|Membrane Transport Proteins|Plants|User-Computer Interface', 'Journal Article', '10.1186/s12859-019-2857-7', '31337335', 'Pathogenic protist membrane transporter proteins play important roles not only in exchanging molecules into and out of cells but also in acquiring nutrients and biosynthetic compounds from their hosts. Currently, there is no centralized protist membrane transporter database published, which makes system-wide comparisons and studies of host-pathogen membranomes difficult to achieve.'),
(99162, 2019, 'bmc', 'Magic-BLAST, an accurate RNA-seq aligner for long and short reads', 'Boratyn, Grzegorz M|Thierry-Mieg, Jean|Thierry-Mieg, Danielle|Busby, Ben|Madden, Thomas L', 'National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, 20894, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, 20894, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, 20894, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, 20894, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, 20894, USA. madden@ncbi.nlm.nih.gov.', 'United States|United States|United States|United States|United States', 20, 'Algorithms|Base Sequence|Databases, Nucleic Acid|Humans|Introns|RNA|ROC Curve|Sequence Alignment|Sequence Analysis, RNA|Software|Time Factors', 'Journal Article', '10.1186/s12859-019-2996-x', '31345161', 'Next-generation sequencing technologies can produce tens of millions of reads, often paired-end, from transcripts or genomes. But few programs can align RNA on the genome and accurately discover introns, especially with long reads. We introduce Magic-BLAST, a new aligner based on ideas from the Magic pipeline.'),
(99163, 2019, 'bmc', 'Prediction and interpretation of miRNA-disease associations based on miRNA target genes using canonical correlation analysis', 'Chen, Hailin|Zhang, Zuping|Feng, Dayi', 'School of Software, East China Jiaotong University, Nanchang, 330013, China. chl_mail@csu.edu.cn.|School of Computer Science and Engineering, Central South University, Changsha, 410083, China.|School of Software, East China Jiaotong University, Nanchang, 330013, China.', 'China|China|China', 6, 'Algorithms|Computational Biology|Databases, Genetic|Disease|Genetic Association Studies|Humans|MicroRNAs|Models, Genetic', 'Journal Article', '10.1186/s12859-019-2998-8', '31345171', 'It has been shown that the deregulation of miRNAs is associated with the development and progression of many human diseases. To reduce time and cost of biological experiments, a number of algorithms have been proposed for predicting miRNA-disease associations. However, the existing methods rarely investigated the cause-and-effect mechanism behind these associations, which hindered further biomedical follow-ups.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(99164, 2019, 'bmc', 'ROBOT: A Tool for Automating Ontology Workflows', 'Jackson, Rebecca C|Balhoff, James P|Douglass, Eric|Harris, Nomi L|Mungall, Christopher J|Overton, James A', 'Knocean Inc., Toronto, Ontario, Canada. rbca.jackson@gmail.com.|Renaissance Computing Institute, University of North Carolina, Chapel Hill, North Carolina, USA.|Lawrence Berkeley National Laboratory, Berkeley, California, USA.|Lawrence Berkeley National Laboratory, Berkeley, California, USA.|Lawrence Berkeley National Laboratory, Berkeley, California, USA.|Knocean Inc., Toronto, Ontario, Canada.', 'Canada|United States|United States|United States|United States|Canada', 4, 'Biological Ontologies|Disease|Humans|Programming Languages|Software|Workflow', 'Journal Article', '10.1186/s12859-019-3002-3', '31357927', 'Ontologies are invaluable in the life sciences, but building and maintaining ontologies often requires a challenging number of distinct tasks such as running automated reasoners and quality control checks, extracting dependencies and application-specific subsets, generating standard reports, and generating release files in multiple formats. Similar to more general software development, automation is the key to executing and managing these tasks effectively and to releasing more robust products in standard forms. For ontologies using the Web Ontology Language (OWL), the OWL API Java library is the foundation for a range of software tools, including the Protégé ontology editor. In the Open Biological and Biomedical Ontologies (OBO) community, we recognized the need to package a wide range of low-level OWL API functionality into a library of common higher-level operations and to make those operations available as a command-line tool.'),
(99165, 2019, 'bmc', 'Improving prediction of phenotypic drug response on cancer cell lines using deep convolutional network', 'Liu, Pengfei|Li, Hongjian|Li, Shuai|Leung, Kwong-Sak', 'Department of Computer Science and Engineering, the Chinese University of Hong Kong, Sha Tin, N.T., Hong Kong, China. pfliu@cse.cuhk.edu.hk.|SDIVF R&D Centre, Hong Kong Science Park, Sha Tin, N.T., Hong Kong, China.|Department of Computer Science and Engineering, the Chinese University of Hong Kong, Sha Tin, N.T., Hong Kong, China.|Department of Computer Science and Engineering, the Chinese University of Hong Kong, Sha Tin, N.T., Hong Kong, China.', 'China|China|China|China', 0, 'Antineoplastic Agents|Cell Line, Tumor|Databases, Factual|Deep Learning|Genomics|Humans|Inhibitory Concentration 50|Neoplasms|Neural Networks (Computer)|Organ Specificity|Phenotype|Regression Analysis', 'Journal Article', '10.1186/s12859-019-2910-6', '31357929', 'Understanding the phenotypic drug response on cancer cell lines plays a vital role in anti-cancer drug discovery and re-purposing. The Genomics of Drug Sensitivity in Cancer (GDSC) database provides open data for researchers in phenotypic screening to build and test their models. Previously, most research in these areas starts from the molecular fingerprints or physiochemical features of drugs, instead of their structures.'),
(99166, 2019, 'bmc', 'IRESpy: an XGBoost model for prediction of internal ribosome entry sites', 'Wang, Junhui|Gribskov, Michael', 'Biological Sciences Department, Purdue University, West Lafayette, IN, USA.|Biological Sciences Department, Purdue University, West Lafayette, IN, USA. mgribsko@purdue.edu.', 'United States|United States', 0, '5 Untranslated Regions|Algorithms|Base Sequence|Computational Biology|Humans|Internal Ribosome Entry Sites|Models, Theoretical|Probability|RNA, Viral|Software', 'Journal Article', '10.1186/s12859-019-2999-7', '31362694', 'Internal ribosome entry sites (IRES) are segments of mRNA found in untranslated regions that can recruit the ribosome and initiate translation independently of the 5 cap-dependent translation initiation mechanism. IRES usually function when 5 cap-dependent translation initiation has been blocked or repressed. They have been widely found to play important roles in viral infections and cellular processes. However, a limited number of confirmed IRES have been reported due to the requirement for highly labor intensive, slow, and low efficiency laboratory experiments. Bioinformatics tools have been developed, but there is no reliable online tool.'),
(99167, 2019, 'bmc', 'Stochastic Lanczos estimation of genomic variance components for linear mixed-effects models', 'Border, Richard|Becker, Stephen', 'Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, 80309, CO, USA. richard.border@colorado.edu.|Department of Applied Mathematics, University of Colorado Boulder, Boulder, 80309, CO, USA.', 'United States|United States', 0, 'Algorithms|Genomics|Likelihood Functions|Linear Models|Monte Carlo Method|Software|Stochastic Processes|Time Factors', 'Journal Article', '10.1186/s12859-019-2978-z', '31362713', 'Linear mixed-effects models (LMM) are a leading method in conducting genome-wide association studies (GWAS) but require residual maximum likelihood (REML) estimation of variance components, which is computationally demanding. Previous work has reduced the computational burden of variance component estimation by replacing direct matrix operations with iterative and stochastic methods and by employing loose tolerances to limit the number of iterations in the REML optimization procedure. Here, we introduce two novel algorithms, stochastic Lanczos derivative-free REML (SLDF_REML) and Lanczos first-order Monte Carlo REML (L_FOMC_REML), that exploit problem structure via the principle of Krylov subspace shift-invariance to speed computation beyond existing methods. Both novel algorithms only require a single round of computation involving iterative matrix operations, after which their respective objectives can be repeatedly evaluated using vector operations. Further, in contrast to existing stochastic methods, SLDF_REML can exploit precomputed genomic relatedness matrices (GRMs), when available, to further speed computation.'),
(99168, 2019, 'bmc', 'HIV drug resistance prediction with weighted categorical kernel functions', 'Ramon, Elies|Belanche-Muñoz, Lluís|Pérez-Enciso, Miguel', 'Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB Consortium, Campus UAB, 08193 Bellaterra, Barcelona, Spain. elies.ramon@cragenomica.es.|Computer Science Department, Technical University of Catalonia, Carrer de Jordi Girona 1-3, 08034, Barcelona, Spain.|Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB Consortium, Campus UAB, 08193 Bellaterra, Barcelona, Spain.', 'Spain|Spain|Spain', 2, 'Algorithms|Anti-HIV Agents|Computational Biology|Drug Resistance, Viral|HIV Infections|HIV-1|Humans|Linear Models|Principal Component Analysis', 'Journal Article', '10.1186/s12859-019-2991-2', '31362714', 'Antiretroviral drugs are a very effective therapy against HIV infection. However, the high mutation rate of HIV permits the emergence of variants that can be resistant to the drug treatment. Predicting drug resistance to previously unobserved variants is therefore very important for an optimum medical treatment. In this paper, we propose the use of weighted categorical kernel functions to predict drug resistance from virus sequence data. These kernel functions are very simple to implement and are able to take into account HIV data particularities, such as allele mixtures, and to weigh the different importance of each protein residue, as it is known that not all positions contribute equally to the resistance.'),
(99169, 2019, 'bmc', 'LncRNA HOTAIR-mediated Wnt/xcexb2-catenin network modeling to predict and validate therapeutic targets for cartilage damage', 'Zhou, Wei|He, Xiaojuan|Chen, Ziyi|Fan, Danping|Wang, Yonghua|Feng, Hui|Zhang, Ge|Lu, Aiping|Xiao, Lianbo', 'Institute of Arthritis Research, Shanghai Academy of Chinese Medical Sciences, Guanghua Integrative Medicine Hospital / Shanghai University of T.C.M, Shanghai, 200052, China.|Institute of Arthritis Research, Shanghai Academy of Chinese Medical Sciences, Guanghua Integrative Medicine Hospital / Shanghai University of T.C.M, Shanghai, 200052, China.|Musculoskeletal Research Laboratory, Department of Orthopaedics & Traumatology, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, 999077, SAR, China.|Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, 100700, China.|College of Life Science, Northwest University, Xian, 710000, Shaanxi, China.|Institute of Arthritis Research, Shanghai Academy of Chinese Medical Sciences, Guanghua Integrative Medicine Hospital / Shanghai University of T.C.M, Shanghai, 200052, China.|Institute of Arthritis Research, Shanghai Academy of Chinese Medical Sciences, Guanghua Integrative Medicine Hospital / Shanghai University of T.C.M, Shanghai, 200052, China. zhangge@hkbu.edu.hk.|Institute of Arthritis Research, Shanghai Academy of Chinese Medical Sciences, Guanghua Integrative Medicine Hospital / Shanghai University of T.C.M, Shanghai, 200052, China. aipinglu@hkbu.edu.hk.|Institute of Arthritis Research, Shanghai Academy of Chinese Medical Sciences, Guanghua Integrative Medicine Hospital / Shanghai University of T.C.M, Shanghai, 200052, China. xlb@medmail.com.cn.', 'China|China|China|China|China|China|China|China|China', 4, 'Axin Protein|Cartilage, Articular|Gene Regulatory Networks|Humans|Matrix Metalloproteinase 13|Models, Biological|Molecular Targeted Therapy|RNA, Long Noncoding|Reproducibility of Results|Time Factors|Wnt Signaling Pathway', 'Journal Article', '10.1186/s12859-019-2981-4', '31366320', 'Cartilage damage is a crucial feature involved in several pathological conditions characterized by joint disorders, such as osteoarthritis and rheumatoid arthritis. Accumulated evidences showed that Wnt/xcexb2-catenin pathway plays a role in the pathogenesis of cartilage damage. In addition, it is experimentally documented that lncRNA (long non-coding RNA) HOTAIR plays a key role in the regulation of Wnt/xcexb2-catenin pathway based on directly decreased WIF-1 expression. Further, it is reported that Wnt/xcexb2-catenin pathway is a potent pathway to regulate the expression of MMP-13, which is responsible for degradation of collagen type II in articular cartilage. It is increasingly recognized that systems modeling approach provides an opportunity to understand the complex relationships and direct quantitative analysis of dynamic network in various diseases.'),
(99170, 2019, 'bmc', 'DROPA: DRIP-seq optimized peak annotator', 'Russo, Marco|De Lucca, Bruno|Flati, Tiziano|Gioiosa, Silvia|Chillemi, Giovanni|Capranico, Giovanni', 'Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy.|Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy.|National Council of Research, CNR, Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, Bari, Italy.|National Council of Research, CNR, Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, Bari, Italy.|National Council of Research, CNR, Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, Bari, Italy.|Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy. giovanni.capranico@unibo.it.', 'Italy|Italy|Italy|Italy|Italy|Italy', 3, 'Base Sequence|DNA|Gene Expression Regulation|Immunoprecipitation|Molecular Sequence Annotation|RNA|Software', 'Journal Article', '10.1186/s12859-019-3009-9', '31387525', 'R-loops are three-stranded nucleic acid structures that usually form during transcription and that may lead to gene regulation or genome instability. DRIP (DNA:RNA Immunoprecipitation)-seq techniques are widely used to map R-loops genome-wide providing insights into R-loop biology. However, annotation of DRIP-seq peaks to genes can be a tricky step, due to the lack of strand information when using the common basic DRIP technique.'),
(99171, 2019, 'bmc', 'Novel deep learning model for more accurate prediction of drug-drug interaction effects', 'Lee, Geonhee|Park, Chihyun|Ahn, Jaegyoon', 'Department of Computer Science and Engineering, Incheon National University, Incheon, 22012, South Korea.|Department of Computer Sciences, Yonsei University, Seoul, 03722, South Korea.|Department of Computer Science and Engineering, Incheon National University, Incheon, 22012, South Korea. jgahn@inu.ac.kr.', '-|-|-', 1, 'Area Under Curve|Databases, Factual|Deep Learning|Drug Interactions|Humans|Models, Theoretical|Neural Networks (Computer)|Support Vector Machine', 'Journal Article', '10.1186/s12859-019-3013-0', '31387547', 'Predicting the effect of drug-drug interactions (DDIs) precisely is important for safer and more effective drug co-prescription. Many computational approaches to predict the effect of DDIs have been proposed, with the aim of reducing the effort of identifying these interactions in vivo or in vitro, but room remains for improvement in prediction performance.'),
(99172, 2019, 'bmc', 'Yanagi: Fast and interpretable segment-based alternative splicing and gene expression analysis', 'Gunady, Mohamed K|Mount, Stephen M|Corrada Bravo, Héctor', 'Department of Computer Science, University of Maryland, College Park, Maryland, USA.|Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland, USA.|Department of Computer Science, University of Maryland, College Park, Maryland, USA. hcorrada@umiacs.umd.edu.', 'United States|United States|United States', 0, 'Algorithms|Alternative Splicing|Animals|Area Under Curve|Drosophila|Humans|RNA|ROC Curve|Sequence Analysis, RNA|Transcriptome', 'Journal Article', '10.1186/s12859-019-2947-6', '31409274', 'Ultra-fast pseudo-alignment approaches are the tool of choice in transcript-level RNA sequencing (RNA-seq) analyses. Unfortunately, these methods couple the tasks of pseudo-alignment and transcript quantification. This coupling precludes the direct usage of pseudo-alignment to other expression analyses, including alternative splicing or differential gene expression analysis, without including a non-essential transcript quantification step.'),
(99173, 2019, 'bmc', 'Estimating statistical significance of local protein profile-profile alignments', 'Margelevixc4x8dius, Mindaugas', 'Institute of Biotechnology, Life Sciences Center, Vilnius University, Saulxc4x97tekio al. 7, Vilnius, 10257, Lithuania. mindaugas.margelevicius@bti.vu.lt.', 'Lithuania', 0, 'Algorithms|Amino Acid Sequence|Models, Theoretical|Protein Conformation|Proteins|Sequence Alignment', 'Journal Article', '10.1186/s12859-019-2913-3', '31409275', 'Alignment of sequence families described by profiles provides a sensitive means for establishing homology between proteins and is important in protein evolutionary, structural, and functional studies. In the context of a steadily growing amount of sequence data, estimating the statistical significance of alignments, including profile-profile alignments, plays a key role in alignment-based homology search algorithms. Still, it is an open question as to what and whether one type of distribution governs profile-profile alignment score, especially when profile-profile substitution scores involve such terms as secondary structure predictions.'),
(99174, 2019, 'bmc', 'DynOVis: a web tool to study dynamic perturbations for capturing dose-over-time effects in biological networks', 'Kuijpers, T J M|Wolters, J E J|Kleinjans, J C S|Jennen, D G J', 'Department of Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht University, P.O. Box 616, Maastricht, 6200 MD, The Netherlands. tim.kuijpers@maastrichtuniversity.nl.|Department of Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht University, P.O. Box 616, Maastricht, 6200 MD, The Netherlands.|Department of Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht University, P.O. Box 616, Maastricht, 6200 MD, The Netherlands.|Department of Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht University, P.O. Box 616, Maastricht, 6200 MD, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Acetaminophen|Computational Biology|Databases, Factual|Gene Regulatory Networks|Humans|NF-kappa B|Signal Transduction|User-Computer Interface', 'Journal Article', '10.1186/s12859-019-2995-y', '31409281', 'The development of high throughput sequencing techniques provides us with the possibilities to obtain large data sets, which capture the effect of dynamic perturbations on cellular processes. However, because of the dynamic nature of these processes, the analysis of the results is challenging. Therefore, there is a great need for bioinformatics tools that address this problem.'),
(99175, 2019, 'bmc', 'LSX: automated reduction of gene-specific lineage evolutionary rate heterogeneity for multi-gene phylogeny inference', 'Rivera-Rivera, Carlos J|Montoya-Burgos, Juan I', 'Department of Genetics and Evolution, University of Geneva, Quai Ernest-Ansermet 30, 1211, Geneva, Switzerland.|Department of Genetics and Evolution, University of Geneva, Quai Ernest-Ansermet 30, 1211, Geneva, Switzerland. juan.montoya@unige.ch.', 'Switzerland|Switzerland', 0, 'Algorithms|Biological Evolution|Computational Biology|Evolution, Molecular|Phylogeny|Software', 'Journal Article', '10.1186/s12859-019-3020-1', '31409290', 'Background Lineage rate heterogeneity can be a major source of bias, especially in multi-gene phylogeny inference. We had previously tackled this issue by developing LS3, a data subselection algorithm that, by removing fast-evolving sequences in a gene-specific manner, identifies subsets of sequences that evolve at a relatively homogeneous rate. However, this algorithm had two major shortcomings: (i) it was automated and published as a set of bash scripts, and hence was Linux-specific, and not user friendly, and (ii) it could result in very stringent sequence subselection when extremely slow-evolving sequences were present.  Results We address these challenges and produce a new, platform-independent program, LSX, written in R, which includes a reprogrammed version of the original LS3 algorithm and has added features to make better lineage rate calculations. In addition, we developed and included an alternative version of the algorithm, LS4, which reduces lineage rate heterogeneity by detecting sequences that evolve too fast and sequences that evolve too slow, resulting in less stringent data subselection when extremely slow-evolving sequences are present. The efficiency of LSX and of LS4 with datasets with extremely slow-evolving sequences is demonstrated with simulated data, and by the resolution of a contentious node in the catfish phylogeny that was affected by an unusually high lineage rate heterogeneity in the dataset.  Conclusions LSX is a new bioinformatic tool, with an accessible code, and with which the effect of lineage rate heterogeneity can be explored in gene sequence datasets of virtually any size. In addition, the two modalities of the sequence subsampling algorithm included, LS3 and LS4, allow the user to optimize the amount of non-phylogenetic signal removed while keeping a maximum of phylogenetic signal.'),
(99176, 2019, 'bmc', 'Guide for library design and bias correction for large-scale transcriptome studies using highly multiplexed RNAseq methods', 'Katayama, Shintaro|Skoog, Tiina|Söderhäll, Cilla|Einarsdottir, Elisabet|Krjutškov, Kaarel|Kere, Juha', 'Department of Biosciences and Nutrition, Karolinska Institutet, 14183, Huddinge, Sweden. shintaro.katayama@ki.se.|Department of Biosciences and Nutrition, Karolinska Institutet, 14183, Huddinge, Sweden.|Department of Biosciences and Nutrition, Karolinska Institutet, 14183, Huddinge, Sweden.|Department of Biosciences and Nutrition, Karolinska Institutet, 14183, Huddinge, Sweden.|Department of Biosciences and Nutrition, Karolinska Institutet, 14183, Huddinge, Sweden.|Department of Biosciences and Nutrition, Karolinska Institutet, 14183, Huddinge, Sweden.', 'Sweden|Sweden|Sweden|Sweden|Sweden|Sweden', 3, 'Cell Line|Cluster Analysis|Gene Library|High-Throughput Nucleotide Sequencing|Humans|Principal Component Analysis|RNA|Sequence Analysis, RNA|Transcriptome|User-Computer Interface', 'Journal Article', '10.1186/s12859-019-3017-9', '31409293', 'Standard RNAseq methods using bulk RNA and recent single-cell RNAseq methods use DNA barcodes to identify samples and cells, and the barcoded cDNAs are pooled into a library pool before high throughput sequencing. In cases of single-cell and low-input RNAseq methods, the library is further amplified by PCR after the pooling. Preparation of hundreds or more samples for a large study often requires multiple library pools. However, sometimes correlation between expression profiles among the libraries is low and batch effect biases make integration of data between library pools difficult.'),
(99177, 2019, 'bmc', 'Additional Neural Matrix Factorization model for computational drug repositioning', 'Yang, Xinxing|Zamit, Lbrahim|Liu, Yu|He, Jieyue', 'School of Computer Science and Engineering, Key Lab of Computer Network & Information Integration, MOE, Southeast University, Nanjing, 210018, China.|School of Computer Science and Engineering, Key Lab of Computer Network & Information Integration, MOE, Southeast University, Nanjing, 210018, China.|School of Computer Science and Engineering, Key Lab of Computer Network & Information Integration, MOE, Southeast University, Nanjing, 210018, China.|School of Computer Science and Engineering, Key Lab of Computer Network & Information Integration, MOE, Southeast University, Nanjing, 210018, China. jieyuehe@seu.edu.cn.', 'China|China|China|China', 0, 'Algorithms|Computational Biology|Databases as Topic|Drug Discovery|Drug Repositioning|Humans|Models, Theoretical|Reproducibility of Results', 'Journal Article', '10.1186/s12859-019-2983-2', '31412762', 'Computational drug repositioning, which aims to find new applications for existing drugs, is gaining more attention from the pharmaceutical companies due to its low attrition rate, reduced cost, and shorter timelines for novel drug discovery. Nowadays, a growing number of researchers are utilizing the concept of recommendation systems to answer the question of drug repositioning. Nevertheless, there still lie some challenges to be addressed: 1) Learning ability deficiencies, the adopted model cannot learn a higher level of drug-disease associations from the data. 2) Data sparseness limits the generalization ability of the model. 3)Model is easy to overfit if the effect of negative samples is not taken into consideration.'),
(99178, 2019, 'bmc', 'UNIPred-Web: a web tool for the integration and visualization of biomolecular networks for protein function prediction', 'Perlasca, Paolo|Frasca, Marco|Ba, Cheick Tidiane|Notaro, Marco|Petrini, Alessandro|Casiraghi, Elena|Grossi, Giuliano|Gliozzo, Jessica|Valentini, Giorgio|Mesiti, Marco', 'Department of Computer Science, Università degli Studi di Milano, Via Celoria 18, Milano, 20133, Italy.|Department of Computer Science, Università degli Studi di Milano, Via Celoria 18, Milano, 20133, Italy.|Department of Computer Science, Università degli Studi di Milano, Via Celoria 18, Milano, 20133, Italy.|Department of Computer Science, Università degli Studi di Milano, Via Celoria 18, Milano, 20133, Italy.|Department of Computer Science, Università degli Studi di Milano, Via Celoria 18, Milano, 20133, Italy.|Department of Computer Science, Università degli Studi di Milano, Via Celoria 18, Milano, 20133, Italy.|Department of Computer Science, Università degli Studi di Milano, Via Celoria 18, Milano, 20133, Italy.|Department of Computer Science, Università degli Studi di Milano, Via Celoria 18, Milano, 20133, Italy.|Department of Computer Science, Università degli Studi di Milano, Via Celoria 18, Milano, 20133, Italy.|Department of Computer Science, Università degli Studi di Milano, Via Celoria 18, Milano, 20133, Italy. mesiti@di.unimi.it.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 1, 'Algorithms|Computational Biology|Internet|Protein Interaction Maps|Proteins|Software|User-Computer Interface', 'Journal Article', '10.1186/s12859-019-2959-2', '31412768', 'One of the main issues in the automated protein function prediction (AFP) problem is the integration of multiple networked data sources. The UNIPred algorithm was thereby proposed to efficiently integrate -in a function-specific fashion- the protein networks by taking into account the imbalance that characterizes protein annotations, and to subsequently predict novel hypotheses about unannotated proteins. UNIPred is publicly available as R code, which might result of limited usage for non-expert users. Moreover, its application requires efforts in the acquisition and preparation of the networks to be integrated. Finally, the UNIPred source code does not handle the visualization of the resulting consensus network, whereas suitable views of the network topology are necessary to explore and interpret existing protein relationships.'),
(99179, 2019, 'bmc', 'Multiple-kernel learning for genomic data mining and prediction', 'Wilson, Christopher M|Li, Kaiqiao|Yu, Xiaoqing|Kuan, Pei-Fen|Wang, Xuefeng', 'Department of Biostatistics and Bioinformatics at Moffitt Cancer Center, Tampa, FL, USA.|Department of Applied Mathematics and Statistics at Stony Brook University, Stony Brook, NY, USA.|Department of Biostatistics and Bioinformatics at Moffitt Cancer Center, Tampa, FL, USA.|Department of Applied Mathematics and Statistics at Stony Brook University, Stony Brook, NY, USA.|Department of Biostatistics and Bioinformatics at Moffitt Cancer Center, Tampa, FL, USA. xuefeng.wang@moffitt.org.', 'United States|United States|United States|United States|United States', 6, '-', 'Journal Article', '10.1186/s12859-019-2992-1', '31416413', 'Advances in medical technology have allowed for customized prognosis, diagnosis, and treatment regimens that utilize multiple heterogeneous data sources. Multiple kernel learning (MKL) is well suited for the integration of multiple high throughput data sources. MKL remains to be under-utilized by genomic researchers partly due to the lack of unified guidelines for its use, and benchmark genomic datasets.'),
(99180, 2019, 'bmc', 'Indirect association and ranking hypotheses for literature based discovery', 'Henry, Sam|McInnes, Bridget T', 'Department of Computer Science, Virginia Commonwealth University, 601 W. Main St. Rm 435, Richmond, 23284, USA. henryst@vcu.edu.|Department of Computer Science, Virginia Commonwealth University, 601 W. Main St. Rm 435, Richmond, 23284, USA.', 'United States|United States', 2, '-', 'Journal Article', '10.1186/s12859-019-2989-9', '31416434', 'Literature Based Discovery (LBD) produces more potential hypotheses than can be manually reviewed, making automatically ranking these hypotheses critical. In this paper, we introduce the indirect association measures of Linking Term Association (LTA), Minimum Weight Association (MWA), and Shared B to C Set Association (SBC), and compare them to Linking Set Association (LSA), concept embeddings vector cosine, Linking Term Count (LTC), and direct co-occurrence vector cosine. Our proposed indirect association measures extend traditional association measures to quantify indirect rather than direct associations while preserving valuable statistical properties.'),
(99181, 2019, 'bmc', 'FQStat: a parallel architecture for very high-speed assessment of sequencing quality metrics', 'Chanumolu, Sree K|Albahrani, Mustafa|Otu, Hasan H', 'Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, Lincoln, NE, 68588, USA.|Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, Lincoln, NE, 68588, USA.|Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, Lincoln, NE, 68588, USA. hotu2@unl.edu.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1186/s12859-019-3015-y', '31416440', 'High throughput DNA/RNA sequencing has revolutionized biological and clinical research. Sequencing is widely used, and generates very large amounts of data, mainly due to reduced cost and advanced technologies. Quickly assessing the quality of giga-to-tera base levels of sequencing data has become a routine but important task. Identification and elimination of low-quality sequence data is crucial for reliability of downstream analysis results. There is a need for a high-speed tool that uses optimized parallel programming for batch processing and simply gauges the quality of sequencing data from multiple datasets independent of any other processing steps.'),
(99182, 2019, 'bmc', 'RF_Purify: a novel tool for comprehensive analysis of tumor-purity in methylation array data based on random forest regression', 'Johann, Pascal David|Jäger, Natalie|Pfister, Stefan M|Sill, Martin', 'Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany. p.johann@kitz-heidelberg.de.|Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.|Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.|Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.', 'Germany|Germany|Germany|Germany', 4, '-', 'Journal Article', '10.1186/s12859-019-3014-z', '31419933', 'With the advent of array-based techniques to measure methylation levels in primary tumor samples, systematic investigations of methylomes have widely been performed on a large number of tumor entities. Most of these approaches are not based on measuring individual cell methylation but rather the bulk tumor sample DNA, which contains a mixture of tumor cells, infiltrating immune cells and other stromal components. This raises questions about the purity of a certain tumor sample, given the varying degrees of stromal infiltration in different entities. Previous methods to infer tumor purity require or are based on the use of matching control samples which are rarely available. Here we present a novel, reference free method to quantify tumor purity, based on two Random Forest classifiers, which were trained on ABSOLUTE as well as ESTIMATE purity values from TCGA tumor samples. We subsequently apply this method to a previously published, large dataset of brain tumors, proving that these models perform well in datasets that have not been characterized with respect to tumor purity .'),
(99183, 2019, 'bmc', 'VIST - a Variant-Information Search Tool for precision oncology', 'Ševa, Jurica|Wiegandt, David Luis|Götze, Julian|Lamping, Mario|Rieke, Damian|Schäfer, Reinhold|Jähnichen, Patrick|Kittner, Madeleine|Pallarz, Steffen|Starlinger, Johannes|Keilholz, Ulrich|Leser, Ulf', 'Knowledge Management in Bioinformatics, Department of Computer Science, Humboldt-Universität zu Berlin, Rudower Chaussee 25, Berlin, 12489, Germany.|Knowledge Management in Bioinformatics, Department of Computer Science, Humboldt-Universität zu Berlin, Rudower Chaussee 25, Berlin, 12489, Germany.|University Hospital Tübingen, Hoppe-Seyler-Straße 3, Tübingen, 72076, Germany.|Charité Comprehensive Cancer Center, Charitéplatz 1, Berlin, 10117, Germany.|Charité Comprehensive Cancer Center, Charitéplatz 1, Berlin, 10117, Germany.|Charité Comprehensive Cancer Center, Charitéplatz 1, Berlin, 10117, Germany.|Knowledge Management in Bioinformatics, Department of Computer Science, Humboldt-Universität zu Berlin, Rudower Chaussee 25, Berlin, 12489, Germany.|Knowledge Management in Bioinformatics, Department of Computer Science, Humboldt-Universität zu Berlin, Rudower Chaussee 25, Berlin, 12489, Germany.|Knowledge Management in Bioinformatics, Department of Computer Science, Humboldt-Universität zu Berlin, Rudower Chaussee 25, Berlin, 12489, Germany.|Knowledge Management in Bioinformatics, Department of Computer Science, Humboldt-Universität zu Berlin, Rudower Chaussee 25, Berlin, 12489, Germany.|Charité Comprehensive Cancer Center, Charitéplatz 1, Berlin, 10117, Germany.|Knowledge Management in Bioinformatics, Department of Computer Science, Humboldt-Universität zu Berlin, Rudower Chaussee 25, Berlin, 12489, Germany. leser@informatik.hu-berlin.de.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, '-', 'Journal Article', '10.1186/s12859-019-2958-3', '31419935', 'Diagnosis and treatment decisions in cancer increasingly depend on a detailed analysis of the mutational status of a patients genome. This analysis relies on previously published information regarding the association of variations to disease progression and possible interventions. Clinicians to a large degree use biomedical search engines to obtain such information, however, the vast majority of scientific publications focus on basic science and have no direct clinical impact. We develop the Variant-Information Search Tool (VIST), a search engine designed for the targeted search of clinically relevant publications given an oncological mutation profile.'),
(99184, 2019, 'bmc', 'Multitask learning for biomedical named entity recognition with cross-sharing structure', 'Wang, Xi|Lyu, Jiagao|Dong, Li|Xu, Ke', 'State Key Laboratory of Software Development Environment, Beihang University, Beijing, 100191, China.|State Key Laboratory of Software Development Environment, Beihang University, Beijing, 100191, China.|State Key Laboratory of Software Development Environment, Beihang University, Beijing, 100191, China.|State Key Laboratory of Software Development Environment, Beihang University, Beijing, 100191, China. kexu@nlsde.buaa.edu.cn.', 'China|China|China|China', 0, '-', 'Journal Article', '10.1186/s12859-019-3000-5', '31419937', 'Biomedical named entity recognition (BioNER) is a fundamental and essential task for biomedical literature mining, which affects the performance of downstream tasks. Most BioNER models rely on domain-specific features or hand-crafted rules, but extracting features from massive data requires much time and human efforts. To solve this, neural network models are used to automatically learn features. Recently, multi-task learning has been applied successfully to neural network models of biomedical literature mining. For BioNER models, using multi-task learning makes use of features from multiple datasets and improves the performance of models.'),
(99185, 2019, 'bmc', 'Mapping anatomical related entities to human body parts based on wikipedia in discharge summaries', 'Wang, Yipei|Fan, Xingyu|Chen, Luoxin|Chang, Eric I-Chao|Ananiadou, Sophia|Tsujii, Junichi|Xu, Yan', 'State Key Laboratory of Software Development Environment and Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education and Research Institute of Beihang University in Shenzhen, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Xueyuan Road No.37, Beijing, 100191, China.|Bioengineering College of Chongqing University, Shazheng Street No. 174, Chongqing, 400044, China.|State Key Laboratory of Software Development Environment and Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education and Research Institute of Beihang University in Shenzhen, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Xueyuan Road No.37, Beijing, 100191, China.|Microsoft Research, Danling Street No. 5, Beijing, 100080, China.|The National Centre for Text Mining, School of Computer Science, The University of Manchester, Manchester, UK.|The National Centre for Text Mining, School of Computer Science, The University of Manchester, Manchester, UK.|State Key Laboratory of Software Development Environment and Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education and Research Institute of Beihang University in Shenzhen, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Xueyuan Road No.37, Beijing, 100191, China. xuyan04@gmail.com.', 'China|China|China|China|United Kingdom|United Kingdom|China', 0, '-', 'Journal Article', '10.1186/s12859-019-3005-0', '31419946', '*: Background Consisting of dictated free-text documents such as discharge summaries, medical narratives are widely used in medical natural language processing. Relationships between anatomical entities and human body parts are crucial for building medical text mining applications. To achieve this, we establish a mapping system consisting of a Wikipedia-based scoring algorithm and a named entity normalization method (NEN). The mapping system makes full use of information available on Wikipedia, which is a comprehensive Internet medical knowledge base. We also built a new ontology, Tree of Human Body Parts (THBP), from core anatomical parts by referring to anatomical experts and Unified Medical Language Systems (UMLS) to make the mapping system efficacious for clinical treatments. *: Result The gold standard is derived from 50 discharge summaries from our previous work, in which 2,224 anatomical entities are included. The F1-measure of the baseline system is 70.20%, while our algorithm based on Wikipedia achieves 86.67% with the assistance of NEN. *: Conclusions We construct a framework to map anatomical entities to THBP ontology using normalization and a scoring algorithm based on Wikipedia. The proposed framework is proven to be much more effective and efficient than the main baseline system.'),
(99186, 2019, 'bmc', 'A model of pulldown alignments from SssI-treated DNA improves DNA methylation prediction', 'Moreland, Blythe S|Oman, Kenji M|Bundschuh, Ralf', 'Department of Physics, The Ohio State University, Columbus, OH, USA.|Fred Hutchinson Cancer Research Center, Seattle, WA, USA.|Department of Physics, The Ohio State University, Columbus, OH, USA. bundschuh@mps.ohio-state.edu.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1186/s12859-019-3011-2', '31426747', 'Protein pulldown using Methyl-CpG binding domain (MBD) proteins followed by high-throughput sequencing is a common method to determine DNA methylation. Algorithms have been developed to estimate absolute methylation level from read coverage generated by affinity enrichment-based techniques, but the most accurate one for MBD-seq data requires additional data from an SssI-treated Control experiment.'),
(99187, 2019, 'bmc', 'VOLARE: visual analysis of disease-associated microbiome-immune system interplay', 'Siebert, Janet C|Neff, Charles Preston|Schneider, Jennifer M|Regner, Emilie H|Ohri, Neha|Kuhn, Kristine A|Palmer, Brent E|Lozupone, Catherine A|Görg, Carsten', 'Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA. jsiebert@cytoanalytics.com.|Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA.|Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA.|Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA.|Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA.|Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA.|Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA.|Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA.|CytoAnalytics, Denver, CO, 80113, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1186/s12859-019-3021-0', '31429723', 'Relationships between specific microbes and proper immune system development, composition, and function have been reported in a number of studies. However, researchers have discovered only a fraction of the likely relationships. Omic methodologies such as 16S ribosomal RNA (rRNA) sequencing and time-of-flight mass cytometry (CyTOF) immunophenotyping generate data that support generation of hypotheses, with the potential to identify additional relationships at a level of granularity ripe for further experimentation. Pairwise linear regressions between microbial and host immune features provide one approach for quantifying relationships between omes, and the differences in these relationships across study cohorts or arms. This approach yields a top table of candidate results. However, the top table alone lacks the detail that domain experts such as microbiologists and immunologists need to vet candidate results for follow-up experiments.'),
(99188, 2019, 'bmc', 'Measuring similarity between gene interaction profiles', 'Barido-Sottani, Joëlle|Chapman, Samuel D|Kosman, Evsey|Mushegian, Arcady R', 'Stowers Institute for Medical Research, Kansas City, MO, USA.|Stowers Institute for Medical Research, Kansas City, MO, USA.|Institute for Cereal Crops Improvement, School of Plant Sciences and Food Security, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel.|Stowers Institute for Medical Research, Kansas City, MO, USA. mushegian2@gmail.com.', 'United States|United States|Israel|United States', 0, '-', 'Journal Article', '10.1186/s12859-019-3024-x', '31438841', 'Gene and protein interaction data are often represented as interaction networks, where nodes stand for genes or gene products and each edge stands for a relationship between a pair of gene nodes. Commonly, that relationship within a pair is specified by high similarity between profiles (vectors) of experimentally defined interactions of each of the two genes with all other genes in the genome, only gene pairs that interact with similar sets of genes are linked by an edge in the network. The tight groups of genes/gene products that work together in a cell can be discovered by the analysis of those complex networks.'),
(99189, 2019, 'bmc', 'An elastic-net logistic regression approach to generate classifiers and gene signatures for types of immune cells and T helper cell subsets', 'Torang, Arezo|Gupta, Paraag|Klinke, David J', 'Department of Chemical and Biomedical Engineering, West Virginia University, 1306 Evansdale Dr, Morgantown, 26506, WV, USA.|Department of Chemical and Biomedical Engineering, West Virginia University, 1306 Evansdale Dr, Morgantown, 26506, WV, USA.|Department of Chemical and Biomedical Engineering, West Virginia University, 1306 Evansdale Dr, Morgantown, 26506, WV, USA. david.klinke@mail.wvu.edu.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1186/s12859-019-2994-z', '31438843', 'Host immune response is coordinated by a variety of different specialized cell types that vary in time and location. While host immune response can be studied using conventional low-dimensional approaches, advances in transcriptomics analysis may provide a less biased view. Yet, leveraging transcriptomics data to identify immune cell subtypes presents challenges for extracting informative gene signatures hidden within a high dimensional transcriptomics space characterized by low sample numbers with noisy and missing values. To address these challenges, we explore using machine learning methods to select gene subsets and estimate gene coefficients simultaneously.'),
(99190, 2019, 'bmc', 'Prediction of regulatory targets of alternative isoforms of the epidermal growth factor receptor in a glioblastoma cell line', 'Weinholdt, Claus|Wichmann, Henri|Kotrba, Johanna|Ardell, David H|Kappler, Matthias|Eckert, Alexander W|Vordermark, Dirk|Grosse, Ivo', 'Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle, Germany. claus.weinholdt@informatik.uni-halle.de.|Department of Oral and Maxillofacial Plastic Surgery, Martin Luther University Halle-Wittenberg, Halle, Germany.|Department of Oral and Maxillofacial Plastic Surgery, Martin Luther University Halle-Wittenberg, Halle, Germany.|Molecular Cell Biology, School of Natural Sciences, University of California, Merced, USA.|Department of Oral and Maxillofacial Plastic Surgery, Martin Luther University Halle-Wittenberg, Halle, Germany.|Department of Oral and Maxillofacial Plastic Surgery, Martin Luther University Halle-Wittenberg, Halle, Germany.|Department of Radiotherapy, Martin Luther University Halle-Wittenberg, Halle, Germany.|Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle, Germany.', 'Germany|Germany|Germany|United States|Germany|Germany|Germany|Germany', 0, '-', 'Journal Article', '10.1186/s12859-019-2944-9', '31438847', 'The epidermal growth factor receptor (EGFR) is a major regulator of proliferation in tumor cells. Elevated expression levels of EGFR are associated with prognosis and clinical outcomes of patients in a variety of tumor types. There are at least four splice variants of the mRNA encoding four protein isoforms of EGFR in humans, named I through IV. EGFR isoform I is the full-length protein, whereas isoforms II-IV are shorter protein isoforms. Nevertheless, all EGFR isoforms bind the epidermal growth factor (EGF). Although EGFR is an essential target of long-established and successful tumor therapeutics, the exact function and biomarker potential of alternative EGFR isoforms II-IV are unclear, motivating more in-depth analyses. Hence, we analyzed transcriptome data from glioblastoma cell line SF767 to predict target genes regulated by EGFR isoforms II-IV, but not by EGFR isoform I nor other receptors such as HER2, HER3, or HER4.'),
(99191, 2019, 'bmc', 'Influence of batch effect correction methods on drug induced differential gene expression profiles', 'Zhou, Wei|Koudijs, Karel K M|Böhringer, Stefan', 'Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands. w.zhou@erasmusmc.nl.|Department of Clinical Pharmacy & Toxicology, Leiden University Medical Center, Leiden, The Netherlands.|Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands.', 'Netherlands|Netherlands|Netherlands', 1, '-', 'Journal Article', '10.1186/s12859-019-3028-6', '31438848', 'Batch effects were not accounted for in most of the studies of computational drug repositioning based on gene expression signatures. It is unknown how batch effect removal methods impact the results of signature-based drug repositioning. Herein, we conducted differential analyses on the Connectivity Map (CMAP) database using several batch effect correction methods to evaluate the influence of batch effect correction methods on computational drug repositioning using microarray data and compare several batch effect correction methods.'),
(99192, 2019, 'bmc', 'Shared data science infrastructure for genomics data', 'Bagheri, Hamid|Muppirala, Usha|Masonbrink, Rick E|Severin, Andrew J|Rajan, Hridesh', 'Department of Computer Science, Iowa State University, 226 Atanasoff Hall, Ames, 50011, USA. hbagheri@iastate.edu.|Genome Informatics Facility, Iowa State University, 206 Science I, Ames, 50011, USA.|Genome Informatics Facility, Iowa State University, 206 Science I, Ames, 50011, USA.|Genome Informatics Facility, Iowa State University, 206 Science I, Ames, 50011, USA.|Department of Computer Science, Iowa State University, 226 Atanasoff Hall, Ames, 50011, USA.', 'United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1186/s12859-019-2967-2', '31438850', '-'),
(99193, 2019, 'bmc', 'Target-small decoy search strategy for false discovery rate estimation', 'Kim, Hyunwoo|Lee, Sangjeong|Park, Heejin', 'Research Data Sharing Center, Korea Institute of Science and Technology Information, Daejeon, 34141, Republic of Korea.|Department of Computer Science, Hanyang University, Seoul, 06978, Republic of Korea.|Department of Computer Science, Hanyang University, Seoul, 06978, Republic of Korea. hjpark@hanyang.ac.kr.', '-|-|-', 1, '-', 'Journal Article', '10.1186/s12859-019-3034-8', '31443634', 'One of the most important steps in peptide identification is to estimate the false discovery rate (FDR). The most commonly used method for estimating FDR is the target-decoy search strategy (TDS). While this method is simple and effective, it is time/space-inefficient because it searches a database that is twice as large as the original protein database. This inefficiency problem becomes more evident as protein databases get bigger and bigger. We propose a target-small decoy search strategy and present a rigorous verification that it reduces the database size and search time while retaining the accuracy of target-decoy search strategy (TDS).');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(99194, 2019, 'bmc', 'Three-dimensional tumor growth in time-varying chemical fields: a modeling framework and theoretical study', 'Antonopoulos, Markos|Dionysiou, Dimitra|Stamatakos, Georgios|Uzunoglu, Nikolaos', 'Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece. markosan@central.ntua.gr.|Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece.|Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece.|Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece.', 'Greece|Greece|Greece|Greece', 0, '-', 'Journal Article', '10.1186/s12859-019-2997-9', '31455206', 'Contemporary biological observations have revealed a large variety of mechanisms acting during the expansion of a tumor. However, there are still many qualitative and quantitative aspects of the phenomenon that remain largely unknown. In this context, mathematical and computational modeling appears as an invaluable tool providing the means for conducting in silico experiments, which are cheaper and less tedious than real laboratory experiments.'),
(99195, 2019, 'bmc', 'Epi-GTBN: an approach of epistasis mining based on genetic Tabu algorithm and Bayesian network', 'Guo, Yang|Zhong, Zhiman|Yang, Chen|Hu, Jiangfeng|Jiang, Yaling|Liang, Zizhen|Gao, Hui|Liu, Jianxiao', '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.|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.|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. liujianxiao321@163.com.', 'China|China|China|China|China|China|China|China', 0, '-', 'Journal Article', '10.1186/s12859-019-3022-z', '31455207', 'Mining epistatic loci which affects specific phenotypic traits is an important research issue in the field of biology. Bayesian network (BN) is a graphical model which can express the relationship between genetic loci and phenotype. Until now, it has been widely used into epistasis mining in many research work. However, this method has two disadvantages: low learning efficiency and easy to fall into local optimum. Genetic algorithm has the excellence of rapid global search and avoiding falling into local optimum. It is scalable and easy to integrate with other algorithms. This work proposes an epistasis mining approach based on genetic tabu algorithm and Bayesian network (Epi-GTBN). It uses genetic algorithm into the heuristic search strategy of Bayesian network. The individual structure can be evolved through the genetic operations of selection, crossover and mutation. It can help to find the optimal network structure, and then further to mine the epistasis loci effectively. In order to enhance the diversity of the population and obtain a more effective global optimal solution, we use the tabu search strategy into the operations of crossover and mutation in genetic algorithm. It can help to accelerate the convergence of the algorithm.'),
(99196, 2019, 'bmc', 'Fine-grained alignment of cryo-electron subtomograms based on MPI parallel optimization', 'Lxc3xbc, Yongchun|Zeng, Xiangrui|Zhao, Xiaofang|Li, Shirui|Li, Hua|Gao, Xin|Xu, Min', 'University of Chinese Academy of Sciences, Beijing, China. lvyongchun@ncic.ac.cn.|Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, USA.|University of Chinese Academy of Sciences, Beijing, China.|Institute of Computing Technology of the Chinese Academy of Sciences, Beijing, China.|University of Chinese Academy of Sciences, Beijing, China.|King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal, Saudi Arabia.|Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, USA. mxu1@cs.cmu.edu.', 'China|United States|China|China|China|Saudi Arabia|United States', 1, '-', 'Journal Article', '10.1186/s12859-019-3003-2', '31455212', 'Cryo-electron tomography (Cryo-ET) is an imaging technique used to generate three-dimensional structures of cellular macromolecule complexes in their native environment. Due to developing cryo-electron microscopy technology, the image quality of three-dimensional reconstruction of cryo-electron tomography has greatly improved. However, cryo-ET images are characterized by low resolution, partial data loss and low signal-to-noise ratio (SNR). In order to tackle these challenges and improve resolution, a large number of subtomograms containing the same structure needs to be aligned and averaged. Existing methods for refining and aligning subtomograms are still highly time-consuming, requiring many computationally intensive processing steps (i.e. the rotations and translations of subtomograms in three-dimensional space).'),
(99197, 2019, 'bmc', 'Correction to: The Kendrick modelling platform: language abstractions and tools for epidemiology', 'Bui T, Mai Anh|Papoulias, Nick|Stinckwich, Serge|Ziane, Mikal|Roche, Benjamin', 'Software Engineering Department, School of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi, Vietnam. anhbtm@soict.hust.edu.vn.|Université de La Rochelle, UMR 7266 LIENSs, CNRS, La Rochelle, France.|Sorbonne Université, IRD, Unité de Modélisation Mathématiques et Informatique des Systèmes Complexes, UMMISCO, F-93143, Bondy, France.|Université de Paris, Paris, France.|Sorbonne Université, IRD, Unité de Modélisation Mathématiques et Informatique des Systèmes Complexes, UMMISCO, F-93143, Bondy, France.', '-|France|France|France|France', 0, '-', 'Published Erratum', '10.1186/s12859-019-2986-z', '31455214', 'Following publication of the original article [1], the author noticed that the following lines were missing from the published article. The original article has been corrected.'),
(99198, 2019, 'bmc', 'An equivalence approach to the integrative analysis of feature lists', 'Sánchez-Pla, Alex|Salicrú, Miquel|Ocaña, Jordi', 'Genetics, Microbiology and Statistics Department, Universitat de Barcelona, Avinguda Diagonal, 648, Barcelona, 08028, Spain. asanchez@ub.edu.|Genetics, Microbiology and Statistics Department, Universitat de Barcelona, Avinguda Diagonal, 648, Barcelona, 08028, Spain.|Genetics, Microbiology and Statistics Department, Universitat de Barcelona, Avinguda Diagonal, 648, Barcelona, 08028, Spain.', 'Spain|Spain|Spain', 0, '-', 'Journal Article', '10.1186/s12859-019-3008-x', '31455218', 'Although a few comparison methods based on the biological meaning of gene lists have been developed, the goProfiles approach is one of the few that are being used for that purpose. It consists of projecting lists of genes into predefined levels of the Gene Ontology, in such a way that a multinomial model can be used for estimation and testing. Of particular interest is the fact that it may be used for proving equivalence (in the sense of enough similarity) between two lists, instead of proving differences between them, which seems conceptually better suited to the end goal of establishing similarity among gene lists. An equivalence method has been derived that uses a distance-based approach and the confidence interval inclusion principle. Equivalence is declared if the upper limit of a one-sided confidence interval for the distance between two profiles is below a pre-established equivalence limit.'),
(99199, 2019, 'bmc', 'Reverse active learning based atrous DenseNet for pathological image classification', 'Li, Yuexiang|Xie, Xinpeng|Shen, Linlin|Liu, Shaoxiong', 'Computer Vision Institute, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China.|Computer Vision Institute, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China.|Computer Vision Institute, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China. llshen@szu.edu.cn.|The Sixth Peoples Hospital of Shenzhen, Shenzhen, China.', 'China|China|China|China', 0, '-', 'Journal Article', '10.1186/s12859-019-2979-y', '31455228', 'Due to the recent advances in deep learning, this model attracted researchers who have applied it to medical image analysis. However, pathological image analysis based on deep learning networks faces a number of challenges, such as the high resolution (gigapixel) of pathological images and the lack of annotation capabilities. To address these challenges, we propose a training strategy called deep-reverse active learning (DRAL) and atrous DenseNet (ADN) for pathological image classification. The proposed DRAL can improve the classification accuracy of widely used deep learning networks such as VGG-16 and ResNet by removing mislabeled patches in the training set. As the size of a cancer area varies widely in pathological images, the proposed ADN integrates the atrous convolutions with the dense block for multiscale feature extraction.'),
(99200, 2019, 'bmc', 'fagin: synteny-based phylostratigraphy and finer classification of young genes', 'Arendsee, Zebulun|Li, Jing|Singh, Urminder|Bhandary, Priyanka|Seetharam, Arun|Wurtele, Eve Syrkin', 'Department of Genetics Development and Cell Biology, Iowa State University, Ames, IA, 50010, USA.|Department of Genetics Development and Cell Biology, Iowa State University, Ames, IA, 50010, USA.|Department of Genetics Development and Cell Biology, Iowa State University, Ames, IA, 50010, USA.|Department of Genetics Development and Cell Biology, Iowa State University, Ames, IA, 50010, USA.|Genome Informatics Facility, Office of Biotechnology, Iowa State University, Ames, IA, 50011, USA.|Department of Genetics Development and Cell Biology, Iowa State University, Ames, IA, 50010, USA. mash@iastate.edu.', 'United States|United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1186/s12859-019-3023-y', '31455236', 'With every new genome that is sequenced, thousands of species-specific genes (orphans) are found, some originating from ultra-rapid mutations of existing genes, many others originating de novo from non-genic regions of the genome. If some of these genes survive across speciations, then extant organisms will contain a patchwork of genes whose ancestors first appeared at different times. Standard phylostratigraphy, the technique of partitioning genes by their age, is based solely on protein similarity algorithms. However, this approach relies on negative evidence xe2x94x80 a failure to detect a homolog of a query gene. An alternative approach is to limit the search for homologs to syntenic regions. Then, genes can be positively identified as de novo orphans by tracing them to non-coding sequences in related species.'),
(99201, 2019, 'bmc', 'Measuring rank robustness in scored protein interaction networks', 'Bozhilova, Lyuba V|Whitmore, Alan V|Wray, Jonny|Reinert, Gesine|Deane, Charlotte M', 'Department of Statistics, University of Oxford, 24-29 St Giles, Oxford, OX1 3LB, UK.|e-Therapeutics Plc, 17 Fenlock Rd, Long Hanborough, OX29 8LN, UK.|e-Therapeutics Plc, 17 Fenlock Rd, Long Hanborough, OX29 8LN, UK.|Department of Statistics, University of Oxford, 24-29 St Giles, Oxford, OX1 3LB, UK.|Department of Statistics, University of Oxford, 24-29 St Giles, Oxford, OX1 3LB, UK. deane@stats.ox.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1186/s12859-019-3036-6', '31462221', 'Protein interaction databases often provide confidence scores for each recorded interaction based on the available experimental evidence. Protein interaction networks (PINs) are then built by thresholding on these scores, so that only interactions of sufficiently high quality are included. These networks are used to identify biologically relevant motifs or nodes using metrics such as degree or betweenness centrality. This type of analysis can be sensitive to the choice of threshold. If a node metric is to be useful for extracting biological signal, it should induce similar node rankings across PINs obtained at different reasonable confidence score thresholds.'),
(99202, 2019, 'bmc', 'Determination of growth-coupling strategies and their underlying principles', 'Alter, Tobias B|Ebert, Birgitta E', 'Institute of Applied Microbiology, RWTH Aachen University, Aachen, Germany.|Institute of Applied Microbiology, RWTH Aachen University, Aachen, Germany. birgitta.ebert@uq.edu.au.', 'Germany|Germany', 0, '-', 'Journal Article', '10.1186/s12859-019-2946-7', '31462231', 'Metabolic coupling of product synthesis and microbial growth is a prominent approach for maximizing production performance. Growth-coupling (GC) also helps stabilizing target production and allows the selection of superior production strains by adaptive laboratory evolution. To support the implementation of growth-coupling strain designs, we seek to identify biologically relevant, metabolic principles that enforce strong growth-coupling on the basis of reaction knockouts.'),
(99203, 2019, 'bmc', 'Analyzing the regulation of miRNAs on protein-protein interaction network in Hodgkin lymphoma', 'Lei, Huimin|Liu, Wenxu|Si, Jiarui|Wang, Ju|Zhang, Tao', 'School of Biomedical Engineering, Tianjin Medical University, Tianjin, China.|School of Biomedical Engineering, Tianjin Medical University, Tianjin, China.|School of Basic Medicine, Tianjin Medical University, Tianjin, China.|School of Biomedical Engineering, Tianjin Medical University, Tianjin, China.|School of Biomedical Engineering, Tianjin Medical University, Tianjin, China. zhangtao@tijmu.edu.cn.', 'China|China|China|China|China', 0, '-', 'Journal Article', '10.1186/s12859-019-3041-9', '31477006', 'Hodgkin Lymphoma (HL) is a type of aggressive malignancy in lymphoma that has high incidence in young adults and elderly patients. Identification of reliable diagnostic markers and efficient therapeutic targets are especially important for the diagnosis and treatment of HL. Although many HL-related molecules have been identified, our understanding on the molecular mechanisms underlying the disease is still far from complete due to its complex and heterogeneous characteristics. In such situation, exploring the molecular mechanisms underlying HL via systems biology approaches provides a promising option. In this study, we try to elucidate the molecular mechanisms related to the disease and identify potential pharmaceutical targets from a network-based perspective.'),
(99204, 2019, 'bmc', 'SigsPack, a package for cancer mutational signatures', 'Schumann, Franziska|Blanc, Eric|Messerschmidt, Clemens|Blankenstein, Thomas|Busse, Antonia|Beule, Dieter', 'Core Unit Bioinformatics, Berlin Institute of Health, Charitéplatz 1, Berlin, 10117, Germany.|Core Unit Bioinformatics, Berlin Institute of Health, Charitéplatz 1, Berlin, 10117, Germany.|Core Unit Bioinformatics, Berlin Institute of Health, Charitéplatz 1, Berlin, 10117, Germany.|Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Robert-Rössle-Str. 10, Berlin, 13092, Germany.|Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, Berlin, 10117, Germany.|Core Unit Bioinformatics, Berlin Institute of Health, Charitéplatz 1, Berlin, 10117, Germany. dieter.beule@bihealth.de.', 'Germany|Germany|Germany|Germany|Germany|Germany', 1, '-', 'Journal Article', '10.1186/s12859-019-3043-7', '31477009', 'Mutational signatures are specific patterns of somatic mutations introduced into the genome by oncogenic processes. Several mutational signatures have been identified and quantified from multiple cancer studies, and some of them have been linked to known oncogenic processes. Identification of the processes contributing to mutations observed in a sample is potentially informative to understand the cancer etiology.'),
(99205, 2019, 'bmc', 'Cytokit: a single-cell analysis toolkit for high dimensional fluorescent microscopy imaging', 'Czech, Eric|Aksoy, Bulent Arman|Aksoy, Pinar|Hammerbacher, Jeff', 'Microbiology and Immunology Department at Medical University of South Carolina, Charleston, USA. eric@hammerlab.org.|Microbiology and Immunology Department at Medical University of South Carolina, Charleston, USA.|Microbiology and Immunology Department at Medical University of South Carolina, Charleston, USA.|Microbiology and Immunology Department at Medical University of South Carolina, Charleston, USA.', 'United States|United States|United States|United States', 3, '-', 'Journal Article', '10.1186/s12859-019-3055-3', '31477013', 'Multiplexed in-situ fluorescent imaging offers several advantages over single-cell assays that do not preserve the spatial characteristics of biological samples. This spatial information, in addition to morphological properties and extensive intracellular or surface marker profiling, comprise promising avenues for rapid advancements in the understanding of disease progression and diagnosis. As protocols for conducting such imaging experiments continue to improve, it is the intent of this study to provide and validate software for processing the large quantity of associated data in kind.'),
(99206, 2019, 'bmc', 'NPA: an R package for computing network perturbation amplitudes using gene expression data and two-layer networks', 'Martin, Florian|Gubian, Sylvain|Talikka, Marja|Hoeng, Julia|Peitsch, Manuel C', 'PMI R&D, Philip Morris Products S.A, Quai Jeanrenaud 5, CH-2000, Neuchâtel, Switzerland. Florian.Martin@pmi.com.|PMI R&D, Philip Morris Products S.A, Quai Jeanrenaud 5, CH-2000, Neuchâtel, Switzerland.|PMI R&D, Philip Morris Products S.A, Quai Jeanrenaud 5, CH-2000, Neuchâtel, Switzerland.|PMI R&D, Philip Morris Products S.A, Quai Jeanrenaud 5, CH-2000, Neuchâtel, Switzerland.|PMI R&D, Philip Morris Products S.A, Quai Jeanrenaud 5, CH-2000, Neuchâtel, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 0, '-', 'Journal Article', '10.1186/s12859-019-3016-x', '31481014', 'High-throughput gene expression technologies provide complex datasets reflecting mechanisms perturbed in an experiment, typically in a treatment versus control design. Analysis of these information-rich data can be guided based on a priori knowledge, such as networks of related proteins or genes. Assessing the response of a specific mechanism and investigating its biological basis is extremely important in systems toxicology, as compounds or treatment need to be assessed with respect to a predefined set of key mechanisms that could lead to toxicity. Two-layer networks are suitable for this task, and a robust computational methodology specifically addressing those needs was previously published. The NPA package ( https://github.com/philipmorrisintl/NPA ) implements the algorithm, and a data package of eight two-layer networks representing key mechanisms, such as xenobiotic metabolism, apoptosis, or epithelial immune innate activation, is provided.'),
(99207, 2019, 'bmc', 'mycelyso - high-throughput analysis of Streptomyces mycelium live cell imaging data', 'Sachs, Christian Carsten|Koepff, Joachim|Wiechert, Wolfgang|Grünberger, Alexander|Nöh, Katharina', 'Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Str., Jülich, 52425, Germany.|Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Str., Jülich, 52425, Germany.|Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Str., Jülich, 52425, Germany.|Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Str., Jülich, 52425, Germany.|Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Str., Jülich, 52425, Germany. k.noeh@fz-juelich.de.', 'Germany|Germany|Germany|Germany|Germany', 0, '-', 'Journal Article', '10.1186/s12859-019-3004-1', '31484491', 'Streptomycetes are filamentous microorganisms of high biotechnological relevance, especially for the production of antibiotics. In submerged cultures, the productivity of these microorganisms is closely linked to their growth morphology. Microfluidic lab-on-a-chip cultivation systems, coupled with automated time-lapse imaging, generate spatio-temporal insights into the mycelium development of streptomycetes, therewith extending the biotechnological toolset by spatio-temporal screening under well-controlled and reproducible conditions. However, the analysis of the complex mycelial structure formation is limited by the extent of manual interventions required during processing of the acquired high-volume image data. These interventions typically lead to high evaluation times and, therewith, limit the analytic throughput and exploitation of microfluidic-based screenings.'),
(99208, 2019, 'bmc', 'SamPler - a novel method for selecting parameters for gene functional annotation routines', 'Cruz, Fernando|Lagoa, Davide|Mendes, João|Rocha, Isabel|Ferreira, Eugénio C|Rocha, Miguel|Dias, Oscar', 'Centre of Biological Engineering, University of Minho, 4710-057, Braga, Portugal.|Centre of Biological Engineering, University of Minho, 4710-057, Braga, Portugal.|Centre of Biological Engineering, University of Minho, 4710-057, Braga, Portugal.|Centre of Biological Engineering, University of Minho, 4710-057, Braga, Portugal.|Centre of Biological Engineering, University of Minho, 4710-057, Braga, Portugal.|Centre of Biological Engineering, University of Minho, 4710-057, Braga, Portugal.|Centre of Biological Engineering, University of Minho, 4710-057, Braga, Portugal. odias@ceb.uminho.pt.', 'Portugal|Portugal|Portugal|Portugal|Portugal|Portugal|Portugal', 1, '-', 'Journal Article', '10.1186/s12859-019-3038-4', '31488049', 'As genome sequencing projects grow rapidly, the diversity of organisms with recently assembled genome sequences peaks at an unprecedented scale, thereby highlighting the need to make gene functional annotations fast and efficient. However, the (high) quality of such annotations must be guaranteed, as this is the first indicator of the genomic potential of every organism. Automatic procedures help accelerating the annotation process, though decreasing the confidence and reliability of the outcomes. Manually curating a genome-wide annotation of genes, enzymes and transporter proteins function is a highly time-consuming, tedious and impractical task, even for the most proficient curator. Hence, a semi-automated procedure, which balances the two approaches, will increase the reliability of the annotation, while speeding up the process. In fact, a prior analysis of the annotation algorithm may leverage its performance, by manipulating its parameters, hastening the downstream processing and the manual curation of assigning functions to genes encoding proteins.'),
(99209, 2019, 'bmc', 'Fast and accurate average genome size and 16S rRNA gene average copy number computation in metagenomic data', 'Pereira-Flores, Emiliano|Glöckner, Frank Oliver|Fernandez-Guerra, Antonio', 'Microbial Genomics and Bioinformatics Research Group, Max Planck Institute for Marine Microbiology, Celsiusstraße 1, 28359, Bremen, Germany. epereira@mpi-bremen.de.|Microbial Genomics and Bioinformatics Research Group, Max Planck Institute for Marine Microbiology, Celsiusstraße 1, 28359, Bremen, Germany.|Microbial Genomics and Bioinformatics Research Group, Max Planck Institute for Marine Microbiology, Celsiusstraße 1, 28359, Bremen, Germany. afernand@mpi-bremen.de.', 'Germany|Germany|Germany', 0, '-', 'Journal Article', '10.1186/s12859-019-3031-y', '31488068', 'Metagenomics caused a quantum leap in microbial ecology. However, the inherent size and complexity of metagenomic data limit its interpretation. The quantification of metagenomic traits in metagenomic analysis workflows has the potential to improve the exploitation of metagenomic data. Metagenomic traits are organisms characteristics linked to their performance. They are measured at the genomic level taking a random sample of individuals in a community. As such, these traits provide valuable information to uncover microorganisms ecological patterns. The Average Genome Size (AGS) and the 16S rRNA gene Average Copy Number (ACN) are two highly informative metagenomic traits that reflect microorganisms ecological strategies as well as the environmental conditions they inhabit.'),
(99210, 2019, 'bmc', 'PTPD: predicting therapeutic peptides by deep learning and word2vec', 'Wu, Chuanyan|Gao, Rui|Zhang, Yusen|De Marinis, Yang', 'School of Control Science and Engineering, Shandong University, Jingshi Road, Jinan, 250061, China.|School of Control Science and Engineering, Shandong University, Jingshi Road, Jinan, 250061, China. gaorui@sdu.edu.cn.|School of Mathematics and Statistics, Shandong University at Weihai, Weihai, 264209, China.|Diabetes and Endocrinology, Lund University, Malmo, 20502, Sweden.', 'China|China|China|Sweden', 5, '-', 'Journal Article', '10.1186/s12859-019-3006-z', '31492094', '*: Background In the search for therapeutic peptides for disease treatments, many efforts have been made to identify various functional peptides from large numbers of peptide sequence databases. In this paper, we propose an effective computational model that uses deep learning and word2vec to predict therapeutic peptides (PTPD). *: Results Representation vectors of all k-mers were obtained through word2vec based on k-mer co-existence information. The original peptide sequences were then divided into k-mers using the windowing method. The peptide sequences were mapped to the input layer by the embedding vector obtained by word2vec. Three types of filters in the convolutional layers, as well as dropout and max-pooling operations, were applied to construct feature maps. These feature maps were concatenated into a fully connected dense layer, and rectified linear units (ReLU) and dropout operations were included to avoid over-fitting of PTPD. The classification probabilities were generated by a sigmoid function. PTPD was then validated using two datasets: an independent anticancer peptide dataset and a virulent protein dataset, on which it achieved accuracies of 96% and 94%, respectively. *: Conclusions PTPD identified novel therapeutic peptides efficiently, and it is suitable for application as a useful tool in therapeutic peptide design.'),
(99211, 2019, 'bmc', 'Sibe: a computation tool to apply protein sequence statistics to predict folding and design in silico', 'Cheung, Ngaam J|Yu, Wookyung', 'Department of Brain and Cognitive Science, DGIST, Daegu, 42988, South Korea.|Department of Brain and Cognitive Science, DGIST, Daegu, 42988, South Korea. wkyu@dgist.ac.kr.', '-|-', 0, '-', 'Journal Article', '10.1186/s12859-019-2984-1', '31492097', 'Evolutionary information contained in the amino acid sequences of proteins specifies the biological function and fold, but exactly what information contained in the protein sequence drives both of these processes? Considerable progress has been made to answer this fundamental question, but it remains challenging to explore the potential space of cooperative interactions between amino acids. Statistical analysis plays a significant role in studying such interactions and its use has expanded in recent years to studies ranging from coevolution-guided rational protein design to protein folding in silico.'),
(99212, 2019, 'bmc', 'Model annotation and discovery with the Physiome Model Repository', 'Sarwar, Dewan M|Kalbasi, Reza|Gennari, John H|Carlson, Brian E|Neal, Maxwell L|Bono, Bernard de|Atalag, Koray|Hunter, Peter J|Nickerson, David P', 'Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.|Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.|Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, USA.|Molecular & Integrative Physiology, University of Michigan, Ann Arbor, Michigan, USA.|Center for Global Infectious Disease Research, Seattle Childrens Research Institute, Seattle, Washington, USA.|Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.|Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.|Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.|Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand. d.nickerson@auckland.ac.nz.', 'New Zealand|New Zealand|United States|United States|United States|New Zealand|New Zealand|New Zealand|New Zealand', 0, '-', 'Journal Article', '10.1186/s12859-019-2987-y', '31492098', 'Mathematics and Phy sics-based simulation models have the potential to help interpret and encapsulate biological phenomena in a computable and reproducible form. Similarly, comprehensive descriptions of such models help to ensure that such models are accessible, discoverable, and reusable. To this end, researchers have developed tools and standards to encode mathematical models of biological systems enabling reproducibility and reuse, tools and guidelines to facilitate semantic description of mathematical models, and repositories in which to archive, share, and discover models. Scientists can leverage these resources to investigate specific questions and hypotheses in a more efficient manner.'),
(99213, 2019, 'bmc', 'A novel evolutionary model for constructing gene coexpression networks with comprehensive features', 'Gu, Yuexi|Zu, Jian|Li, Yu', 'School of Mathematics and Statistics, Xian Jiaotong University, Xian, 710049, Peoples Republic of China.|School of Mathematics and Statistics, Xian Jiaotong University, Xian, 710049, Peoples Republic of China. jianzu@xjtu.edu.cn.|School of Mathematics and Statistics, Xian Jiaotong University, Xian, 710049, Peoples Republic of China.', 'China|China|China', 0, '-', 'Journal Article', '10.1186/s12859-019-3035-7', '31492104', 'Uncovering the evolutionary principles of gene coexpression network is important for our understanding of the network topological property of new genes. However, most existing evolutionary models only considered the evolution of duplication genes and only based on the degree of genes, ignoring the other key topological properties. The evolutionary mechanism by which how are new genes integrated into the ancestral networks are not yet to be comprehensively characterized. Herein, based on the human ribonucleic acid-sequencing (RNA-seq) data, we develop a new evolutionary model of gene coexpression network which considers the evolutionary process of both duplication genes and de novo genes.'),
(99214, 2019, 'bmc', 'Visualization methods for differential expression analysis', 'Rutter, Lindsay|Moran Lauter, Adrienne N|Graham, Michelle A|Cook, Dianne', 'Bioinformatics and Computational Biology Program, Iowa State University, Ames, USA. lindsayannerutter@gmail.com.|USDA-ARS, Corn Insects and Crop Genetics Research Unit, Ames, USA.|USDA-ARS, Corn Insects and Crop Genetics Research Unit, Ames, USA.|Econometrics and Business Statistics, Monash University, Clayton VIC, Australia.', 'United States|United States|United States|Australia', 4, '-', 'Journal Article', '10.1186/s12859-019-2968-1', '31492109', 'Despite the availability of many ready-made testing software, reliable detection of differentially expressed genes in RNA-seq data is not a trivial task. Even though the data collection is considered high-throughput, data analysis has intricacies that require careful human attention. Researchers should use modern data analysis techniques that incorporate visual feedback to verify the appropriateness of their models. While some RNA-seq packages provide static visualization tools, their capabilities should be expanded and their meaningfulness should be explicitly demonstrated to users.'),
(99215, 2019, 'bmc', 'Multiple-level biomedical event trigger recognition with transfer learning', 'Chen, Yifei', 'School of Information Engineering, Nanjing Audit University, 86 West Yushan Road, Nanjing, China. yifeichen91@nau.edu.cn.', 'China', 1, '-', 'Journal Article', '10.1186/s12859-019-3030-z', '31492112', 'Automatic extraction of biomedical events from literature is an important task in the understanding biological systems, allowing for faster update of the latest discoveries automatically. Detecting trigger words which indicate events is a critical step in the process of event extraction, because following steps depend on the recognized triggers. The task in this study is to identify event triggers from the literature across multiple levels of biological organization. In order to achieve high performances, the machine learning based approaches, such as neural networks, must be trained on a dataset with plentiful annotations. However, annotations might be difficult to obtain on the multiple levels, and annotated resources have so far mainly focused on the relations and processes at the molecular level. In this work, we aim to apply transfer learning for multiple-level trigger recognition, in which a source dataset with sufficient annotations on the molecular level is utilized to improve performance on a target domain with insufficient annotations and more trigger types.'),
(99216, 2019, 'bmc', 'BINDER: computationally inferring a gene regulatory network for Mycobacterium abscessus', 'Staunton, Patrick M|Miranda-CasoLuengo, Aleksandra A|Loftus, Brendan J|Gormley, Isobel Claire', 'School of Medicine, Conway Institute, University College Dublin, Dublin, Ireland.|Moyne Institute of Preventive Medicine, Department of Microbiology, Trnity College Dublin, Dublin, Ireland.|School of Medicine, Conway Institute, University College Dublin, Dublin, Ireland.|School of Mathematics and Statistics, Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland. claire.gormley@ucd.ie.', 'Ireland|Ireland|Ireland|Ireland', 0, '-', 'Journal Article', '10.1186/s12859-019-3042-8', '31500560', 'Although many of the genic features in Mycobacterium abscessus have been fully validated, a comprehensive understanding of the regulatory elements remains lacking. Moreover, there is little understanding of how the organism regulates its transcriptomic profile, enabling cells to survive in hostile environments. Here, to computationally infer the gene regulatory network for Mycobacterium abscessus we propose a novel statistical computational modelling approach: BayesIan gene regulatory Networks inferreD via gene coExpression and compaRative genomics (BINDER). In tandem with derived experimental coexpression data, the property of genomic conservation is exploited to probabilistically infer a gene regulatory network in Mycobacterium abscessus.Inference on regulatory interactions is conducted by combining primary and auxiliary data strata. The data forming the primary and auxiliary strata are derived from RNA-seq experiments and sequence information in the primary organism Mycobacterium abscessus as well as ChIP-seq data extracted from a related proxy organism Mycobacterium tuberculosis. The primary and auxiliary data are combined in a hierarchical Bayesian framework, informing the apposite bivariate likelihood function and prior distributions respectively. The inferred relationships provide insight to regulon groupings in Mycobacterium abscessus.'),
(99217, 2019, 'bmc', 'Proper evaluation of chemical cross-linking-based spatial restraints improves the precision of modeling homo-oligomeric protein complexes', 'Gaber, Aljaxc5xbe|Gunxc4x8dar, Gregor|Pavxc5xa1ixc4x8d, Miha', 'Department of Chemistry and Biochemistry, Faculty of Chemistry and Chemical Technology, University of Ljubljana, Vexc4x8dna pot 113, 1000, Ljubljana, SI, Slovenia.|Department of Chemistry and Biochemistry, Faculty of Chemistry and Chemical Technology, University of Ljubljana, Vexc4x8dna pot 113, 1000, Ljubljana, SI, Slovenia.|Department of Chemistry and Biochemistry, Faculty of Chemistry and Chemical Technology, University of Ljubljana, Vexc4x8dna pot 113, 1000, Ljubljana, SI, Slovenia. miha.pavsic@fkkt.uni-lj.si.', 'Slovenia|Slovenia|Slovenia', 1, '-', 'Journal Article', '10.1186/s12859-019-3032-x', '31500562', 'The function of oligomeric proteins is inherently linked to their quaternary structure. In the absence of high-resolution data, low-resolution information in the form of spatial restraints can significantly contribute to the precision and accuracy of structural models obtained using computational approaches. To obtain such restraints, chemical cross-linking coupled with mass spectrometry (XL-MS) is commonly used. However, the use of XL-MS in the modeling of protein complexes comprised of identical subunits (homo-oligomers) is often hindered by the inherent ambiguity of intra- and inter-subunit connection assignment.'),
(99218, 2019, 'bmc', 'nanite: using machine learning to assess the quality of atomic force microscopy-enabled nano-indentation data', 'Mxc3xbcller, Paul|Abuhattum, Shada|Mxc3xb6llmert, Stephanie|Ulbricht, Elke|Taubenberger, Anna V|Guck, Jochen', 'Biotechnology Center, Center for Molecular and Cellular Bioengineering, Technische Universitxc3xa4t Dresden, Tatzberg 47/49, Dresden, 01307, Germany. paul_mueller@tu-dresden.de.|Biotechnology Center, Center for Molecular and Cellular Bioengineering, Technische Universitxc3xa4t Dresden, Tatzberg 47/49, Dresden, 01307, Germany.|Biotechnology Center, Center for Molecular and Cellular Bioengineering, Technische Universitxc3xa4t Dresden, Tatzberg 47/49, Dresden, 01307, Germany.|Biotechnology Center, Center for Molecular and Cellular Bioengineering, Technische Universitxc3xa4t Dresden, Tatzberg 47/49, Dresden, 01307, Germany.|Biotechnology Center, Center for Molecular and Cellular Bioengineering, Technische Universitxc3xa4t Dresden, Tatzberg 47/49, Dresden, 01307, Germany.|Biotechnology Center, Center for Molecular and Cellular Bioengineering, Technische Universitxc3xa4t Dresden, Tatzberg 47/49, Dresden, 01307, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 1, '-', 'Journal Article', '10.1186/s12859-019-3010-3', '31500563', 'Atomic force microscopy (AFM) allows the mechanical characterization of single cells and live tissue by quantifying force-distance (FD) data in nano-indentation experiments. One of the main problems when dealing with biological tissue is the fact that the measured FD curves can be disturbed. These disturbances are caused, for instance, by passive cell movement, adhesive forces between the AFM probe and the cell, or insufficient attachment of the tissue to the supporting cover slide. In practice, the resulting artifacts are easily spotted by an experimenter who then manually sorts out curves before proceeding with data evaluation. However, this manual sorting step becomes increasingly cumbersome for studies that involve numerous measurements or for quantitative imaging based on FD maps.'),
(99219, 2019, 'bmc', 'Predicting links between tumor samples and genes using 2-Layered graph based diffusion approach', 'Timilsina, Mohan|Yang, Haixuan|Sahay, Ratnesh|Rebholz-Schuhmann, Dietrich', 'Insight Centre for Data Analytics, National University of Ireland Galway, Galway, Ireland. mohan.timilsina@insight-centre.org.|School of Mathematics Statistics and Applied Mathematics, National University of Ireland Galway, Galway, Ireland.|Insight Centre for Data Analytics, National University of Ireland Galway, Galway, Ireland.|Insight Centre for Data Analytics, National University of Ireland Galway, Galway, Ireland.', 'Ireland|Ireland|Ireland|Ireland', 1, '-', 'Journal Article', '10.1186/s12859-019-3056-2', '31500564', 'Determining the association between tumor sample and the gene is demanding because it requires a high cost for conducting genetic experiments. Thus, the discovered association between tumor sample and gene further requires clinical verification and validation. This entire mechanism is time-consuming and expensive. Due to this issue, predicting the association between tumor samples and genes remain a challenge in biomedicine.'),
(99220, 2019, 'bmc', 'HPAanalyze: an R package that facilitates the retrieval and analysis of the Human Protein Atlas data', 'Tran, Anh Nhat|Dussaq, Alex M|Kennell, Timothy|Willey, Christopher D|Hjelmeland, Anita B', 'Department of Cell, Developmental and Integrative Biology, University of Alabama at Birmingham, THT 948, 1900 University Blvd, Birmingham, AL, 35294, USA. trannhatanh89@gmail.com.|Department of Pathology, University of Alabama at Birmingham, 121 Shelby Biomedical Research Building, Birmingham, AL, 35294, USA.|Department of Genetics, University of Alabama at Birmingham, 121 Shelby Biomedical Research Building, Birmingham, AL, 35294, USA.|Department of Radiation Oncology, University of Alabama at Birmingham, 176 Facility Building, Birmingham, AL, 35294, USA.|Department of Cell, Developmental and Integrative Biology, University of Alabama at Birmingham, THT 948, 1900 University Blvd, Birmingham, AL, 35294, USA. hjelmea@uab.edu.', 'United States|United States|United States|United States|United States', 2, '-', 'Journal Article', '10.1186/s12859-019-3059-z', '31500569', 'The Human Protein Atlas (HPA) aims to map human proteins via multiple technologies including imaging, proteomics and transcriptomics. Access of the HPA data is mainly via web-based interface allowing views of individual proteins, which may not be optimal for data analysis of a gene set, or automatic retrieval of original images.'),
(99221, 2019, 'bmc', 'RetSynth: determining all optimal and sub-optimal synthetic pathways that facilitate synthesis of target compounds in chassis organisms', 'Whitmore, Leanne S|Nguyen, Bernard|Pinar, Ali|George, Anthe|Hudson, Corey M', 'Sandia National Laboratories, East Avenue, Livermore, 94550, USA.|Sandia National Laboratories, East Avenue, Livermore, 94550, USA.|Sandia National Laboratories, East Avenue, Livermore, 94550, USA.|Sandia National Laboratories, East Avenue, Livermore, 94550, USA.|Sandia National Laboratories, East Avenue, Livermore, 94550, USA. cmhudso@sandia.gov.', 'United States|United States|United States|United States|United States', 0, '-', 'Journal Article', '10.1186/s12859-019-3025-9', '31500573', 'The efficient biological production of industrially and economically important compounds is a challenging problem. Brute-force determination of the optimal pathways to efficient production of a target chemical in a chassis organism is computationally intractable. Many current methods provide a single solution to this problem, but fail to provide all optimal pathways, optional sub-optimal solutions or hybrid biological/non-biological solutions.'),
(99222, 2019, 'bmc', 'A network embedding-based multiple information integration method for the MiRNA-disease association prediction', 'Gong, Yuchong|Niu, Yanqing|Zhang, Wen|Li, Xiaohong', 'School of Computer Science, Wuhan University, Wuhan, 430072, China.|School of Mathematics and Statistics, South-Central University for Nationalities, Wuhan, 430074, China.|College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China. zhangwen@mail.hzau.edu.cn.|School of Computer Science, Wuhan University, Wuhan, 430072, China. leexh@whu.edu.cn.', 'China|China|China|China', 14, '-', 'Journal Article', '10.1186/s12859-019-3063-3', '31510919', 'MiRNAs play significant roles in many fundamental and important biological processes, and predicting potential miRNA-disease associations makes contributions to understanding the molecular mechanism of human diseases. Existing state-of-the-art methods make use of miRNA-target associations, miRNA-family associations, miRNA functional similarity, disease semantic similarity and known miRNA-disease associations, but the known miRNA-disease associations are not well exploited.'),
(99223, 2019, 'bmc', 'CytoConverter: a web-based tool to convert karyotypes to genomic coordinates', 'Wang, Janet|LaFramboise, Thomas', 'Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, OH, 44106, USA.|Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, OH, 44106, USA. Thomas.LaFramboise@case.edu.', 'United States|United States', 1, '-', 'Journal Article', '10.1186/s12859-019-3062-4', '31510921', 'Cytogenetic nomenclature is used to describe chromosomal aberrations (or lack thereof) in a collection of cells, referred to as the cells karyotype. The nomenclature identifies locations on chromosomes using a system of cytogenetic bands, each with a unique name and region on a chromosome. Each band is microscopically visible after staining, and encompasses a large portion of the chromosome. More modern analyses employ genomic coordinates, which precisely specify a chromosomal location according to its distance from the end of the chromosome. Currently, there is no tool to convert cytogenetic nomenclature into genomic coordinates. Since locations of genes and other genomic features are usually specified by genomic coordinates, a conversion tool will facilitate the identification of the features that are harbored in the regions of chromosomal gain and loss that are implied by a karyotype.'),
(99224, 2019, 'bmc', 'A classification model for lncRNA and mRNA based on k-mers and a convolutional neural network', 'Wen, Jianghui|Liu, Yeshu|Shi, Yu|Huang, Haoran|Deng, Bing|Xiao, Xinping', 'School of Science, Wuhan University of Technology, Wuhan, 430070, Peoples Republic of China.|School of Science, Wuhan University of Technology, Wuhan, 430070, Peoples Republic of China.|School of Science, Wuhan University of Technology, Wuhan, 430070, Peoples Republic of China.|School of Science, Wuhan University of Technology, Wuhan, 430070, Peoples Republic of China.|Wuhan Academy of Agricultural Sciences, Wuhan, 430208, Peoples Republic of China. dengbing0906@163.com.|School of Science, Wuhan University of Technology, Wuhan, 430070, Peoples Republic of China. xiaoxp@whut.edu.cn.', 'China|China|China|China|China|China', 0, '-', 'Journal Article', '10.1186/s12859-019-3039-3', '31519146', 'Long-chain non-coding RNA (lncRNA) is closely related to many biological activities. Since its sequence structure is similar to that of messenger RNA (mRNA), it is difficult to distinguish between the two based only on sequence biometrics. Therefore, it is particularly important to construct a model that can effectively identify lncRNA and mRNA.'),
(99225, 2019, 'bmc', 'Towards pixel-to-pixel deep nucleus detection in microscopy images', 'Xing, Fuyong|Xie, Yuanpu|Shi, Xiaoshuang|Chen, Pingjun|Zhang, Zizhao|Yang, Lin', 'Department of Biostatistics and Informatics, and the Data Science to Patient Value initiative, University of Colorado Anschutz Medical Campus, 13001 E 17th Pl, Aurora, Colorado 80045, United States. fuyong.xing@ucdenver.edu.|J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, 1275 Center Drive, Gainesville, Florida 32611, United States.|J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, 1275 Center Drive, Gainesville, Florida 32611, United States.|J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, 1275 Center Drive, Gainesville, Florida 32611, United States.|Department of Computer and Information Science and Engineering, University of Florida, 432 Newell Drive, Gainesville, Florida 32611, United States.|J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, 1275 Center Drive, Gainesville, Florida 32611, United States.', 'United States|United States|United States|United States|United States|United States', 3, '-', 'Journal Article', '10.1186/s12859-019-3037-5', '31521104', 'Nucleus is a fundamental task in microscopy image analysis and supports many other quantitative studies such as object counting, segmentation, tracking, etc. Deep neural networks are emerging as a powerful tool for biomedical image computing, in particular, convolutional neural networks have been widely applied to nucleus/cell detection in microscopy images. However, almost all models are tailored for specific datasets and their applicability to other microscopy image data remains unknown. Some existing studies casually learn and evaluate deep neural networks on multiple microscopy datasets, but there are still several critical, open questions to be addressed.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(99226, 2019, 'bmc', 'A purely bioinformatic pipeline for the prediction of mammalian odorant receptor gene enhancers', 'DeglInnocenti, Andrea|Meloni, Gabriella|Mazzolai, Barbara|Ciofani, Gianni', 'Smart Bio-Interfaces, Istituto Italiano di Tecnologia, Viale Rinaldo Piaggio 34, 56025, Pontedera (Pisa), Italy. andrea.deglinnocenti@iit.it.|Center for Micro-BioRobotics, Istituto Italiano di Tecnologia, Viale Rinaldo Piaggio 34, 56025, Pontedera (Pisa), Italy.|Center for Micro-BioRobotics, Istituto Italiano di Tecnologia, Viale Rinaldo Piaggio 34, 56025, Pontedera (Pisa), Italy.|Smart Bio-Interfaces, Istituto Italiano di Tecnologia, Viale Rinaldo Piaggio 34, 56025, Pontedera (Pisa), Italy. gianni.ciofani@iit.it.', 'Italy|Italy|Italy|Italy', 0, '-', 'Journal Article', '10.1186/s12859-019-3012-1', '31521109', 'In most mammals, a vast array of genes coding for chemosensory receptors mediates olfaction. Odorant receptor (OR) genes generally constitute the largest multifamily (>xe2x80x891100 intact members in the mouse). From the whole pool, each olfactory neuron expresses a single OR allele following poorly characterized mechanisms termed OR gene choice. OR genes are found in genomic aggregations known as clusters. Nearby enhancers, named elements, are crucial regulators of OR gene choice. Despite their importance, searching for new elements is burdensome. Other chemosensory receptor genes responsible for smell adhere to expression modalities resembling OR gene choice, and are arranged in genomic clusters - often with chromosomal linkage to OR genes. Still, no elements are known for them.'),
(99227, 2019, 'bmc', 'HH-suite3 for fast remote homology detection and deep protein annotation', 'Steinegger, Martin|Meier, Markus|Mirdita, Milot|Vxc3xb6hringer, Harald|Haunsberger, Stephan J|Sxc3xb6ding, Johannes', 'Quantitative and Computational Biology Group, Max-Planck Institute for Biophysical Chemistry, Am Fassberg 11, Munich, 81379, Germany.|Quantitative and Computational Biology Group, Max-Planck Institute for Biophysical Chemistry, Am Fassberg 11, Munich, 81379, Germany.|Quantitative and Computational Biology Group, Max-Planck Institute for Biophysical Chemistry, Am Fassberg 11, Munich, 81379, Germany.|Quantitative and Computational Biology Group, Max-Planck Institute for Biophysical Chemistry, Am Fassberg 11, Munich, 81379, Germany.|Royal College of Surgeons, Dublin, D02 YN77, Ireland.|Quantitative and Computational Biology Group, Max-Planck Institute for Biophysical Chemistry, Am Fassberg 11, Munich, 81379, Germany. soeding@mpibpc.mpg.de.', 'Germany|Germany|Germany|Germany|Ireland|Germany', 25, '-', 'Journal Article', '10.1186/s12859-019-3019-7', '31521110', 'HH-suite is a widely used open source software suite for sensitive sequence similarity searches and protein fold recognition. It is based on pairwise alignment of profile Hidden Markov models (HMMs), which represent multiple sequence alignments of homologous proteins.'),
(99228, 2019, 'bmc', 'A multiscale mathematical model of cell dynamics during neurogenesis in the mouse cerebral cortex', 'Postel, Marie|Karam, Alice|Pézeron, Guillaume|Schneider-Maunoury, Sylvie|Clément, Frédérique', 'Sorbonne Université, Université Paris-Diderot SPC, CNRS, Laboratoire Jacques-Louis Lions, LJLL, Paris, France. marie.postel@sorbonne-universite.fr.|Sorbonne Université, CNRS UMR7622, Inserm U1156, Institut de Biologie Paris-Seine (IBPS), Laboratoire de Biologie du développement (LBD), Paris, France.|Sorbonne Université, CNRS UMR7622, Inserm U1156, Institut de Biologie Paris-Seine (IBPS), Laboratoire de Biologie du développement (LBD), Paris, France.|Sorbonne Université, CNRS UMR7622, Inserm U1156, Institut de Biologie Paris-Seine (IBPS), Laboratoire de Biologie du développement (LBD), Paris, France.|Inria, Université Paris-Saclay, Palaiseau, France.', 'France|France|France|France|France', 1, '-', 'Journal Article', '10.1186/s12859-019-3018-8', '31521111', 'Neurogenesis in the murine cerebral cortex involves the coordinated divisions of two main types of progenitor cells, whose numbers, division modes and cell cycle durations set up the final neuronal output. To understand the respective roles of these factors in the neurogenesis process, we combine experimental in vivo studies with mathematical modeling and numerical simulations of the dynamics of neural progenitor cells. A special focus is put on the population of intermediate progenitors (IPs), a transit amplifying progenitor type critically involved in the size of the final neuron pool.'),
(99229, 2019, 'bmc', 'Identifying protein complexes based on an edge weight algorithm and core-attachment structure', 'Wang, Rongquan|Liu, Guixia|Wang, Caixia', 'College of Computer Science and Technology, Jilin University, No. 2699 Qianjin Street, Changchun, 130012, China.|College of Computer Science and Technology, Jilin University, No. 2699 Qianjin Street, Changchun, 130012, China. liugx@jlu.edu.cn.|School of International Economics, China Foreign Affairs University, 24 Zhanlanguan Road, Xicheng District, Beijing, 100037, China.', 'China|China|China', 2, '-', 'Journal Article', '10.1186/s12859-019-3007-y', '31521132', 'Protein complex identification from protein-protein interaction (PPI) networks is crucial for understanding cellular organization principles and functional mechanisms. In recent decades, numerous computational methods have been proposed to identify protein complexes. However, most of the current state-of-the-art studies still have some challenges to resolve, including their high false-positives rates, incapability of identifying overlapping complexes, lack of consideration for the inherent organization within protein complexes, and absence of some biological attachment proteins.'),
(99230, 2019, 'bmc', 'A novel protein descriptor for the prediction of drug binding sites', 'Jiang, Mingjian|Li, Zhen|Bian, Yujie|Wei, Zhiqiang', 'Department of Computer Science and Technology, Ocean University of China, 238 Songling Road, Qingdao, 266100, China.|Department of Computer Science and Technology, Ocean University of China, 238 Songling Road, Qingdao, 266100, China.|Department of Computer Science and Technology, Ocean University of China, 238 Songling Road, Qingdao, 266100, China.|Department of Computer Science and Technology, Ocean University of China, 238 Songling Road, Qingdao, 266100, China. weizhiqiangouc@163.com.', 'China|China|China|China', 1, '-', 'Journal Article', '10.1186/s12859-019-3058-0', '31533611', 'Binding sites are the pockets of proteins that can bind drugs, the discovery of these pockets is a critical step in drug design. With the help of computers, protein pockets prediction can save manpower and financial resources.'),
(99231, 2019, 'bmc', 'Automatic discovery of 100-miRNA signature for cancer classification using ensemble feature selection', 'Lopez-Rincon, Alejandro|Martinez-Archundia, Marlet|Martinez-Ruiz, Gustavo U|Schoenhuth, Alexander|Tonda, Alberto', 'Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, Utrecht University, David de Wied building,Universiteitsweg 99, Utrecht, 3584 CG, The Netherlands. alejandro.lopez@iscpif.fr.|Laboratorio de Modelado Molecular, Bioinformática y diseño de fármacos. Departamento de Posgrado. Escuela Superior de Medicina del Instituto Politécnico Nacional (IPN), Mexico City, Mexico.|Faculty of Medicine, National Autonomous University of Mexico, Federico Gomez Childrens Hospital of Mexico, Mexico City, Mexico.|Life Sciences and Health, CWI, Amsterdam, Netherlands.|UMR 782 GMPA, Université Paris-Saclay, INRA, AgroParisTech, Thiverval-Grignon, France.', 'Netherlands|Mexico|Mexico|Netherlands|France', 4, '-', 'Journal Article', '10.1186/s12859-019-3050-8', '31533612', 'MicroRNAs (miRNAs) are noncoding RNA molecules heavily involved in human tumors, in which few of them circulating the human body. Finding a tumor-associated signature of miRNA, that is, the minimum miRNA entities to be measured for discriminating both different types of cancer and normal tissues, is of utmost importance. Feature selection techniques applied in machine learning can help however they often provide naive or biased results.'),
(99232, 2019, 'bmc', 'Adverse drug reaction detection via a multihop self-attention mechanism', 'Zhang, Tongxuan|Lin, Hongfei|Ren, Yuqi|Yang, Liang|Xu, Bo|Yang, Zhihao|Wang, Jian|Zhang, Yijia', 'College of Computer Science and Technology, Dalian University of Technology, Dalian, China.|College of Computer Science and Technology, Dalian University of Technology, Dalian, China. hongfeilin@dlut.edu.cn.|College of Computer Science and Technology, Dalian University of Technology, Dalian, China.|College of Computer Science and Technology, Dalian University of Technology, Dalian, China.|College of Computer Science and Technology, Dalian University of Technology, Dalian, China.|College of Computer Science and Technology, Dalian University of Technology, Dalian, China.|College of Computer Science and Technology, Dalian University of Technology, Dalian, China.|College of Computer Science and Technology, Dalian University of Technology, Dalian, China.', 'China|China|China|China|China|China|China|China', 0, '-', 'Journal Article', '10.1186/s12859-019-3053-5', '31533622', 'The adverse reactions that are caused by drugs are potentially life-threatening problems. Comprehensive knowledge of adverse drug reactions (ADRs) can reduce their detrimental impacts on patients. Detecting ADRs through clinical trials takes a large number of experiments and a long period of time. With the growing amount of unstructured textual data, such as biomedical literature and electronic records, detecting ADRs in the available unstructured data has important implications for ADR research. Most of the neural network-based methods typically focus on the simple semantic information of sentence sequences, however, the relationship of the two entities depends on more complex semantic information.'),
(99233, 2019, 'bmc', 'Machine learning for discovering missing or wrong protein function annotations : A comparison using updated benchmark datasets', 'Nakano, Felipe Kenji|Lietaert, Mathias|Vens, Celine', 'KU Leuven, Campus KULAK - Department of Public Health and Primary Care, Etienne Sabbelaan 53, Kortrijk, 8500, Belgium. felipekenji.nakano@kuleuven.be.|Howest University of Applied Sciences, Campus Brugge Station, Rijselstraat 5, Brugge, 8200, Belgium.|KU Leuven, Campus KULAK - Department of Public Health and Primary Care, Etienne Sabbelaan 53, Kortrijk, 8500, Belgium.', 'Belgium|Belgium|Belgium', 6, '-', 'Journal Article', '10.1186/s12859-019-3060-6', '31547800', 'A massive amount of proteomic data is generated on a daily basis, nonetheless annotating all sequences is costly and often unfeasible. As a countermeasure, machine learning methods have been used to automatically annotate new protein functions. More specifically, many studies have investigated hierarchical multi-label classification (HMC) methods to predict annotations, using the Functional Catalogue (FunCat) or Gene Ontology (GO) label hierarchies. Most of these studies employed benchmark datasets created more than a decade ago, and thus train their models on outdated information. In this work, we provide an updated version of these datasets. By querying recent versions of FunCat and GO yeast annotations, we provide 24 new datasets in total. We compare four HMC methods, providing baseline results for the new datasets. Furthermore, we also evaluate whether the predictive models are able to discover new or wrong annotations, by training them on the old data and evaluating their results against the most recent information.'),
(99234, 2019, 'bmc', 'A machine learning-based service for estimating quality of genomes using PATRIC', 'Parrello, Bruce|Butler, Rory|Chlenski, Philippe|Olson, Robert|Overbeek, Jamie|Pusch, Gordon D|Vonstein, Veronika|Overbeek, Ross', 'Fellowship for Interpretation of Genomes, Burr Ridge, 60527, IL, USA.|Computing, Environment, and Life Sciences Directorate, Argonne National Laboratory, 4200 S. Cass Avenue, Lemont, 60439, IL, USA.|Fellowship for Interpretation of Genomes, Burr Ridge, 60527, IL, USA. pchlenski@gmail.com.|Computing, Environment, and Life Sciences Directorate, Argonne National Laboratory, 4200 S. Cass Avenue, Lemont, 60439, IL, USA.|Fellowship for Interpretation of Genomes, Burr Ridge, 60527, IL, USA.|Fellowship for Interpretation of Genomes, Burr Ridge, 60527, IL, USA.|Fellowship for Interpretation of Genomes, Burr Ridge, 60527, IL, USA.|Fellowship for Interpretation of Genomes, Burr Ridge, 60527, IL, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 1, '-', 'Journal Article', '10.1186/s12859-019-3068-y', '31581946', 'Recent advances in high-volume sequencing technology and mining of genomes from metagenomic samples call for rapid and reliable genome quality evaluation. The current release of the PATRIC database contains over 220,000 genomes, and current metagenomic technology supports assemblies of many draft-quality genomes from a single sample, most of which will be novel.'),
(99235, 2019, 'bmc', 'CAGEfightR: analysis of 5-end data using R/Bioconductor', 'Thodberg, Malte|Thieffry, Axel|Vitting-Seerup, Kristoffer|Andersson, Robin|Sandelin, Albin', 'Department of Biology, University of Copenhagen, Ole Maalxc3xb8es Vej 5, DK2100, Copenhagen N, Denmark. malte.thodberg@bric.ku.dk.|Department of Biology, University of Copenhagen, Ole Maalxc3xb8es Vej 5, DK2100, Copenhagen N, Denmark.|Department of Biology, University of Copenhagen, Ole Maalxc3xb8es Vej 5, DK2100, Copenhagen N, Denmark.|Department of Biology, University of Copenhagen, Ole Maalxc3xb8es Vej 5, DK2100, Copenhagen N, Denmark.|Department of Biology, University of Copenhagen, Ole Maalxc3xb8es Vej 5, DK2100, Copenhagen N, Denmark. albin@binf.ku.dk.', 'Denmark|Denmark|Denmark|Denmark|Denmark', 3, '-', 'Journal Article', '10.1186/s12859-019-3029-5', '31585526', '5-end sequencing assays, and Cap Analysis of Gene Expression (CAGE) in particular, have been instrumental in studying transcriptional regulation. 5-end methods provide genome-wide maps of transcription start sites (TSSs) with base pair resolution. Because active enhancers often feature bidirectional TSSs, such data can also be used to predict enhancer candidates. The current availability of mature and comprehensive computational tools for the analysis of 5-end data is limited, preventing efficient analysis of new and existing 5-end data.');

-- --------------------------------------------------------

--
-- Structure for view `authors_view`
--

CREATE ALGORITHM=UNDEFINED DEFINER=`root`@`localhost` SQL SECURITY DEFINER VIEW `authors_view`  AS  select `a`.`id` AS `id`,`a`.`nome` AS `nome`,`a`.`afiliacao` AS `afiliacao`,`a`.`pais` AS `pais`,`c`.`citacoes_scopus` AS `citacoes`,`c`.`total_papers` AS `total_papers` from (`authors` `a` join `author_citacoes` `c`) where ((`a`.`id` = `c`.`id_author`) and (`a`.`unico` = 1)) ;

--
-- Indexes for dumped tables
--

--
-- Indexes for table `authors`
--
ALTER TABLE `authors`
  ADD PRIMARY KEY (`id`);

--
-- Indexes for table `author_citacoes`
--
ALTER TABLE `author_citacoes`
  ADD PRIMARY KEY (`id`),
  ADD UNIQUE KEY `id_author` (`id_author`);

--
-- Indexes for table `author_desambiguation`
--
ALTER TABLE `author_desambiguation`
  ADD PRIMARY KEY (`id`);

--
-- Indexes for table `author_paper`
--
ALTER TABLE `author_paper`
  ADD PRIMARY KEY (`id`);

--
-- Indexes for table `keywords`
--
ALTER TABLE `keywords`
  ADD PRIMARY KEY (`id`);

--
-- Indexes for table `papers`
--
ALTER TABLE `papers`
  ADD PRIMARY KEY (`id`);

--
-- AUTO_INCREMENT for dumped tables
--

--
-- AUTO_INCREMENT for table `authors`
--
ALTER TABLE `authors`
  MODIFY `id` int(11) NOT NULL AUTO_INCREMENT, AUTO_INCREMENT=199047;

--
-- AUTO_INCREMENT for table `author_citacoes`
--
ALTER TABLE `author_citacoes`
  MODIFY `id` int(11) NOT NULL AUTO_INCREMENT, AUTO_INCREMENT=124168;

--
-- AUTO_INCREMENT for table `author_desambiguation`
--
ALTER TABLE `author_desambiguation`
  MODIFY `id` int(11) NOT NULL AUTO_INCREMENT, AUTO_INCREMENT=141882;

--
-- AUTO_INCREMENT for table `author_paper`
--
ALTER TABLE `author_paper`
  MODIFY `id` int(11) NOT NULL AUTO_INCREMENT, AUTO_INCREMENT=171523;

--
-- AUTO_INCREMENT for table `keywords`
--
ALTER TABLE `keywords`
  MODIFY `id` int(11) NOT NULL AUTO_INCREMENT, AUTO_INCREMENT=32611;

--
-- AUTO_INCREMENT for table `papers`
--
ALTER TABLE `papers`
  MODIFY `id` int(11) NOT NULL AUTO_INCREMENT, AUTO_INCREMENT=99236;
COMMIT;

