INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(98500, 2018, 'bmc', 'Predicting overlapping protein complexes based on core-attachment and a local modularity structure', 'Wang, Rongquan|Liu, Guixia|Wang, Caixia|Su, Lingtao|Sun, Liyan', '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.|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.', 'China|China|China|China|China', 10, 'Algorithms|Cluster Analysis|Databases, Protein|Protein Interaction Mapping|Protein Interaction Maps|Proteins', 'Journal Article', '10.1186/s12859-018-2309-9', '30134824', 'In recent decades, detecting protein complexes (PCs) from protein-protein interaction networks (PPINs) has been an active area of research. There are a large number of excellent graph clustering methods that work very well for identifying PCs. However, most of existing methods usually overlook the inherent core-attachment organization of PCs. Therefore, these methods have three major limitations we should concern. Firstly, many methods have ignored the importance of selecting seed, especially without considering the impact of overlapping nodes as seed nodes. Thus, there may be false predictions. Secondly, PCs are generally supposed to be dense subgraphs. However, the subgraphs with high local modularity structure usually correspond to PCs. Thirdly, a number of available methods lack handling noise mechanism, and miss some peripheral proteins. In summary, all these challenging issues are very important for predicting more biological overlapping PCs.'),
(98501, 2018, 'bmc', 'RNAfitme: a webserver for modeling nucleobase and nucleoside residue conformation in fixed-backbone RNA structures', 'Antczak, Maciej|Zok, Tomasz|Osowiecki, Maciej|Popenda, Mariusz|Adamiak, Ryszard W|Szachniuk, Marta', 'Institute of Computing Science & European Centre for Bioinformatics and Genomics, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland.|Institute of Computing Science & European Centre for Bioinformatics and Genomics, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland.|Department of Biology, Adam Mickiewicz University, Umultowska 89, 61-614, Poznan, Poland.|Institute of Bioorganic Chemistry, Polish Academy of Sciences, Noskowskiego 12/14, 61-704, Poznan, Poland.|Institute of Computing Science & European Centre for Bioinformatics and Genomics, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland.|Institute of Computing Science & European Centre for Bioinformatics and Genomics, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland. marta.szachniuk@cs.put.poznan.pl.', 'Poland|Poland|Poland|Poland|Poland|Poland', 0, 'Algorithms|Base Sequence|Glutamine|Internet|Models, Molecular|Nucleic Acid Conformation|Nucleosides|Nucleotide Motifs|RNA|RNA, Transfer|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/s12859-018-2317-9', '30134831', 'Computational RNA 3D structure prediction and modeling are rising as complementary approaches to high-resolution experimental techniques for structure determination. They often apply to substitute or complement them. Recently, researchers interests have directed towards in silico methods to fit, remodel and refine RNA tertiary structure models. Their power lies in a problem-specific exploration of RNA conformational space and efficient optimization procedures. The aim is to improve the accuracy of models obtained either computationally or experimentally.'),
(98502, 2018, 'bmc', 'bioSyntax: syntax highlighting for computational biology', 'Babaian, Artem|Ebou, Anicet|Fegen, Alyssa|Kam, Ho Yin|Novakovsky, German E|Wong, Jasper|Aïssi, Dylan|Yao, Li', 'Terry Fox Laboratory, BC Cancer Research Centre, 675 West 10th Avenue, Vancouver, BC, V5Z 1L3, Canada. ababaian@bccrc.ca.|Departement de Formation et de Recherches Agriculture et Ressources Animales, Institut National Polytechnique Felix Houphouet-Boigny, Yamoussoukro, Côte dIvoire.|Faculty of Science, University of British Columbia, Vancouver, BC, Canada.|Faculty of Mathematics, University of Waterloo, Waterloo, ON, Canada.|Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada.|Genome Science and Technology, University of British Columbia, Vancouver, BC, Canada.|Department of General and Interventional Cardiology, University Heart Center Hamburg, Hamburg, Germany.|THU-PKU Joint Center for Life Sciences, Tsinghua University, Beijing, China.', 'Canada|Côte d\'Ivoire|Canada|Canada|Canada|Canada|Germany|China', 0, 'Computational Biology|Information Storage and Retrieval|Nucleotides|Sequence Alignment|Software', 'Journal Article', '10.1186/s12859-018-2315-y', '30134911', 'Computational biology requires the reading and comprehension of biological data files. Plain-text formats such as SAM, VCF, GTF, PDB and FASTA, often contain critical information which is obfuscated by the data structure complexity.'),
(98503, 2018, 'bmc', 'miRge 20 for comprehensive analysis of microRNA sequencing data', 'Lu, Yin|Baras, Alexander S|Halushka, Marc K', 'Department of Pathology, Johns Hopkins University SOM, 720 Rutland Avenue/Ross Bldg. Rm 632B, Baltimore, MD, 21205, USA.|Department of Pathology, Johns Hopkins University SOM, 720 Rutland Avenue/Ross Bldg. Rm 632B, Baltimore, MD, 21205, USA.|Department of Pathology, Johns Hopkins University SOM, 720 Rutland Avenue/Ross Bldg. Rm 632B, Baltimore, MD, 21205, USA. mhalush1@jhmi.edu.', 'United States|United States|United States', 8, 'Animals|Computational Biology|High-Throughput Nucleotide Sequencing|Humans|Mice|MicroRNAs|Phylogeny|Rats|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/s12859-018-2287-y', '30153801', 'miRNAs play important roles in the regulation of gene expression. The rapidly developing field of microRNA sequencing (miRNA-seq, small RNA-seq) needs comprehensive, robust, user-friendly and standardized bioinformatics tools to analyze these large datasets. We present miRge 2.0, in which multiple enhancements were made towards these goals.'),
(98504, 2018, 'bmc', 'PseUI: Pseudouridine sites identification based on RNA sequence information', 'He, Jingjing|Fang, Ting|Zhang, Zizheng|Huang, Bei|Zhu, Xiaolei|Xiong, Yi', 'School of Life Sciences, Anhui University, Hefei, 230601, Anhui, China.|School of Life Sciences, Anhui University, Hefei, 230601, Anhui, China.|School of Life Sciences, Anhui University, Hefei, 230601, Anhui, China.|School of Life Sciences, Anhui University, Hefei, 230601, Anhui, China.|School of Life Sciences, Anhui University, Hefei, 230601, Anhui, China. xlzhu_mdl@hotmail.com.|School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China. xiongyi@sjtu.edu.cn.', 'China|China|China|China|China|China', 44, 'Animals|Computational Biology|Humans|Mice|Pseudouridine|RNA|Saccharomyces cerevisiae|Sequence Analysis, RNA|Support Vector Machine', 'Journal Article', '10.1186/s12859-018-2321-0', '30157750', 'Pseudouridylation is the most prevalent type of posttranscriptional modification in various stable RNAs of all organisms, which significantly affects many cellular processes that are regulated by RNA. Thus, accurate identification of pseudouridine (xcexa8) sites in RNA will be of great benefit for understanding these cellular processes. Due to the low efficiency and high cost of current available experimental methods, it is highly desirable to develop computational methods for accurately and efficiently detecting xcexa8 sites in RNA sequences. However, the predictive accuracy of existing computational methods is not satisfactory and still needs improvement.'),
(98505, 2018, 'bmc', 'Rapid and precise alignment of raw reads against redundant databases with KMA', 'Clausen, Philip T L C|Aarestrup, Frank M|Lund, Ole', 'Department of Bioinformatics, Technical University of Denmark, 2800, Kgs Lyngby, Denmark. plan@food.dtu.dk.|Research Group for Genomic Epidemiology, National Food Institute, Technical University of Denmark, 2800, Kgs Lyngby, Denmark.|Department of Bioinformatics, Technical University of Denmark, 2800, Kgs Lyngby, Denmark.', 'Denmark|Denmark|Denmark', 39, 'Algorithms|Computational Biology|Computer Simulation|Databases, Factual|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-018-2336-6', '30157759', 'As the cost of sequencing has declined, clinical diagnostics based on next generation sequencing (NGS) have become reality. Diagnostics based on sequencing will require rapid and precise mapping against redundant databases because some of the most important determinants, such as antimicrobial resistance and core genome multilocus sequence typing (MLST) alleles, are highly similar to one another. In order to facilitate this, a novel mapping method, KMA (k-mer alignment), was designed. KMA is able to map raw reads directly against redundant databases, it also scales well for large redundant databases. KMA uses k-mer seeding to speed up mapping and the Needleman-Wunsch algorithm to accurately align extensions from k-mer seeds. Multi-mapping reads are resolved using a novel sorting scheme (ConClave scheme), ensuring an accurate selection of templates.'),
(98506, 2018, 'bmc', 'Clustering approaches for visual knowledge exploration in molecular interaction networks', 'Ostaszewski, Marek|Kieffer, Emmanuel|Danoy, Grégoire|Schneider, Reinhard|Bouvry, Pascal', 'Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7, Avenue des Hauts-Fourneaux, Esch-Belval, Luxembourg. marek.ostaszewski@uni.lu.|Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, 6, Avenue de la Fonte, Esch-Belval, Luxembourg.|Computer Science and Communications Research Unit, University of Luxembourg, 6, Avenue de la Fonte, Esch-Belval, Luxembourg.|Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7, Avenue des Hauts-Fourneaux, Esch-Belval, Luxembourg.|Computer Science and Communications Research Unit, University of Luxembourg, 6, Avenue de la Fonte, Esch-Belval, Luxembourg.', 'Luxembourg|Luxembourg|Luxembourg|Luxembourg|Luxembourg', 1, 'Algorithms|Cluster Analysis|Computational Biology|Computer Graphics|Data Mining|Databases, Factual|Gene Regulatory Networks|Humans', 'Journal Article', '10.1186/s12859-018-2314-z', '30157777', 'Biomedical knowledge grows in complexity, and becomes encoded in network-based repositories, which include focused, expert-drawn diagrams, networks of evidence-based associations and established ontologies. Combining these structured information sources is an important computational challenge, as large graphs are difficult to analyze visually.'),
(98507, 2018, 'bmc', 'Selection of marker genes for genetic barcoding of microorganisms and binning of metagenomic reads by Barcoder software tools', 'Rotimi, Adeola M|Pierneef, Rian|Reva, Oleg N', 'Centre for Bioinformatics and Computational Biology, Dep. Biochemistry, University of Pretoria, Lynnwood Rd, Hillcrest, Pretoria, 0002, South Africa.|Centre for Bioinformatics and Computational Biology, Dep. Biochemistry, University of Pretoria, Lynnwood Rd, Hillcrest, Pretoria, 0002, South Africa.|Centre for Bioinformatics and Computational Biology, Dep. Biochemistry, University of Pretoria, Lynnwood Rd, Hillcrest, Pretoria, 0002, South Africa. oleg.reva@up.ac.za.', 'South Africa|South Africa|South Africa', 0, 'Algorithms|Bacteria|Base Sequence|DNA Barcoding, Taxonomic|Genes, Bacterial|Genetic Markers|Humans|Metagenome|Metagenomics|Microbiota|Phylogeny|ROC Curve|Reproducibility of Results|Sample Size|Software|Species Specificity|Whole Genome Sequencing', 'Journal Article', '10.1186/s12859-018-2320-1', '30165813', 'Metagenomic approaches have revealed the complexity of environmental microbiomes with the advancement in whole genome sequencing displaying a significant level of genetic heterogeneity on the species level. It has become apparent that patterns of superior bioactivity of bacteria applicable in biotechnology as well as the enhanced virulence of pathogens often requires distinguishing between closely related species or sub-species. Current methods for binning of metagenomic reads usually do not allow for identification below the genus level and generally stops at the family level.'),
(98508, 2018, 'bmc', 'HebbPlot: an intelligent tool for learning and visualizing chromatin mark signatures', 'Girgis, Hani Z|Velasco, Alfredo|Reyes, Zachary E', 'Tandy School of Computer Science, University of Tulsa, 800 South Tucker Drive, Tulsa, 74104-9700, OK, USA. hani-girgis@utulsa.edu.|Tandy School of Computer Science, University of Tulsa, 800 South Tucker Drive, Tulsa, 74104-9700, OK, USA.|Tandy School of Computer Science, University of Tulsa, 800 South Tucker Drive, Tulsa, 74104-9700, OK, USA.', 'United States|United States|United States', 1, 'Chromatin|Epigenomics|Gene Expression Regulation|Histones|Humans|Promoter Regions, Genetic|Protein Processing, Post-Translational|Regulatory Sequences, Nucleic Acid|Software', 'Journal Article', '10.1186/s12859-018-2312-1', '30176808', 'Histone modifications play important roles in gene regulation, heredity, imprinting, and many human diseases. The histone code is complex and consists of more than 100 marks. Therefore, biologists need computational tools to characterize general signatures representing the distributions of tens of chromatin marks around thousands of regions.'),
(98509, 2018, 'bmc', 'Application of whole genome data for in silico evaluation of primers and probes routinely employed for the detection of viral species by RT-qPCR using dengue virus as a case study', 'Vanneste, Kevin|Garlant, Linda|Broeders, Sylvia|Van Gucht, Steven|Roosens, Nancy H', 'Transversal activities in applied genomics, Sciensano, (1050), Brussels, Belgium. kevin.vanneste@sciensano.be.|Transversal activities in applied genomics, Sciensano, (1050), Brussels, Belgium.|Transversal activities in applied genomics, Sciensano, (1050), Brussels, Belgium.|Viral Diseases, Sciensano, (1050), Brussels, Belgium.|Transversal activities in applied genomics, Sciensano, (1050), Brussels, Belgium. nancy.roosens@sciensano.be.', 'Belgium|Belgium|Belgium|Belgium|Belgium', 5, 'Computer Simulation|DNA Primers|Dengue|Dengue Virus|Genome, Viral|Humans|RNA, Viral|Real-Time Polymerase Chain Reaction', 'Evaluation Studies', '10.1186/s12859-018-2313-0', '30180800', 'Viral infection by dengue virus is a major public health problem in tropical countries. Early diagnosis and detection are increasingly based on quantitative reverse transcriptase real-time polymerase chain reaction (RT-qPCR) directed against genomic regions conserved between different isolates. Genetic variation can however result in mismatches of primers and probes with their targeted nucleic acid regions. Whole genome sequencing allows to characterize and track such changes, which in turn enables to evaluate, optimize, and (re-)design novel and existing RT-qPCR methods. The immense amount of available sequence data renders this however a labour-intensive and complex task.'),
(98510, 2018, 'bmc', 'BrownieAligner: accurate alignment of Illumina sequencing data to de Bruijn graphs', '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', 8, 'Algorithms|Computational Biology|Computer Graphics|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Programming Languages|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-018-2319-7', '30180801', 'Aligning short reads to a reference genome is an important task in many genome analysis pipelines. This task is computationally more complex when the reference genome is provided in the form of a de Bruijn graph instead of a linear sequence string.'),
(98511, 2018, 'bmc', 'Classification based on extensions of LS-PLS using logistic regression: application to clinical and multiple genomic data', 'Bazzoli, Caroline|Lambert-Lacroix, Sophie', 'Laboratoire Jean Kuntzman, Univ. Grenoble-Alpes, 700 avenue centrale, Saint Martin dHères, 38401, France. caroline.bazzoli@univ-grenoble-alpes.fr.|TIMC-IMAG, Univ. Grenoble-Alpes, 5 Avenue du Grand Sablon, La Tronche, 38700, France.', 'France|France', 2, 'Algorithms|Datasets as Topic|Gene Expression Profiling|Genome, Human|Genomics|Humans|Least-Squares Analysis|Logistic Models|Neoplasms', 'Journal Article', '10.1186/s12859-018-2311-2', '30189832', 'To address high-dimensional genomic data, most of the proposed prediction methods make use of genomic data alone without considering clinical data, which are often available and known to have predictive value. Recent studies suggest that combining clinical and genomic information may improve predictions. We consider here methods for classification purposes that simultaneously use both types of variables but apply dimensionality reduction only to the high-dimensional genomic ones.'),
(98512, 2018, 'bmc', 'Effective normalization for copy number variation in Hi-C data', 'Servant, Nicolas|Varoquaux, Nelle|Heard, Edith|Barillot, Emmanuel|Vert, Jean-Philippe', 'Institut Curie, PSL Research University, Paris, F-75005, France. nicolas.servant@curie.fr.|Department of Statistics, University of California, Berkeley, USA.|Institut Curie, PSL Research University, CNRS UMR3215, INSERM U934, Paris, F-75005, France.|Institut Curie, PSL Research University, Paris, F-75005, France.|Institut Curie, PSL Research University, Paris, F-75005, France.', 'France|United States|France|France|France', 4, 'Chromosome Aberrations|Chromosome Mapping|Computational Biology|DNA Copy Number Variations|Genome, Human|Genomics|Humans|Neoplasms', 'Journal Article', '10.1186/s12859-018-2256-5', '30189838', 'Normalization is essential to ensure accurate analysis and proper interpretation of sequencing data, and chromosome conformation capture data such as Hi-C have particular challenges. Although several methods have been proposed, the most widely used type of normalization of Hi-C data usually casts estimation of unwanted effects as a matrix balancing problem, relying on the assumption that all genomic regions interact equally with each other.'),
(98513, 2018, 'bmc', 'An integrated strategy for identifying new targets and inferring the mechanism of action: taking rhein as an example', 'Sun, Hao|Shen, Yiting|Luo, Guangwen|Cai, Yuepiao|Xiang, Zheng', 'School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, 325035, China.|School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, 325035, China.|School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, 325035, China.|School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, 325035, China. ypcai@wmu.edu.cn.|School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, 325035, China. xzh0077@126.com.', 'China|China|China|China|China', 2, 'Anthraquinones|Computational Biology|Drug Discovery|Enzyme Inhibitors|Humans|Pharmacology|Protein Interaction Maps|Proteins', 'Journal Article', '10.1186/s12859-018-2346-4', '30189851', 'Target identification is necessary for the comprehensive inference of the mechanism of action of a compound. The application of computational methods to predict the targets of bioactive compounds saves cost and time in drug research and development. Therefore, we designed an integrated strategy consisting of ligand-protein docking, network analysis, enrichment analysis, and an experimental surface plasmon resonance (SPR) method to identify and validate new targets, and then used enriched pathways to elucidate the underlying pharmacological mechanisms. Here, we used rhein, a compound with various pharmacological activities, as an example to find some of its previously unknown targets and to determine its pharmacological activity.'),
(98514, 2018, 'bmc', 'Linked open data-based framework for automatic biomedical ontology generation', 'Alobaidi, Mazen|Malik, Khalid Mahmood|Sabra, Susan', 'Computer Science and Engineering Department, Oakland University, 2200 N. Squirrel Rd, Rochester, MI, 48309, USA.|Computer Science and Engineering Department, Oakland University, 2200 N. Squirrel Rd, Rochester, MI, 48309, USA. mahmood@oakland.edu.|Computer Science and Engineering Department, Oakland University, 2200 N. Squirrel Rd, Rochester, MI, 48309, USA.', 'United States|United States|United States', 9, 'Biological Ontologies|Biomedical Research|Humans|Knowledge Bases|Natural Language Processing|Semantics', 'Journal Article', '10.1186/s12859-018-2339-3', '30200874', 'Fulfilling the vision of Semantic Web requires an accurate data model for organizing knowledge and sharing common understanding of the domain. Fitting this description, ontologies are the cornerstones of Semantic Web and can be used to solve many problems of clinical information and biomedical engineering, such as word sense disambiguation, semantic similarity, question answering, ontology alignment, etc. Manual construction of ontology is labor intensive and requires domain experts and ontology engineers. To downsize the labor-intensive nature of ontology generation and minimize the need for domain experts, we present a novel automated ontology generation framework, Linked Open Data approach for Automatic Biomedical Ontology Generation (LOD-ABOG), which is empowered by Linked Open Data (LOD). LOD-ABOG performs concept extraction using knowledge base mainly UMLS and LOD, along with Natural Language Processing (NLP) operations, and applies relation extraction using LOD, Breadth first Search (BSF) graph method, and Freepal repository patterns.'),
(98515, 2018, 'bmc', 'Enhancer reprogramming in mammalian genomes', 'Flores, Mario A|Ovcharenko, Ivan', 'Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, 20894, USA.|Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, 20894, USA. ovcharen@nih.gov.', 'United States|United States', 4, 'Animals|Enhancer Elements, Genetic|Gene Expression Regulation|Genome|Humans|Mice|Phenotype', 'Journal Article', '10.1186/s12859-018-2343-7', '30200877', 'Transcription factor binding site (TFBS) loss, gain, and reshuffling within the sequence of a regulatory element could alter the function of that regulatory element. Some of the changes will be detrimental to the fitness of the species and will result in gradual removal from a population, while other changes would be either beneficial or just a part of genetic drift and end up being fixed in a population. This reprogramming of regulatory elements results in modification of the gene regulatory landscape during evolution.'),
(98516, 2018, 'bmc', 'Identification of differentially expressed genes by means of outlier detection', 'Irigoien, Itziar|Arenas, Concepción', 'Department of Computation Science and Artificial Intelligence, University of the Basque Country UPV/EHU, Donostia, Spain.|Department of Genetics, Microbiology and Statistics, University of Barcelona, Barcelona, Spain. carenas@ub.edu.', 'Spain|Spain', 3, 'Biomarkers|Gene Expression Profiling|Humans|Neoplasms|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/s12859-018-2318-8', '30200879', 'An important issue in microarray data is to select, from thousands of genes, a small number of informative differentially expressed (DE) genes which may be key elements for a disease. If each gene is analyzed individually, there is a big number of hypotheses to test and a multiple comparison correction method must be used. Consequently, the resulting cut-off value may be too small. Moreover, an important issue is the selections replicability of the DE genes. We present a new method, called ORdensity, to obtain a reproducible selection of DE genes. It takes into account the relation between all genes and it is not a gene-by-gene approach, unlike the usually applied techniques to DE gene selection.'),
(98517, 2018, 'bmc', 'SuperNoder: a tool to discover over-represented modular structures in networks', 'Dessì, Danilo|Cirrone, Jacopo|Recupero, Diego Reforgiato|Shasha, Dennis', 'Department of Mathematics and Computer Science, University of Cagliari, Cagliari, 09124, Italy. danilo_dessi@unica.it.|Department of Computer Science, Courant Institute of Mathematical Sciences, New York University, New York City, 10012, USA.|Department of Mathematics and Computer Science, University of Cagliari, Cagliari, 09124, Italy.|Department of Computer Science, Courant Institute of Mathematical Sciences, New York University, New York City, 10012, USA.', 'Italy|United States|Italy|United States', 0, 'Algorithms|Computational Biology|Humans|Metabolic Networks and Pathways|Protein Interaction Maps|Software', 'Journal Article', '10.1186/s12859-018-2350-8', '30200901', 'Networks whose nodes have labels can seem complex. Fortunately, many have substructures that occur often (motifs). A societal example of a motif might be a household. Replacing such motifs by named supernodes reduces the complexity of the network and can bring out insightful features. Doing so repeatedly may give hints about higher level structures of the network. We call this recursive process Recursive Supernode Extraction.'),
(98518, 2018, 'bmc', 'GeFaST: An improved method for OTU assignment by generalising Swarms fastidious clustering approach', 'Müller, Robert|Nebel, Markus E', 'International Research Training Group Computational Methods for the Analysis of the Diversity and Dynamics of Genomes, Bielefeld University, Bielefeld, Germany. romueller@techfak.uni-bielefeld.de.|International Research Training Group Computational Methods for the Analysis of the Diversity and Dynamics of Genomes, Bielefeld University, Bielefeld, Germany.', 'Germany|Germany', 0, 'Algorithms|Cluster Analysis|Databases as Topic|High-Throughput Nucleotide Sequencing|RNA, Ribosomal, 16S|Time Factors', 'Journal Article', '10.1186/s12859-018-2349-1', '30208838', 'Massive genomic data sets from high-throughput sequencing allow for new insights into complex biological systems such as microbial communities. Analyses of their diversity and structure are typically preceded by clustering millions of 16S rRNA gene sequences into OTUs. Swarm introduced a new clustering strategy which addresses important conceptual and performance issues of the popular de novo clustering approach. However, some parts of the new strategy, e.g. the fastidious option for increased clustering quality, come with their own restrictions.'),
(98519, 2018, 'bmc', 'Searching for 3D structural models from a library of biological shapes using a few 2D experimental images', 'Tiwari, Sandhya P|Tama, Florence|Miyashita, Osamu', 'Computational Structural Biology Unit, RIKEN Center for Computational Science, Kobe, Japan.|Computational Structural Biology Unit, RIKEN Center for Computational Science, Kobe, Japan. florence.tama@riken.jp.|Computational Structural Biology Unit, RIKEN Center for Computational Science, Kobe, Japan.', 'Japan|Japan|Japan', 2, 'Cluster Analysis|Databases as Topic|Imaging, Three-Dimensional|Microscopy, Electron|Models, Molecular', 'Journal Article', '10.1186/s12859-018-2358-0', '30208849', 'Advancements in biophysical experimental techniques have pushed the limits in terms of the types of phenomena that can be characterized, the amount of data that can be produced and the resolution at which we can visualize them. Single particle techniques such as Electron Microscopy (EM) and X-ray free electron laser (XFEL) scattering require a large number of 2D images collected to resolve three-dimensional (3D) structures. In this study, we propose a quick strategy to retrieve potential 3D shapes, as low-resolution models, from a few 2D experimental images by searching a library of 2D projection images generated from existing 3D structures.'),
(98520, 2018, 'bmc', 'Priority-Lasso: a simple hierarchical approach to the prediction of clinical outcome using multi-omics data', 'Klau, Simon|Jurinovic, Vindi|Hornung, Roman|Herold, Tobias|Boulesteix, Anne-Laure', 'Institute for Medical Information Processing, Biometry and Epidemiology, University of Munich, Munich, Germany. simonklau@ibe.med.uni-muenchen.de.|Institute for Medical Information Processing, Biometry and Epidemiology, University of Munich, Munich, Germany.|Institute for Medical Information Processing, Biometry and Epidemiology, University of Munich, Munich, Germany.|Department of Internal Medicine III, University of Munich, Munich, Germany.|Institute for Medical Information Processing, Biometry and Epidemiology, University of Munich, Munich, Germany.', 'Germany|Germany|Germany|Germany|Germany', 6, 'Genomics|Humans|Kaplan-Meier Estimate|Leukemia, Myeloid, Acute|Reproducibility of Results|Risk Factors|Software|Treatment Outcome', 'Journal Article', '10.1186/s12859-018-2344-6', '30208855', 'The inclusion of high-dimensional omics data in prediction models has become a well-studied topic in the last decades. Although most of these methods do not account for possibly different types of variables in the set of covariates available in the same dataset, there are many such scenarios where the variables can be structured in blocks of different types, e.g., clinical, transcriptomic, and methylation data. To date, there exist a few computationally intensive approaches that make use of block structures of this kind.'),
(98521, 2018, 'bmc', 'DM-PhyClus: a Bayesian phylogenetic algorithm for infectious disease transmission cluster inference', 'Villandré, Luc|Labbe, Aurélie|Brenner, Bluma|Roger, Michel|Stephens, David A', 'Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, 1020 avenue des Pins Ouest, Montreal, H3A 1A2, QC, Canada. luc.villandre@mail.mcgill.ca.|Department of Decision Science, HEC Montréal, 3000, chemin de la Côte-Sainte-Catherine, Montreal, H3T 2A7, QC, Canada.|McGill AIDS Centre, Lady Davis Institute, Jewish General Hospital, 3755 chemin de la Côte-Sainte-Catherine, Montreal, H3T 1E2, QC, Canada.|Centre de Recherche du Centre Hospitalier de lUniversité de Montréal (CRCHUM), 900 rue Saint-Denis, Pavillon R, Montreal, H2X 0A9, QC, Canada.|Department of Mathematics and Statistics, McGill University, 805 rue Sherbrooke Ouest, Montreal, H3A 0B9, QC, Canada.', 'Canada|Canada|Canada|Canada|Canada', 3, 'Algorithms|Bayes Theorem|Cluster Analysis|HIV Infections|HIV-1|Homosexuality, Male|Humans|Male|Phylogeny|Software', 'Journal Article', '10.1186/s12859-018-2347-3', '30217139', 'Conventional phylogenetic clustering approaches rely on arbitrary cutpoints applied a posteriori to phylogenetic estimates. Although in practice, Bayesian and bootstrap-based clustering tend to lead to similar estimates, they often produce conflicting measures of confidence in clusters. The current study proposes a new Bayesian phylogenetic clustering algorithm, which we refer to as DM-PhyClus (Dirichlet-Multinomial Phylogenetic Clustering), that identifies sets of sequences resulting from quick transmission chains, thus yielding easily-interpretable clusters, without using any ad hoc distance or confidence requirement.'),
(98522, 2018, 'bmc', 'Grid-based computational methods for the design of constraint-based parsimonious chemical reaction networks to simulate metabolite production: GridProd', 'Tamura, Takeyuki', 'Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Japan. tamura@kuicr.kyoto-u.ac.jp.', 'Japan', 0, 'Algorithms|Carbon|Computational Biology|Escherichia coli|Genome, Bacterial|Metabolic Flux Analysis|Metabolic Networks and Pathways', 'Journal Article', '10.1186/s12859-018-2352-6', '30217144', 'Constraint-based metabolic flux analysis of knockout strategies is an efficient method to simulate the production of useful metabolites in microbes. Owing to the recent development of technologies for artificial DNA synthesis, it may become important in the near future to mathematically design minimum metabolic networks to simulate metabolite production.'),
(98523, 2018, 'bmc', 'Using controls to limit false discovery in the era of big data', 'Parks, Matthew M|Raphael, Benjamin J|Lawrence, Charles E', 'Department of Physiology and Biophysics, Weill Cornell Medicine, 1300 York Ave, New York, NY, 10065, USA.|Department of Computer Science, Princeton University, 35 Olden Street, Princeton, NJ, 08540, USA.|Center for Computational Molecular Biology, Brown University, 115 Waterman Street, Providence, RI, 02912, USA. charles_lawrence@brown.edu.', 'United States|United States|United States', 0, 'Bayes Theorem|DNA Repair|Databases, Factual|Genome, Human|Humans|Models, Statistical', 'Journal Article', '10.1186/s12859-018-2356-2', '30217148', 'Procedures for controlling the false discovery rate (FDR) are widely applied as a solution to the multiple comparisons problem of high-dimensional statistics. Current FDR-controlling procedures require accurately calculated p-values and rely on extrapolation into the unknown and unobserved tails of the null distribution. Both of these intermediate steps are challenging and can compromise the reliability of the results.'),
(98524, 2018, 'bmc', 'Phage spanins: diversity, topological dynamics and gene convergence', 'Kongari, Rohit|Rajaure, Manoj|Cahill, Jesse|Rasche, Eric|Mijalis, Eleni|Berry, Joel|Young, Ry', 'Center for Phage Technology, Department of Biochemistry and Biophysics, Texas A&M University, 2128 TAMU, College Station, TX, 77843-2128, USA.|National Institutes of Health, Bethesda, MD, USA.|Center for Phage Technology, Department of Biochemistry and Biophysics, Texas A&M University, 2128 TAMU, College Station, TX, 77843-2128, USA.|Center for Phage Technology, Department of Biochemistry and Biophysics, Texas A&M University, 2128 TAMU, College Station, TX, 77843-2128, USA.|Center for Phage Technology, Department of Biochemistry and Biophysics, Texas A&M University, 2128 TAMU, College Station, TX, 77843-2128, USA.|University of California, San Francisco, CA, USA.|Center for Phage Technology, Department of Biochemistry and Biophysics, Texas A&M University, 2128 TAMU, College Station, TX, 77843-2128, USA. ryland@tamu.edu.', 'United States|United States|United States|United States|United States|United States|United States', 11, 'Amino Acid Sequence|Bacteriophage lambda|Protein Conformation|Protein Domains|Sequence Homology|Viral Proteins', 'Journal Article', '10.1186/s12859-018-2342-8', '30219026', 'Spanins are phage lysis proteins required to disrupt the outer membrane. Phages employ either two-component spanins or unimolecular spanins in this final step of Gram-negative host lysis. Two-component spanins like Rz-Rz1 from phage lambda consist of an integral inner membrane protein: i-spanin, and an outer membrane lipoprotein: o-spanin, that form a complex spanning the periplasm. Two-component spanins exist in three different genetic architectures, embedded, overlapped and separated. In contrast, the unimolecular spanins, like gp11 from phage T1, have an N-terminal lipoylation signal sequence and a C-terminal transmembrane domain to account for the topology requirements. Our proposed model for spanin function, for both spanin types, follows a common theme of the outer membrane getting fused with the inner membrane, effecting the release of progeny virions.'),
(98525, 2018, 'bmc', 'A document level neural model integrated domain knowledge for chemical-induced disease relations', 'Zheng, Wei|Lin, Hongfei|Liu, Xiaoxia|Xu, Bo', 'College of Computer Science and Technology, Dalian University of Technology, Dalian, China.|College of Computer Science and Technology, Dalian University of Technology, Dalian, China. hflin@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. xubo@mail.dlut.edu.cn.', 'China|China|China|China', 0, 'Data Mining|Databases, Factual|Disease|Humans|Machine Learning|Neural Networks (Computer)', 'Journal Article', '10.1186/s12859-018-2316-x', '30223767', 'The effective combination of texts and knowledge may improve performances of natural language processing tasks. For the recognition of chemical-induced disease (CID) relations which may span sentence boundaries in an article, although existing CID systems explored the utilization for knowledge bases, the effects of different knowledge on the identification of a special CID havent been distinguished by these systems. Moreover, systems based on neural network only constructed sentence or mention level models.'),
(98526, 2018, 'bmc', 'Data-driven human transcriptomic modules determined by independent component analysis', 'Zhou, Weizhuang|Altman, Russ B', 'Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA.|Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA. russ.altman@stanford.edu.', 'United States|United States', 5, 'Algorithms|Arthritis, Rheumatoid|DNA Fingerprinting|Humans|Leukemia|Oligonucleotide Array Sequence Analysis|Rhabdomyosarcoma|Transcriptome', 'Journal Article', '10.1186/s12859-018-2338-4', '30223787', 'Analyzing the human transcriptome is crucial in advancing precision medicine, and the plethora of over half a million human microarray samples in the Gene Expression Omnibus (GEO) has enabled us to better characterize biological processes at the molecular level. However, transcriptomic analysis is challenging because the data is inherently noisy and high-dimensional. Gene set analysis is currently widely used to alleviate the issue of high dimensionality, but the user-defined choice of gene sets can introduce biasness in results. In this paper, we advocate the use of a fixed set of transcriptomic modules for such analysis. We apply independent component analysis to the large collection of microarray data in GEO in order to discover reproducible transcriptomic modules that can be used as features for machine learning. We evaluate the usability of these modules across six studies, and demonstrate (1) their usage as features for sample classification, and also their robustness in dealing with small training sets, (2) their regularization of data when clustering samples and (3) the biological relevancy of differentially expressed features.'),
(98527, 2018, 'bmc', 'Detecting gene-gene interactions for complex quantitative traits using generalized fuzzy classification', 'Zhou, Xiangdong|Chan, Keith C C', 'College of Mathematics and Computer Science, Fuzhou University, Fuzhou, Fujian, China. zhou_xiangdong@hotmail.com.|Department of Computing, the Hong Kong Polytechnic University, Kowloon, Hong Kong, China.', 'China|China', 0, 'Animals|Computational Biology|Epistasis, Genetic|Female|Fuzzy Logic|Genetic Markers|Humans|Mice|Models, Genetic|Phenotype|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/s12859-018-2361-5', '30227829', 'Quantitative traits or continuous outcomes related to complex diseases can provide more information and therefore more accurate analysis for identifying gene-gene and gene- environment interactions associated with complex diseases. Multifactor Dimensionality Reduction (MDR) is originally proposed to identify gene-gene and gene- environment interactions associated with binary status of complex diseases. Some efforts have been made to extend it to quantitative traits (QTs) and ordinal traits. However these and other methods are still not computationally efficient or effective.'),
(98528, 2018, 'bmc', 'Detecting differentially expressed genes for syndromes by considering change in mean and dispersion simultaneously', 'Ma, Chenchen|Ji, Tieming', 'Department of Statistics, University of Missouri at Columbia, Columbia, 65211, MO, USA.|Department of Statistics, University of Missouri at Columbia, Columbia, 65211, MO, USA. jit@missouri.edu.', 'United States|United States', 0, 'Bayes Theorem|Gene Expression Profiling|Gene Expression Regulation|Humans|Syndrome', 'Journal Article', '10.1186/s12859-018-2354-4', '30236056', 'Using next-generation sequencing technology to measure gene expression, an empirically intriguing question concerns the identification of differentially expressed genes across treatment groups. Existing methods aim to identify genes whose mean expressions differ among treatment groups by assuming equal dispersion across all groups. For syndromes, however, various combinations of gene expression alterations can result in the same disease, leading to greater heteroscedasticity in the biological replicates in the disease group compared to the normal group. Traditional methods that only consider changes in the mean will fail to fully analyze gene expression in such a scenario. In addition, sequencing technology is relatively expensive, most labs can only afford a few replicates per treatment group, which poses further challenges to reliably estimating the mean and dispersion under each treatment condition.'),
(98529, 2018, 'bmc', 'Identifying protein complexes based on node embeddings obtained from protein-protein interaction networks', 'Liu, Xiaoxia|Yang, Zhihao|Sang, Shengtian|Zhou, Ziwei|Wang, Lei|Zhang, Yin|Lin, Hongfei|Wang, Jian|Xu, Bo', 'College of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, Liaoning, Peoples Republic of China.|College of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, Liaoning, Peoples Republic of China. yangzh@dlut.edu.cn.|College of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, Liaoning, Peoples Republic of China.|College of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, Liaoning, Peoples Republic of China.|Beijing Institute of Health Administration and Medical Information, Beijing, 100850, Peoples Republic of China. wangleibihami@gmail.com.|Beijing Institute of Health Administration and Medical Information, Beijing, 100850, Peoples Republic of China.|College of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, Liaoning, Peoples Republic of China.|College of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, Liaoning, Peoples Republic of China.|School of Software Technology, Dalian University of Technology, Dalian, 116024, Liaoning, Peoples Republic of China.', 'China|China|China|China|China|China|China|China|China', 2, 'Algorithms|Computational Biology|Humans|Protein Interaction Mapping|Protein Interaction Maps|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins', 'Journal Article', '10.1186/s12859-018-2364-2', '30241459', 'Protein complexes are one of the keys to deciphering the behavior of a cell system. During the past decade, most computational approaches used to identify protein complexes have been based on discovering densely connected subgraphs in protein-protein interaction (PPI) networks. However, many true complexes are not dense subgraphs and these approaches show limited performances for detecting protein complexes from PPI networks.'),
(98530, 2018, 'bmc', 'Progressive multiple sequence alignment with indel evolution', 'Maiolo, Massimo|Zhang, Xiaolei|Gil, Manuel|Anisimova, Maria', 'Institute of Applied Simulation, School of Life Sciences and Facility Management, Zurich University of Applied Sciences (ZHAW), Grüentalstrasse 14, P.O. Box, Waedenswil, CH-8820, Switzerland.|National Heart and Lung Institute, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK.|Institute of Applied Simulation, School of Life Sciences and Facility Management, Zurich University of Applied Sciences (ZHAW), Grüentalstrasse 14, P.O. Box, Waedenswil, CH-8820, Switzerland.|Institute of Applied Simulation, School of Life Sciences and Facility Management, Zurich University of Applied Sciences (ZHAW), Grüentalstrasse 14, P.O. Box, Waedenswil, CH-8820, Switzerland. maria.anisimova@zhaw.ch.', 'Switzerland|United Kingdom|Switzerland|Switzerland', 0, 'Algorithms|Evolution, Molecular|Humans|INDEL Mutation|Phylogeny|Probability|Sequence Alignment', 'Journal Article', '10.1186/s12859-018-2357-1', '30241460', 'Sequence alignment is crucial in genomics studies. However, optimal multiple sequence alignment (MSA) is NP-hard. Thus, modern MSA methods employ progressive heuristics, breaking the problem into a series of pairwise alignments guided by a phylogeny. Changes between homologous characters are typically modelled by a Markov substitution model. In contrast, the dynamics of indels are not modelled explicitly, because the computation of the marginal likelihood under such models has exponential time complexity in the number of taxa. But the failure to model indel evolution may lead to artificially short alignments due to biased indel placement, inconsistent with phylogenetic relationship.'),
(98531, 2018, 'bmc', 'Overlapping group screening for detection of gene-gene interactions: application to gene expression profiles with survival trait', 'Wang, Jie-Huei|Chen, Yi-Hau', 'Institute of Statistical Science, Academia Sinica, Nankang, Taipei, Taiwan.|Institute of Statistical Science, Academia Sinica, Nankang, Taipei, Taiwan. yhchen@stat.sinica.edu.tw.', '-|-', 1, 'Algorithms|Carcinoma, Non-Small-Cell Lung|Computer Simulation|Databases, Factual|Epistasis, Genetic|Gene Expression Profiling|Genetic Loci|Humans|Lung Neoplasms|Lymphoma, Large B-Cell, Diffuse|Predictive Value of Tests|Survival Rate|Transcriptome', 'Journal Article', '10.1186/s12859-018-2372-2', '30241463', 'The development of a disease is a complex process that may result from joint effects of multiple genes. In this article, we propose the overlapping group screening (OGS) approach to determining active genes and gene-gene interactions incorporating prior pathway information. The OGS method is developed to overcome the challenges in genome-wide data analysis that the number of the genes and gene-gene interactions is far greater than the sample size, and the pathways generally overlap with one another. The OGS method is further proposed for patients survival prediction based on gene expression data.'),
(98532, 2018, 'bmc', 'Initial state perturbations as a validation method for data-driven fuzzy models of cellular networks', 'Magdevska, Lidija|Mraz, Miha|Zimic, Nikolaj|Moxc5xa1kon, Miha', 'Faculty of Computer and Information Science, University of Ljubljana, Vexc4x8dna pot 113, Ljubljana, 1000, Slovenia. lm4828@student.uni-lj.si.|Faculty of Computer and Information Science, University of Ljubljana, Vexc4x8dna pot 113, Ljubljana, 1000, Slovenia.|Faculty of Computer and Information Science, University of Ljubljana, Vexc4x8dna pot 113, Ljubljana, 1000, Slovenia.|Faculty of Computer and Information Science, University of Ljubljana, Vexc4x8dna pot 113, Ljubljana, 1000, Slovenia.', 'Slovenia|Slovenia|Slovenia|Slovenia', 1, 'Computational Biology|Computer Simulation|Fuzzy Logic|Gene Regulatory Networks|Humans|Systems Biology', 'Journal Article', '10.1186/s12859-018-2366-0', '30241464', 'Data-driven methods that automatically learn relations between attributes from given data are a popular tool for building mathematical models in computational biology. Since measurements are prone to errors, approaches dealing with uncertain data are especially suitable for this task. Fuzzy models are one such approach, but they contain a large amount of parameters and are thus susceptible to over-fitting. Validation methods that help detect over-fitting are therefore needed to eliminate inaccurate models.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(98533, 2018, 'bmc', 'ECPred: a tool for the prediction of the enzymatic functions of protein sequences based on the EC nomenclature', 'Dalkiran, Alperen|Rifaioglu, Ahmet Sureyya|Martin, Maria Jesus|Cetin-Atalay, Rengul|Atalay, Volkan|Doxc4x9fan, Tunca', 'Department of Computer Engineering, Middle East Technical University, 06800, Ankara, Turkey.|Department of Computer Engineering, Middle East Technical University, 06800, Ankara, Turkey.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, UK.|KanSiL, Graduate School of Informatics, Middle East Technical University, 06800, Ankara, Turkey.|Department of Computer Engineering, Middle East Technical University, 06800, Ankara, Turkey. vatalay@metu.edu.tr.|European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, UK. tdogan@ebi.ac.uk.', 'Turkey|Turkey|United Kingdom|Turkey|Turkey|United Kingdom', 15, 'Algorithms|Computational Biology|Enzymes|Humans|Sequence Analysis, Protein|Software|Terminology as Topic', 'Journal Article', '10.1186/s12859-018-2368-y', '30241466', 'The automated prediction of the enzymatic functions of uncharacterized proteins is a crucial topic in bioinformatics. Although several methods and tools have been proposed to classify enzymes, most of these studies are limited to specific functional classes and levels of the Enzyme Commission (EC) number hierarchy. Besides, most of the previous methods incorporated only a single input feature type, which limits the applicability to the wide functional space. Here, we proposed a novel enzymatic function prediction tool, ECPred, based on ensemble of machine learning classifiers.'),
(98534, 2018, 'bmc', 'Machine Learning for detection of viral sequences in human metagenomic datasets', 'Bzhalava, Zurab|Tampuu, Ardi|Baxc5x82a, Piotr|Vicente, Raul|Dillner, Joakim', 'Dept. of Laboratory Medicine, Karolinska Institutet, F46, Karolinska University Hospital Huddinge, Stockholm, Sweden.|Institute of Computer Science, University of Tartu, Tartu, Estonia.|Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw, Warsaw, Poland.|Institute of Computer Science, University of Tartu, Tartu, Estonia.|Dept. of Laboratory Medicine, Karolinska Institutet, F46, Karolinska University Hospital Huddinge, Stockholm, Sweden. joakim.dillner@ki.se.', 'Sweden|Estonia|Poland|Estonia|Sweden', 6, 'Algorithms|Base Sequence|Computational Biology|Databases, Genetic|Humans|Machine Learning|Metagenomics|Neural Networks (Computer)|ROC Curve|Sequence Analysis, DNA|Viruses', 'Journal Article', '10.1186/s12859-018-2340-x', '30249176', 'Detection of highly divergent or yet unknown viruses from metagenomics sequencing datasets is a major bioinformatics challenge. When human samples are sequenced, a large proportion of assembled contigs are classified as unknown, as conventional methods find no similarity to known sequences. We wished to explore whether machine learning algorithms using Relative Synonymous Codon Usage frequency (RSCU) could improve the detection of viral sequences in metagenomic sequencing data.'),
(98535, 2018, 'bmc', 'Molecular dynamics and structure function analysis show that substrate binding and specificity are major forces in the functional diversification of Eqolisins', 'Stocchi, Nicolxc3xa1s|Revuelta, Marxc3xada Victoria|Castronuovo, Priscila Ailxc3xadn Lanza|Vera, D Mariano A|Ten Have, Arjen', 'Instituto de Investigaciones Biolxc3xb3gicas (IIB-CONICET-UNMdP), Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Mar del Plata, CC 1245, 7600, Mar del Plata, Argentina.|Instituto de Investigaciones Biolxc3xb3gicas (IIB-CONICET-UNMdP), Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Mar del Plata, CC 1245, 7600, Mar del Plata, Argentina.|QUIAMM-INBIOTEC-CONICET, Department of Chemistry - Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Mar del Plata, Funes 3350, 7600, Mar del Plata, Argentina.|QUIAMM-INBIOTEC-CONICET, Department of Chemistry - Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Mar del Plata, Funes 3350, 7600, Mar del Plata, Argentina.|Instituto de Investigaciones Biolxc3xb3gicas (IIB-CONICET-UNMdP), Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Mar del Plata, CC 1245, 7600, Mar del Plata, Argentina. tenhave.arjen@gmail.com.', 'Argentina|Argentina|Argentina|Argentina|Argentina', 0, 'Amino Acid Sequence|Amino Acid Substitution|Aspartic Acid Endopeptidases|Bayes Theorem|Binding Sites|Conserved Sequence|Evolution, Molecular|Gene Transfer, Horizontal|Glycine|Models, Molecular|Molecular Dynamics Simulation|Mutation|Phylogeny|Protein Structure, Secondary|Sequence Homology, Amino Acid|Structure-Activity Relationship|Substrate Specificity|Thermodynamics', 'Journal Article', '10.1186/s12859-018-2348-2', '30249179', 'Eqolisins are rare acid proteases found in archaea, bacteria and fungi. Certain fungi secrete acids as part of their lifestyle and interestingly these also have many eqolisin paralogs, up to nine paralogs have been recorded. This suggests a process of functional redundancy and diversification has occurred, which was the subject of the research we performed and describe here.'),
(98536, 2018, 'bmc', 'Fast design of arbitrary length loops in proteins using InteractiveRosetta', 'Hooper, William F|Walcott, Benjamin D|Wang, Xing|Bystroff, Christopher', 'Emmes Corporation, Rockville, Washington, MD, USA.|Department of Biology, Rensselaer Polytechnic Institute, Troy, NY, USA.|Department of Chemistry and Chemical Biology, Rensselaer Polytechnic Institute, Troy, NY, USA.|Department of Biology, Rensselaer Polytechnic Institute, Troy, NY, USA. bystrc@rpi.edu.', 'United States|United States|United States|United States', 3, 'Genetic Engineering|INDEL Mutation|Models, Molecular|Protein Domains|Protein Multimerization|Protein Structure, Secondary|Proteins|Software|Time Factors', 'Journal Article', '10.1186/s12859-018-2345-5', '30249181', 'With increasing interest in ab initio protein design, there is a desire to be able to fully explore the design space of insertions and deletions. Nature inserts and deletes residues to optimize energy and function, but allowing variable length indels in the context of an interactive protein design session presents challenges with regard to speed and accuracy.'),
(98537, 2018, 'bmc', 'Valection: design optimization for validation and verification studies', 'Cooper, Christopher I|Yao, Delia|Sendorek, Dorota H|Yamaguchi, Takafumi N|Png, Christine|Houlahan, Kathleen E|Caloian, Cristian|Fraser, Michael|SMC-DNA Challenge Participants|Ellrott, Kyle|Margolin, Adam A|Bristow, Robert G|Stuart, Joshua M|Boutros, Paul C', 'Ontario Institute for Cancer Research, 661 University Avenue, Suite 510, Toronto, Ontario, M5G 0A3, Canada.|Ontario Institute for Cancer Research, 661 University Avenue, Suite 510, Toronto, Ontario, M5G 0A3, Canada.|Ontario Institute for Cancer Research, 661 University Avenue, Suite 510, Toronto, Ontario, M5G 0A3, Canada.|Ontario Institute for Cancer Research, 661 University Avenue, Suite 510, Toronto, Ontario, M5G 0A3, Canada.|Ontario Institute for Cancer Research, 661 University Avenue, Suite 510, Toronto, Ontario, M5G 0A3, Canada.|Ontario Institute for Cancer Research, 661 University Avenue, Suite 510, Toronto, Ontario, M5G 0A3, Canada.|Ontario Institute for Cancer Research, 661 University Avenue, Suite 510, Toronto, Ontario, M5G 0A3, Canada.|Princess Margaret Cancer Centre, University Health Network, Toronto, Canada.|Princess Margaret Cancer Centre, University Health Network, Toronto, Canada.|Computational Biology Program, Oregon Health & Science University, Portland, OR, USA.|Computational Biology Program, Oregon Health & Science University, Portland, OR, USA.|Department of Medical Biophysics, University of Toronto, Toronto, Canada.|Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA, USA.|Ontario Institute for Cancer Research, 661 University Avenue, Suite 510, Toronto, Ontario, M5G 0A3, Canada. Paul.Boutros@oicr.on.ca.', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|United States|United States|Canada|United States|Canada', 2, 'Sequence Analysis, DNA|Software Validation', 'Journal Article', '10.1186/s12859-018-2391-z', '30253747', 'Platform-specific error profiles necessitate confirmatory studies where predictions made on data generated using one technology are additionally verified by processing the same samples on an orthogonal technology. However, verifying all predictions can be costly and redundant, and testing a subset of findings is often used to estimate the true error profile.'),
(98538, 2018, 'bmc', 'Efficient inference of homologs in large eukaryotic pan-proteomes', 'Sheikhizadeh Anari, Siavash|de Ridder, Dick|Schranz, M Eric|Smit, Sandra', 'Bioinformatics Group, Wageningen University, Wageningen, The Netherlands. siavash.sheikhizadehanari@wur.nl.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.|Biosystematics Group, Wageningen University, Wageningen, The Netherlands.|Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands', 1, 'Algorithms|Brassicaceae|Cluster Analysis|Databases, Protein|Eukaryota|Genes, Plant|Genome|Genomics|Humans|Proteome|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1186/s12859-018-2362-4', '30257640', 'Identification of homologous genes is fundamental to comparative genomics, functional genomics and phylogenomics. Extensive public homology databases are of great value for investigating homology but need to be continually updated to incorporate new sequences. As new sequences are rapidly being generated, there is a need for efficient standalone tools to detect homologs in novel data.'),
(98539, 2018, 'bmc', 'Prototyping a precision oncology 30 rapid learning platform', 'Sweetnam, Connor|Mocellin, Simone|Krauthammer, Michael|Knopf, Nathaniel|Baertsch, Robert|Shrager, Jeff', 'Cancer Commons, Los Altos, CA, USA.|Istituto Oncologico Veneto, IOV-IRCSS, and Department of Surgery Oncology and Gastroenterology, University of Padova, Padova, Italy.|Program for Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.|Cancer Commons, Los Altos, CA, USA.|Cancer Commons, Los Altos, CA, USA.|Cancer Commons, Los Altos, CA, USA. jshrager@stanford.edu.', 'United States|Italy|United States|United States|United States|United States', 1, 'Algorithms|Education, Medical|Humans|Medical Oncology|Precision Medicine|Publications|Software', 'Journal Article', '10.1186/s12859-018-2374-0', '30257653', 'We describe a prototype implementation of a platform that could underlie a Precision Oncology Rapid Learning system.'),
(98540, 2018, 'bmc', 'Exploring spatial-frequency-sequential relationships for motor imagery classification with recurrent neural network', 'Luo, Tian-Jian|Zhou, Chang-le|Chao, Fei', 'Department of Cognitive Science, School of Information Science and Engineering, Xiamen University, 422 Siming South Road, Siming District, Xiamen, 361005, China.|Department of Cognitive Science, School of Information Science and Engineering, Xiamen University, 422 Siming South Road, Siming District, Xiamen, 361005, China.|Department of Cognitive Science, School of Information Science and Engineering, Xiamen University, 422 Siming South Road, Siming District, Xiamen, 361005, China. fchao@xmu.edu.cn.', 'China|China|China', 18, 'Algorithms|Brain-Computer Interfaces|Databases, Genetic|Datasets as Topic|Electroencephalography|Humans|Imagination|Neural Networks (Computer)|Signal Processing, Computer-Assisted|Support Vector Machine', 'Journal Article', '10.1186/s12859-018-2365-1', '30268089', 'Conventional methods of motor imagery brain computer interfaces (MI-BCIs) suffer from the limited number of samples and simplified features, so as to produce poor performances with spatial-frequency features and shallow classifiers.'),
(98541, 2018, 'bmc', 'Optimizing PCR primers targeting the bacterial 16S ribosomal RNA gene', 'Sambo, Francesco|Finotello, Francesca|Lavezzo, Enrico|Baruzzo, Giacomo|Masi, Giulia|Peta, Elektra|Falda, Marco|Toppo, Stefano|Barzon, Luisa|Di Camillo, Barbara', 'Department of Information Engineering, University of Padova, Padova, Italy.|Biocenter, Division of Bioinformatics, Medical University of Innsbruck, Innsbruck, Austria.|Department of Molecular Medicine, University of Padova, Padova, Italy.|Department of Information Engineering, University of Padova, Padova, Italy.|Department of Molecular Medicine, University of Padova, Padova, Italy.|Department of Molecular Medicine, University of Padova, Padova, Italy.|Department of Molecular Medicine, University of Padova, Padova, Italy.|Department of Molecular Medicine, University of Padova, Padova, Italy.|Department of Molecular Medicine, University of Padova, Padova, Italy.|Department of Information Engineering, University of Padova, Padova, Italy. barbara.dicamillo@unipd.it.', 'Italy|Austria|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 0, 'Bacteria|DNA Primers|Polymerase Chain Reaction|RNA, Bacterial|RNA, Ribosomal, 16S|Software', 'Journal Article', '10.1186/s12859-018-2360-6', '30268091', 'Targeted amplicon sequencing of the 16S ribosomal RNA gene is one of the key tools for studying microbial diversity. The accuracy of this approach strongly depends on the choice of primer pairs and, in particular, on the balance between efficiency, specificity and sensitivity in the amplification of the different bacterial 16S sequences contained in a sample. There is thus the need for computational methods to design optimal bacterial 16S primers able to take into account the knowledge provided by the new sequencing technologies.'),
(98542, 2018, 'bmc', 'MITE Tracker: an accurate approach to identify miniature inverted-repeat transposable elements in large genomes', 'Crescente, Juan Manuel|Zavallo, Diego|Helguera, Marcelo|Vanzetti, Leonardo Sebastián', 'Grupo Biotecnología y Recursos Genéticos, EEA INTA Marcos Juárez, Ruta 12 km 3, 2580, Marcos Juárez, Argentina.|Instituto de Biotecnología, CNIA, Instituto Nacional de Tecnología Agropecuaria (INTA) Castelar, Los Reseros y Nicolas Repeto, Hurlingham, Buenos Aires, Argentina.|Grupo Biotecnología y Recursos Genéticos, EEA INTA Marcos Juárez, Ruta 12 km 3, 2580, Marcos Juárez, Argentina.|Grupo Biotecnología y Recursos Genéticos, EEA INTA Marcos Juárez, Ruta 12 km 3, 2580, Marcos Juárez, Argentina. vanzetti.leonardo@inta.gob.ar.', 'Argentina|Argentina|Argentina|Argentina', 0, 'DNA Transposable Elements|Genomics|Inverted Repeat Sequences|Oryza|Software', 'Journal Article', '10.1186/s12859-018-2376-y', '30285604', 'Miniature inverted-repeat transposable elements (MITEs) are short, non-autonomous class II transposable elements present in a high number of conserved copies in eukaryote genomes. An accurate identification of these elements can help to shed light on the mechanisms controlling genome evolution and gene regulation. The structure and distribution of these elements are well-defined and therefore computational approaches can be used to identify MITEs sequences.'),
(98543, 2018, 'bmc', 'Uncovering new disease indications for G-protein coupled receptors and their endogenous ligands', 'Freudenberg, Johannes M|Dunham, Ian|Sanseau, Philippe|Rajpal, Deepak K', 'Computational Biology, Target Sciences, GlaxoSmithKline, Collegeville, PA, 19426, USA.|Open Targets, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.|Open Targets, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.|Computational Biology, Target Sciences, GlaxoSmithKline, Collegeville, PA, 19426, USA. Deepak.K.Rajpal@gsk.com.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Disease|Drug Discovery|Humans|Ligands|Protein Binding|Receptors, G-Protein-Coupled', 'Journal Article', '10.1186/s12859-018-2392-y', '30285606', 'The Open Targets Platform integrates different data sources in order to facilitate identification of potential therapeutic drug targets to treat human diseases. It currently provides evidence for nearly 2.6 million potential target-disease pairs. G-protein coupled receptors are a drug target class of high interest because of the number of successful drugs being developed against them over many years. Here we describe a systematic approach utilizing the Open Targets Platform data to uncover and prioritize potential new disease indications for the G-protein coupled receptors and their ligands.'),
(98544, 2018, 'bmc', 'A deep learning-based algorithm for 2-D cell segmentation in microscopy images', 'Al-Kofahi, Yousef|Zaltsman, Alla|Graves, Robert|Marshall, Will|Rusu, Mirabela', 'GE Global Research, One Research Circle, Niskayuna, 12309, NY, USA. alkofahi@ge.com.|GE Healthcare, 1040 12th Ave NW, Issaquah, 98027, WA, USA.|GE Healthcare, 1040 12th Ave NW, Issaquah, 98027, WA, USA.|GE Healthcare, 1040 12th Ave NW, Issaquah, 98027, WA, USA.|GE Global Research, One Research Circle, Niskayuna, 12309, NY, USA.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Humans|Microscopy', 'Journal Article', '10.1186/s12859-018-2375-z', '30285608', 'Automatic and reliable characterization of cells in cell cultures is key to several applications such as cancer research and drug discovery. Given the recent advances in light microscopy and the need for accurate and high-throughput analysis of cells, automated algorithms have been developed for segmenting and analyzing the cells in microscopy images. Nevertheless, accurate, generic and robust whole-cell segmentation is still a persisting need to precisely quantify its morphological properties, phenotypes and sub-cellular dynamics.'),
(98545, 2018, 'bmc', 'Biotite: a unifying open source computational biology framework in Python', 'Kunzmann, Patrick|Hamacher, Kay', 'Department of Computational Biology and Simulation, TU Darmstadt, Schnittspahnstraße 2, Darmstadt, 64287, Germany. kunzmann@bio.tu-darmstadt.de.|Department of Computational Biology and Simulation, TU Darmstadt, Schnittspahnstraße 2, Darmstadt, 64287, Germany.', 'Germany|Germany', 5, 'Aluminum Silicates|Computational Biology|Ferrous Compounds|Programming Languages|Software', 'Journal Article', '10.1186/s12859-018-2367-z', '30285630', 'As molecular biology is creating an increasing amount of sequence and structure data, the multitude of software to analyze this data is also rising. Most of the programs are made for a specific task, hence the user often needs to combine multiple programs in order to reach a goal. This can make the data processing unhandy, inflexible and even inefficient due to an overhead of read/write operations. Therefore, it is crucial to have a comprehensive, accessible and efficient computational biology framework in a scripting language to overcome these limitations.'),
(98546, 2018, 'bmc', 'XenofilteR: computational deconvolution of mouse and human reads in tumor xenograft sequence data', 'Kluin, Roelof J C|Kemper, Kristel|Kuilman, Thomas|de Ruiter, Julian R|Iyer, Vivek|Forment, Josep V|Cornelissen-Steijger, Paulien|de Rink, Iris|Ter Brugge, Petra|Song, Ji-Ying|Klarenbeek, Sjoerd|McDermott, Ultan|Jonkers, Jos|Velds, Arno|Adams, David J|Peeper, Daniel S|Krijgsman, Oscar', 'Central Genomic Facility, Netherlands Cancer Institute, Amsterdam, The Netherlands.|Division of Molecular Oncology and Immunology, Netherlands Cancer Institute, Plesmanlaan 121, 1066, CX, Amsterdam, The Netherlands.|Division of Molecular Oncology and Immunology, Netherlands Cancer Institute, Plesmanlaan 121, 1066, CX, Amsterdam, The Netherlands.|Division of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands.|Experimental Cancer Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK.|The Wellcome Trust/Cancer Research UK (CRUK) Gurdon Institute, University of Cambridge, Cambridge, UK.|Division of Molecular Oncology and Immunology, Netherlands Cancer Institute, Plesmanlaan 121, 1066, CX, Amsterdam, The Netherlands.|Central Genomic Facility, Netherlands Cancer Institute, Amsterdam, The Netherlands.|Division of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands.|Division of Experimental Animal Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands.|Division of Experimental Animal Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands.|Cancer Genome Project, The Wellcome Trust Sanger Institute, Hinxton, Cambridge, CB10 1SA, UK.|Division of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands.|Central Genomic Facility, Netherlands Cancer Institute, Amsterdam, The Netherlands.|Division of Molecular Carcinogenesis, Netherlands Cancer Institute, Amsterdam, The Netherlands.|Division of Molecular Oncology and Immunology, Netherlands Cancer Institute, Plesmanlaan 121, 1066, CX, Amsterdam, The Netherlands. d.peeper@nki.nl.|Division of Molecular Oncology and Immunology, Netherlands Cancer Institute, Plesmanlaan 121, 1066, CX, Amsterdam, The Netherlands. o.krijgsman@nki.nl.', 'Netherlands|Netherlands|Netherlands|Netherlands|United Kingdom|United Kingdom|Netherlands|Netherlands|Netherlands|Netherlands|', 9, 'Animals|Computers|Databases, Genetic|High-Throughput Nucleotide Sequencing|Humans|Mice', 'Journal Article', '10.1186/s12859-018-2353-5', '30286710', 'Mouse xenografts from (patient-derived) tumors (PDX) or tumor cell lines are widely used as models to study various biological and preclinical aspects of cancer. However, analyses of their RNA and DNA profiles are challenging, because they comprise reads not only from the grafted human cancer but also from the murine host. The reads of murine origin result in false positives in mutation analysis of DNA samples and obscure gene expression levels when sequencing RNA. However, currently available algorithms are limited and improvements in accuracy and ease of use are necessary.'),
(98547, 2018, 'bmc', 'GPrank: an R package for detecting dynamic elements from genome-wide time series', 'Topa, Hande|Honkela, Antti', 'Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, 00014, Finland. hande.topa@helsinki.fi.|Helsinki Institute for Information Technology HIIT, Department of Mathematics and Statistics, University of Helsinki, Helsinki, 00014, Finland.', 'Finland|Finland', 0, 'Genetic Variation|Genome|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-018-2370-4', '30286713', 'Genome-wide high-throughput sequencing (HTS) time series experiments are a powerful tool for monitoring various genomic elements over time. They can be used to monitor, for example, gene or transcript expression with RNA sequencing (RNA-seq), DNA methylation levels with bisulfite sequencing (BS-seq), or abundances of genetic variants in populations with pooled sequencing (Pool-seq). However, because of high experimental costs, the time series data sets often consist of a very limited number of time points with very few or no biological replicates, posing challenges in the data analysis.'),
(98548, 2018, 'bmc', 'Reflections on a journey: a retrospective of the ISCB Student Council symposium series', 'Hassan, Mehedi|Namasivayam, Aishwarya Alex|DeBlasio, Dan|Fatima, Nazeefa|Siranosian, Benjamin|Parra, R Gonzalo|Cuypers, Bart|Shome, Sayane|Monzon, Alexander Miguel|Fumey, Julien|Rahman, Farzana', 'Genomics and Computational Biology Research Group, Faculty of Computing, Engineering and Science, University of South Wales, Cardiff, UK.|Luxembourg Centre for Systems Biomedicine, Université du Luxembourg, Belvaux, Luxembourg.|Computational Biology Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.|Department of Biology, Faculty of Science, Lund University, Lund, Sweden.|Department of Genetics, Stanford University, Stanford, California, USA.|European Molecular Biology Laboratory, Heidelberg, Germany.|Institute of Tropical Medicine and the University of Antwerp, Antwerp, Belgium.|Bioinformatics and Computational Biology Program, Iowa State University, Ames, Iowa, USA.|Structural Bioinformatics Group, Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes, Buenos Aires, Argentina.|Human Genetics and Cognitive Function, Institut Pasteur, Paris, France.|Genomics and Computational Biology Research Group, Faculty of Computing, Engineering and Science, University of South Wales, Cardiff, UK. frahman@iscb.org.', 'United Kingdom|Luxembourg|United Kingdom|Sweden|United Kingdom|Germany|Belgium|United Kingdom|Argentina|France|United Kingdom', 0, 'Computational Biology|Congresses as Topic|Fellowships and Scholarships|Humans|Peer Review, Research|Publications|Research Support as Topic|Students', 'Journal Article', '10.1186/s12859-018-2369-x', '30301451', 'This article describes the motivation, origin and evolution of the student symposia series organised by the ISCB Student Council. The meeting series started thirteen years ago in Madrid and has spread to four continents. The article concludes with the highlights of the most recent edition of annual Student Council Symposium held in conjunction with the 25th Conference on Intelligent Systems for Molecular Biology and the 16th European Conference on Computational Biology, in Prague, in July 2017.'),
(98549, 2018, 'bmc', 'Genome-wide analysis of fitness data and its application to improve metabolic models', 'Vitkin, Edward|Solomon, Oz|Sultan, Sharon|Yakhini, Zohar', 'Department of Computer Science, Technion, Haifa, Israel.|Faculty of Biotechnology and Food Engineering, Technion, Haifa, Israel. oz.solomon@idc.ac.il.|School of Computer Science, The Interdisciplinary Center, Herzliya, Israel.|Department of Computer Science, Technion, Haifa, Israel. zohar.yakhini@idc.ac.il.', 'Israel|Israel|Israel|Israel', 0, 'Exercise Test|Genome-Wide Association Study|Genomics|Humans|Models, Biological', 'Journal Article', '10.1186/s12859-018-2341-9', '30305012', 'Synthetic biology and related techniques enable genome scale high-throughput investigation of the effect on organism fitness of different gene knock-downs/outs and of other modifications of genomic sequence.'),
(98550, 2018, 'bmc', 'Data integration by multi-tuning parameter elastic net regression', 'Liu, Jie|Liang, Gangning|Siegmund, Kimberly D|Lewinger, Juan Pablo', 'Department of Preventive Medicine, USC Keck School of Medicine, 2001 N Soto Street, Los Angeles, CA, 90089, USA. liu485@usc.edu.|USC Institute of Urology and the Catherine & Joseph Aresty Department of Urology, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, 90089, USA.|Department of Preventive Medicine, USC Keck School of Medicine, 2001 N Soto Street, Los Angeles, CA, 90089, USA.|Department of Preventive Medicine, USC Keck School of Medicine, 2001 N Soto Street, Los Angeles, CA, 90089, USA.', 'United States|United States|United States|United States', 3, 'Data Analysis|Genomics|Humans|Logistic Models|Software', 'Journal Article', '10.1186/s12859-018-2401-1', '30305021', 'To integrate molecular features from multiple high-throughput platforms in prediction, a regression model that penalizes features from all platforms equally is commonly used. However, data from different platforms are likely to differ in effect sizes, the proportion of predictive features, and correlations structures. Subtle but important features may be missed by shrinking all features equally.'),
(98551, 2018, 'bmc', 'Integrating omics datasets with the OmicsPLS package', 'Bouhaddani, Said El|Uh, Hae-Won|Jongbloed, Geurt|Hayward, Caroline|Klarixc4x87, Lucija|Kiexc5x82basa, Szymon M|Houwing-Duistermaat, Jeanine', 'Dept. of Biomedical Data Sciences, LUMC, Albinusdreef 2, Leiden, 2300 RC, The Netherlands. s.el_bouhaddani@lumc.nl.|Department of Biostatistics and Research Support, UMC Utrecht, div. Julius Centre, Huispost Str. 6.131, Utrecht, 3508 GA, The Netherlands.|Delft Institute of Applied Mathematics, EEMCS, TU Delft, Van Mourik Broekmanweg 6, Delft, 2628 XE, The Netherlands.|MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, Scotland.|Genos Glycobiology Laboratory, Zagreb, 10000, Croatia.|Dept. of Biomedical Data Sciences, LUMC, Albinusdreef 2, Leiden, 2300 RC, The Netherlands.|Dept. of Statistics, University of Leeds, Leeds, LS2 9JT, United Kingdom.', 'Netherlands|Netherlands|Netherlands|-|Croatia|Netherlands|United Kingdom', 10, 'Genomics|Humans|Least-Squares Analysis|Metabolomics|Software', 'Journal Article', '10.1186/s12859-018-2371-3', '30309317', 'With the exponential growth in available biomedical data, there is a need for data integration methods that can extract information about relationships between the data sets. However, these data sets might have very different characteristics. For interpretable results, data-specific variation needs to be quantified. For this task, Two-way Orthogonal Partial Least Squares (O2PLS) has been proposed. To facilitate application and development of the methodology, free and open-source software is required. However, this is not the case with O2PLS.'),
(98552, 2018, 'bmc', 'Accurate prediction of protein-lncRNA interactions by diffusion and HeteSim features across heterogeneous network', 'Deng, Lei|Wang, Junqiang|Xiao, Yun|Wang, Zixiang|Liu, Hui', 'School of Software, Central South University, Changsha, 410075, China.|School of Software, Central South University, Changsha, 410075, China.|School of Software, Central South University, Changsha, 410075, China.|School of Software, Central South University, Changsha, 410075, China.|Lab of Information Management, Changzhou University, Jiangsu, 213164, China. hliu@cczu.edu.cn.', 'China|China|China|China|China', 9, 'Computer Simulation|Databases, Genetic|Neural Networks (Computer)|RNA, Long Noncoding|RNA-Binding Proteins', 'Journal Article', '10.1186/s12859-018-2390-0', '30309340', 'Identifying the interactions between proteins and long non-coding RNAs (lncRNAs) is of great importance to decipher the functional mechanisms of lncRNAs. However, current experimental techniques for detection of lncRNA-protein interactions are limited and inefficient. Many methods have been proposed to predict protein-lncRNA interactions, but few studies make use of the topological information of heterogenous biological networks associated with the lncRNAs.'),
(98553, 2018, 'bmc', 'Dense neural networks for predicting chromatin conformation', 'Farré, Pau|Heurteau, Alexandre|Cuvier, Olivier|Emberly, Eldon', 'Department of Physics, Simon Fraser University, 8888 University Dr., Burnaby, Canada.|Laboratoire de Biologie Moléculaire des Eucaryotes (LBME), CNRS, Bâtiment IBCG, Toulouse, 31062, France.|Laboratoire de Biologie Moléculaire des Eucaryotes (LBME), CNRS, Bâtiment IBCG, Toulouse, 31062, France.|Department of Physics, Simon Fraser University, 8888 University Dr., Burnaby, Canada. eemberly@sfu.ca.', 'Canada|France|France|Canada', 0, 'Chromatin|Molecular Conformation|Neural Networks (Computer)', 'Journal Article', '10.1186/s12859-018-2286-z', '30314429', 'DNA inside eukaryotic cells wraps around histones to form the 11nm chromatin fiber that can further fold into higher-order DNA loops, which may depend on the binding of architectural factors. Predicting how the DNA will fold given a distribution of bound factors, here viewed as a type of sequence, is currently an unsolved problem and several heterogeneous polymer models have shown that many features of the measured structure can be reproduced from simulations. However a model that determines the optimal connection between sequence and structure and that can rapidly assess the effects of varying either one is still lacking.'),
(98554, 2018, 'bmc', 'Predicting variant deleteriousness in non-human species: applying the CADD approach in mouse', 'Groß, Christian|de Ridder, Dick|Reinders, Marcel', 'Delft Bioinformatics Lab, University of Technology Delft, Van Mourik Broekmanweg 6, Delft, 2600GA, The Netherlands.|Bioinformatics Group, Wageningen University & Research, Wageningen, 6708 PB, The Netherlands.|Delft Bioinformatics Lab, University of Technology Delft, Van Mourik Broekmanweg 6, Delft, 2600GA, The Netherlands. M.J.T.Reinders@tudelft.nl.', 'Netherlands|Netherlands|Netherlands', 5, 'Animals|Genetic Variation|Genomics|Humans|Mice|Molecular Sequence Annotation', 'Journal Article', '10.1186/s12859-018-2337-5', '30314430', 'Predicting the deleteriousness of observed genomic variants has taken a step forward with the introduction of the Combined Annotation Dependent Depletion (CADD) approach, which trains a classifier on the wealth of available human genomic information. This raises the question whether it can be done with less data for non-human species. Here, we investigate the prerequisites to construct a CADD-based model for a non-human species.'),
(98555, 2018, 'bmc', 'Finding the mean in a partition distribution', 'Glassen, Thomas J|Oertzen, Timo von|Konovalov, Dmitry A', 'Department of Psychology, Universität der Bundeswehr München, Werner-Heisenberg-Weg 39, Neubiberg, 85577, Germany.|Department of Psychology, Universität der Bundeswehr München, Werner-Heisenberg-Weg 39, Neubiberg, 85577, Germany. timo.vonoertzen@unibw.de.|School of Information Technology, James Cook University, 1 James Cook Drive, Townsville, QLD 4811, Australia.', 'Germany|Germany|Australia', 0, 'Algorithms|Bayes Theorem|Humans', 'Journal Article', '10.1186/s12859-018-2359-z', '30314432', 'Bayesian clustering algorithms, in particular those utilizing Dirichlet Processes (DP), return a sample of the posterior distribution of partitions of a set. However, in many applied cases a single clustering solution is desired, requiring a best partition to be created from the posterior sample. It is an open research question which solution should be recommended in which situation. However, one such candidate is the sample mean, defined as the clustering with minimal squared distance to all partitions in the posterior sample, weighted by their probability. In this article, we review an algorithm that approximates this sample mean by using the Hungarian Method to compute the distance between partitions. This algorithm leaves room for further processing acceleration.'),
(98556, 2018, 'bmc', 'PQ, a new program for phylogeny reconstruction', 'Penzar, Dmitry|Krivozubov, Mikhail|Spirin, Sergey', 'Faculty of Bioengineering and Bioinformatics, Moscow State University, 1 Leninskiye Gory, bld. 73, Moscow, 119991, Russia.|Gamaleya Center of Epidemiology and Microbiology, 18 Gamaleya st., Moscow, 123098, Russia.|Faculty of Bioengineering and Bioinformatics, Moscow State University, 1 Leninskiye Gory, bld. 73, Moscow, 119991, Russia. sas@belozersky.msu.ru.', '-|-|-', 0, 'Algorithms|Phylogeny|Software', 'Journal Article', '10.1186/s12859-018-2399-4', '30314446', 'Many algorithms and programs are available for phylogenetic reconstruction of families of proteins. Methods used widely at present use either a number of distance-based principles or character-based principles of maximum parsimony or maximum likelihood.'),
(98557, 2018, 'bmc', 'Improving network inference algorithms using resampling methods', 'Colby, Sean M|McClure, Ryan S|Overall, Christopher C|Renslow, Ryan S|McDermott, Jason E', 'Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA.|Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA.|Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA.|Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA.|Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA. jason.mcdermott@pnnl.gov.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Gene Expression|Gene Regulatory Networks|Humans', 'Journal Article', '10.1186/s12859-018-2402-0', '30314469', 'Relatively small changes to gene expression data dramatically affect co-expression networks inferred from that data which, in turn, can significantly alter the subsequent biological interpretation. This error propagation is an underappreciated problem that, while hinted at in the literature, has not yet been thoroughly explored. Resampling methods (e.g. bootstrap aggregation, random subspace method) are hypothesized to alleviate variability in network inference methods by minimizing outlier effects and distilling persistent associations in the data. But the efficacy of the approach assumes the generalization from statistical theory holds true in biological network inference applications.'),
(98558, 2018, 'bmc', 'Trendy: segmented regression analysis of expression dynamics in high-throughput ordered profiling experiments', 'Bacher, Rhonda|Leng, Ning|Chu, Li-Fang|Ni, Zijian|Thomson, James A|Kendziorski, Christina|Stewart, Ron', 'Department of Biostatistics, University of Florida, Gainesville, FL, USA. rbacher@ufl.edu.|Morgridge Institute for Research, Madison, WI, USA.|Morgridge Institute for Research, Madison, WI, USA.|Department of Statistics, University of Wisconsin-Madison, Madison, WI, USA.|Morgridge Institute for Research, Madison, WI, USA.|Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA.|Morgridge Institute for Research, Madison, WI, USA. RStewart@morgridge.org.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Gene Expression Profiling|High-Throughput Screening Assays|Humans|Software', 'Journal Article', '10.1186/s12859-018-2405-x', '30326833', 'High-throughput expression profiling experiments with ordered conditions (e.g. time-course or spatial-course) are becoming more common for studying detailed differentiation processes or spatial patterns. Identifying dynamic changes at both the individual gene and whole transcriptome level can provide important insights about genes, pathways, and critical time points.'),
(98559, 2018, 'bmc', 'Discovering the Dark matters in expression data of miRNA based on the miRNA-mRNA and miRNA-lncRNA networks', 'Pian, Cong|Zhang, Guangle|Wu, Sanling|Li, Fei', 'Ministry of Agriculture Key Lab of Molecular Biology of Crop Pathogens and Insects, Institute of Insect Science, Zhejiang University, Hangzhou, 310058, Zhejiang, China.|Department of Mathematics, College of Science, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, China.|Analysis Center of Agrobiology and Environmental Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China.|Ministry of Agriculture Key Lab of Molecular Biology of Crop Pathogens and Insects, Institute of Insect Science, Zhejiang University, Hangzhou, 310058, Zhejiang, China. lifei18@zju.edu.cn.', 'China|China|China|China', 0, 'Gene Regulatory Networks|Humans|MicroRNAs|RNA, Long Noncoding', 'Journal Article', '10.1186/s12859-018-2410-0', '30326837', 'Since miRNAs can play important roles in different cancer types, how to discover cancer related miRNAs is an important issue. In general, the miRNAs with differential expression is the focus of attention. However, some important cancer related miRNAs are not excavated by differential expression analysis. We take this type of miRNAs as dark matters (DM-miRNA). It is our great interests to develop an algorithm to discover DM-miRNAs.'),
(98560, 2018, 'bmc', 'Application of fourier transform and proteochemometrics principles to protein engineering', 'Cadet, Frédéric|Fontaine, Nicolas|Vetrivel, Iyanar|Ng Fuk Chong, Matthieu|Savriama, Olivier|Cadet, Xavier|Charton, Philippe', 'Peaccel SAS, Protein Engineering ACCELerator, n°6 Square Albin Cachot, Box 42, 75013, Paris, France. frederic.cadet@peaccel.com.|Peaccel SAS, Protein Engineering ACCELerator, n°6 Square Albin Cachot, Box 42, 75013, Paris, France.|Peaccel SAS, Protein Engineering ACCELerator, n°6 Square Albin Cachot, Box 42, 75013, Paris, France.|Peaccel SAS, Protein Engineering ACCELerator, n°6 Square Albin Cachot, Box 42, 75013, Paris, France.|Peaccel SAS, Protein Engineering ACCELerator, n°6 Square Albin Cachot, Box 42, 75013, Paris, France.|Peaccel SAS, Protein Engineering ACCELerator, n°6 Square Albin Cachot, Box 42, 75013, Paris, France.|Peaccel SAS, Protein Engineering ACCELerator, n°6 Square Albin Cachot, Box 42, 75013, Paris, France.', 'France|France|France|France|France|France|France', 0, 'Fourier Analysis|Humans|Protein Engineering|Proteins', 'Journal Article', '10.1186/s12859-018-2407-8', '30326841', 'Connecting the dots between the protein sequence and its function is of fundamental interest for protein engineers. In-silico methods are useful in this quest especially when structural information is not available. In this study we propose a mutant library screening tool called iSAR (innovative Sequence Activity Relationship) that relies on the physicochemical properties of the amino acids, digital signal processing and partial least squares regression to uncover these sequence-function correlations.'),
(98561, 2018, 'bmc', 'DeviCNV: detection and visualization of exon-level copy number variants in targeted next-generation sequencing data', 'Kang, Yeeok|Nam, Seong-Hyeuk|Park, Kyung Sun|Kim, Yoonjung|Kim, Jong-Won|Lee, Eunjung|Ko, Jung Min|Lee, Kyung-A|Park, Inho', 'SD Genomics Co., Ltd., 11F, Seoul Gangnam Post Office, 619 Gaepo-ro, Gangnam-gu, Seoul, 06336, Republic of Korea.|SD Genomics Co., Ltd., 11F, Seoul Gangnam Post Office, 619 Gaepo-ro, Gangnam-gu, Seoul, 06336, Republic of Korea.|SD Genomics Co., Ltd., 11F, Seoul Gangnam Post Office, 619 Gaepo-ro, Gangnam-gu, Seoul, 06336, Republic of Korea.|Department of Laboratory Medicine, Yonsei University College of Medicine, 211 Eonjuro, Gangnam-gu, Seoul, 06273, Republic of Korea.|Department of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.|Division of Genetics and Genomics, Boston Childrens Hospital and Harvard Medical School, Boston, USA.|Department of Pediatrics, Seoul National University Childrens Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea.|Department of Laboratory Medicine, Yonsei University College of Medicine, 211 Eonjuro, Gangnam-gu, Seoul, 06273, Republic of Korea. KAL1119@yuhs.ac.|SD Genomics Co., Ltd., 11F, Seoul Gangnam Post Office, 619 Gaepo-ro, Gangnam-gu, Seoul, 06336, Republic of Korea. ihpark@sdgenomics.com.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 2, 'DNA Copy Number Variations|Exons|Genomics|High-Throughput Nucleotide Sequencing|Humans', 'Journal Article', '10.1186/s12859-018-2409-6', '30326846', 'Targeted next-generation sequencing (NGS) is increasingly being adopted in clinical laboratories for genomic diagnostic tests.'),
(98562, 2018, 'bmc', 'Predicting bacterial resistance from whole-genome sequences using k-mers and stability selection', 'Mahé, Pierre|Tournoud, Maud', 'bioMérieux, Chemin de lOrme, Marcy lEtoile, 69280, France. pierre.mahe@biomerieux.com.|bioMérieux, Chemin de lOrme, Marcy lEtoile, 69280, France.', 'France|France', 9, 'Algorithms|Anti-Bacterial Agents|Base Sequence|Databases, Genetic|Drug Resistance, Bacterial|Genome, Bacterial|Logistic Models|Models, Genetic|Mycobacterium tuberculosis|ROC Curve|Reproducibility of Results|Staphylococcus aureus|Whole Genome Sequencing', 'Journal Article', '10.1186/s12859-018-2403-z', '30332990', 'Several studies demonstrated the feasibility of predicting bacterial antibiotic resistance phenotypes from whole-genome sequences, the prediction process usually amounting to detecting the presence of genes involved in antibiotic resistance mechanisms, or of specific mutations, previously identified from a training panel of strains, within these genes. We address the problem from the supervised statistical learning perspective, not relying on prior information about such resistance factors. We rely on a k-mer based genotyping scheme and a logistic regression model, thereby combining several k-mers into a probabilistic model. To identify a small yet predictive set of k-mers, we rely on the stability selection approach (Meinshausen et al., J R Stat Soc Ser B 72:417-73, 2010), that consists in penalizing logistic regression models with a Lasso penalty, coupled with extensive resampling procedures.'),
(98563, 2018, 'bmc', 'Using set theory to reduce redundancy in pathway sets', 'Stoney, Ruth Alexandra|Schwartz, Jean-Marc|Robertson, David L|Nenadic, Goran', 'School of Computer Science, University of Manchester, Manchester, M13 9PL, UK. ruth.stoney@manchester.ac.uk.|School of Biological Sciences, University of Manchester, Manchester, M13 9PT, UK.|School of Biological Sciences, University of Manchester, Manchester, M13 9PT, UK.|School of Computer Science, University of Manchester, Manchester, M13 9PL, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Databases, Genetic|Gene Expression Profiling|Humans|Signal Transduction', 'Journal Article', '10.1186/s12859-018-2355-3', '30340461', 'The consolidation of pathway databases, such as KEGG, Reactome and ConsensusPathDB, has generated widespread biological interest, however the issue of pathway redundancy impedes the use of these consolidated datasets. Attempts to reduce this redundancy have focused on visualizing pathway overlap or merging pathways, but the resulting pathways may be of heterogeneous sizes and cover multiple biological functions. Efforts have also been made to deal with redundancy in pathway data by consolidating enriched pathways into a number of clusters or concepts. We present an alternative approach, which generates pathway subsets capable of covering all of genes presented within either pathway databases or enrichment results, generating substantial reductions in redundancy.'),
(98564, 2018, 'bmc', 'TNER: a novel background error suppression method for mutation detection in circulating tumor DNA', 'Deng, Shibing|Lira, Maruja|Huang, Donghui|Wang, Kai|Valdez, Crystal|Kinong, Jennifer|Rejto, Paul A|Bienkowska, Jadwiga|Hardwick, James|Xie, Tao', 'Pfizer Early Clinical Development Biostatistics, Cambridge, UK.|Pfizer Oncology R & D, San Diego, CA, USA.|Pfizer Oncology R & D, San Diego, CA, USA.|Pfizer Oncology R & D, San Diego, CA, USA.|Pfizer Oncology R & D, San Diego, CA, USA.|Pfizer Oncology R & D, San Diego, CA, USA.|Pfizer Oncology R & D, San Diego, CA, USA.|Pfizer Oncology R & D, San Diego, CA, USA.|Pfizer Oncology R & D, San Diego, CA, USA.|Pfizer Oncology R & D, San Diego, CA, USA. xietao2000@gmail.com.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Circulating Tumor DNA|Computer Simulation|DNA Mutational Analysis|High-Throughput Nucleotide Sequencing|Humans|Mutation|Neoplasms|Normal Distribution|ROC Curve|Software', 'Journal Article', '10.1186/s12859-018-2428-3', '30342468', 'Ultra-deep next-generation sequencing of circulating tumor DNA (ctDNA) holds great promise as a tool for the early detection of cancer and for monitoring disease progression and therapeutic responses. However, the low abundance of ctDNA in the bloodstream coupled with technical errors introduced during library construction and sequencing complicates mutation detection.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(98565, 2018, 'bmc', 'A new cross-platform architecture for epi-info software suite', 'Camp, Blake|Mandivarapu, Jaya Krishna|Ramamurthy, Nagashayan|Wingo, James|Bourgeois, Anu G|Cao, Xiaojun|Sunderraman, Rajshekhar', 'Department of Computer Science, Georgia State University, 25 Park Place, Atlanta, GA, USA. bcamp2@gsu.edu.|Department of Computer Science, Georgia State University, 25 Park Place, Atlanta, GA, USA.|Department of Computer Science, Georgia State University, 25 Park Place, Atlanta, GA, USA.|Department of Computer Science, Georgia State University, 25 Park Place, Atlanta, GA, USA.|Department of Computer Science, Georgia State University, 25 Park Place, Atlanta, GA, USA.|Department of Computer Science, Georgia State University, 25 Park Place, Atlanta, GA, USA.|Department of Computer Science, Georgia State University, 25 Park Place, Atlanta, GA, USA.', 'Georgia|Georgia|Georgia|Georgia|Georgia|Georgia|Georgia', 0, 'Databases, Factual|Disease Outbreaks|Epidemiologic Studies|Humans|Public Health|Software|User-Computer Interface|Workflow', 'Journal Article', '10.1186/s12859-018-2334-8', '30343662', 'The Epi-Info software suite, built and maintained by the Centers for Disease Control and Prevention (CDC), is widely used by epidemiologists and public health researchers to collect and analyze public health data, especially in the event of outbreaks such as Ebola and Zika. As it exists today, Epi-Info Desktop runs only on the Windows platform, and the larger Epi-Info Suite of products consists of separate codebases for several different devices and use-cases. Software portability has become increasingly important over the past few years as it offers a number of obvious benefits. These include reduced development time, reduced cost, and simplified system architecture. Thus, there is a blatant need for continued research. Specifically, it is critical to fully understand any underlying negative performance issues which arise from platform-agnostic systems. Such understanding should allow for improved design, and thus result in substantial mitigation of reduced performance. In this paper, we present a viable cross-platform architecture for Epi-Info which solves many of these problems.'),
(98566, 2018, 'bmc', 'outbreaker2: a modular platform for outbreak reconstruction', 'Campbell, Finlay|Didelot, Xavier|Fitzjohn, Rich|Ferguson, Neil|Cori, Anne|Jombart, Thibaut', 'MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK.|MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK.|MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK.|MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK.|MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK.|MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK. thibautjombart@gmail.com.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 10, 'Algorithms|Biological Evolution|Disease Outbreaks|Ebolavirus|Hemorrhagic Fever, Ebola|Humans|Markov Chains|Models, Theoretical|Monte Carlo Method|Software', 'Journal Article', '10.1186/s12859-018-2330-z', '30343663', 'Reconstructing individual transmission events in an infectious disease outbreak can provide valuable information and help inform infection control policy. Recent years have seen considerable progress in the development of methodologies for reconstructing transmission chains using both epidemiological and genetic data. However, only a few of these methods have been implemented in software packages, and with little consideration for customisability and interoperability. Users are therefore limited to a small number of alternatives, incompatible tools with fixed functionality, or forced to develop their own algorithms at considerable personal effort.'),
(98567, 2018, 'bmc', 'Analysis of drug resistance in HIV protease', 'Pawar, Shrikant D|Freas, Christopher|Weber, Irene T|Harrison, Robert W', 'Department of Computer Science, 25 Park Place, Atlanta, GA 30303, USA.|Department of Computer Science, 25 Park Place, Atlanta, GA 30303, USA.|Department of Biology, 100 Piedmont Ave., Atlanta, GA 30303, USA.|Department of Computer Science, 25 Park Place, Atlanta, GA 30303, USA. rwh@gsu.edu.', 'United States|United States|United States|United States', 1, 'Algorithms|Databases as Topic|Drug Resistance, Viral|Genotype|HIV Infections|HIV Protease|HIV Protease Inhibitors|Humans|Machine Learning|Principal Component Analysis', 'Journal Article', '10.1186/s12859-018-2331-y', '30343664', 'Drug resistance in HIV is the major problem limiting effective antiviral therapy. Computational techniques for predicting drug resistance profiles from genomic data can accelerate the appropriate choice of therapy. These techniques can also be used to select protease mutants for experimental studies of resistance and thereby assist in the development of next-generation therapies.'),
(98568, 2018, 'bmc', 'Noise cancellation using total variation for copy number variation detection', 'Zare, Fatima|Hosny, Abdelrahman|Nabavi, Sheida', 'Computer Science and Engineering Department, University of Connecticut, Storrs, CT, USA. fatemeh.zare@uconn.edu.|Computer Science and Engineering Department, University of Connecticut, Storrs, CT, USA.|Computer Science and Engineering Department and Institute for Systems Genomics, University of Connecticut, Storrs, CT, USA.', 'United States|United States|United States', 3, 'Algorithms|Breast Neoplasms|Computer Simulation|DNA Copy Number Variations|Female|Genomics|High-Throughput Nucleotide Sequencing|Humans|Signal Processing, Computer-Assisted|Time Factors|Wavelet Analysis', 'Journal Article', '10.1186/s12859-018-2332-x', '30343665', 'Due to recent advances in sequencing technologies, sequence-based analysis has been widely applied to detecting copy number variations (CNVs). There are several techniques for identifying CNVs using next generation sequencing (NGS) data, however methods employing depth of coverage or read depth (RD) have recently become a main technique to identify CNVs. The main assumption of the RD-based CNV detection methods is that the readcount value at a specific genomic location is correlated with the copy number at that location. However, readcount datas noise and biases distort the association between the readcounts and copy numbers. For more accurate CNV identification, these biases and noise need to be mitigated. In this work, to detect CNVs more precisely and efficiently we propose a novel denoising method based on the total variation approach and the Taut String algorithm.'),
(98569, 2018, 'bmc', 'Fast estimation of genetic relatedness between members of heterogeneous populations of closely related genomic variants', 'Tsyvina, Viachaslau|Campo, David S|Sims, Seth|Zelikovsky, Alex|Khudyakov, Yury|Skums, Pavel', 'Computer Science Department, Georgia State University, 25 Park Place NE, Atlanta, 30303, GA, USA. vtsyvina1@student.gsu.edu.|Molecular Epidemiology and Bioinformatics Laboratory, Division of Viral Hepatitis, Centers for Disease Control and Prevention, 1600 Cliffton Road, Atlanta, 30333, GA, USA.|Computer Science Department, Georgia State University, 25 Park Place NE, Atlanta, 30303, GA, USA.|Computer Science Department, Georgia State University, 25 Park Place NE, Atlanta, 30303, GA, USA.|Molecular Epidemiology and Bioinformatics Laboratory, Division of Viral Hepatitis, Centers for Disease Control and Prevention, 1600 Cliffton Road, Atlanta, 30333, GA, USA.|Computer Science Department, Georgia State University, 25 Park Place NE, Atlanta, 30303, GA, USA.', 'Georgia|United States|Georgia|Georgia|United States|Georgia', 0, 'Algorithms|Base Sequence|Entropy|Genetic Variation|Genome|High-Throughput Nucleotide Sequencing|Humans|Metagenomics|Phylogeny|Reproducibility of Results|Time Factors', 'Journal Article', '10.1186/s12859-018-2333-9', '30343669', 'Many biological analysis tasks require extraction of families of genetically similar sequences from large datasets produced by Next-generation Sequencing (NGS). Such tasks include detection of viral transmissions by analysis of all genetically close pairs of sequences from viral datasets sampled from infected individuals or studying of evolution of viruses or immune repertoires by analysis of network of intra-host viral variants or antibody clonotypes formed by genetically close sequences. The most obvious naïeve algorithms to extract such sequence families are impractical in light of the massive size of modern NGS datasets.'),
(98570, 2018, 'bmc', 'PoSE: visualization of patterns of sequence evolution using PAML and MATLAB', 'Zhao, Kun|Henderson, Elizabeth|Bullard, Kelley|Oberste, M Steven|Burns, Cara C|Jorba, Jaume', 'Polio and Picornavirus Laboratory Branch, G-10, Division of Viral Diseases, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, 1600 Clifton Rd., N.E, Atlanta, GA, 30329, USA. kzhao@cdc.gov.|Polio and Picornavirus Laboratory Branch, G-10, Division of Viral Diseases, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, 1600 Clifton Rd., N.E, Atlanta, GA, 30329, USA.|IHRC Inc., Atlanta, GA, USA.|Polio and Picornavirus Laboratory Branch, G-10, Division of Viral Diseases, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, 1600 Clifton Rd., N.E, Atlanta, GA, 30329, USA.|Polio and Picornavirus Laboratory Branch, G-10, Division of Viral Diseases, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, 1600 Clifton Rd., N.E, Atlanta, GA, 30329, USA.|Polio and Picornavirus Laboratory Branch, G-10, Division of Viral Diseases, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, 1600 Clifton Rd., N.E, Atlanta, GA, 30329, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Base Pairing|Base Sequence|Codon|Evolution, Molecular|Phylogeny|Poliovirus|Software', 'Journal Article', '10.1186/s12859-018-2335-7', '30343671', 'Determining patterns of nucleotide and amino acid substitution is the first step during sequence evolution analysis. However, it is not easy to visualize the different phylogenetic signatures imprinted in aligned nucleotide and amino acid sequences.'),
(98571, 2018, 'bmc', 'Automated quality control for a molecular surveillance system', 'Sims, Seth|Longmire, Atkinson G|Campo, David S|Ramachandran, Sumathi|Medrzycki, Magdalena|Ganova-Raeva, Lilia|Lin, Yulin|Sue, Amanda|Thai, Hong|Zelikovsky, Alexander|Khudyakov, Yury', 'Division of Viral Hepatitis, Centers for Disease Control and Prevention, 1600 Clifton Road, MS A33, Atlanta, GA, 30329, USA. xzy3@cdc.gov.|Division of Viral Hepatitis, Centers for Disease Control and Prevention, 1600 Clifton Road, MS A33, Atlanta, GA, 30329, USA.|Division of Viral Hepatitis, Centers for Disease Control and Prevention, 1600 Clifton Road, MS A33, Atlanta, GA, 30329, USA. fyv6@cdc.gov.|Division of Viral Hepatitis, Centers for Disease Control and Prevention, 1600 Clifton Road, MS A33, Atlanta, GA, 30329, USA.|Division of Viral Hepatitis, Centers for Disease Control and Prevention, 1600 Clifton Road, MS A33, Atlanta, GA, 30329, USA.|Division of Viral Hepatitis, Centers for Disease Control and Prevention, 1600 Clifton Road, MS A33, Atlanta, GA, 30329, USA.|Division of Viral Hepatitis, Centers for Disease Control and Prevention, 1600 Clifton Road, MS A33, Atlanta, GA, 30329, USA.|Division of Viral Hepatitis, Centers for Disease Control and Prevention, 1600 Clifton Road, MS A33, Atlanta, GA, 30329, USA.|Division of Viral Hepatitis, Centers for Disease Control and Prevention, 1600 Clifton Road, MS A33, Atlanta, GA, 30329, USA.|Department of Computer Science, Georgia State University, 1 Park Place, room 623, Atlanta, GA, 30302, USA.|Division of Viral Hepatitis, Centers for Disease Control and Prevention, 1600 Clifton Road, MS A33, Atlanta, GA, 30329, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|Georgia|United States', 0, 'Automation|Disease Outbreaks|Genotyping Techniques|Hepacivirus|Hepatitis C|Humans|Population Surveillance|Quality Control|Reference Standards|United States', 'Journal Article', '10.1186/s12859-018-2329-5', '30343674', 'Molecular surveillance and outbreak investigation are important for elimination of hepatitis C virus (HCV) infection in the United States. A web-based system, Global Hepatitis Outbreak and Surveillance Technology (GHOST), has been developed using Illumina MiSeq-based amplicon sequence data derived from the HCV E1/E2-junction genomic region to enable public health institutions to conduct cost-effective and accurate molecular surveillance, outbreak detection and strain characterization. However, as there are many factors that could impact input data quality to which the GHOST system is not completely immune, accuracy of epidemiological inferences generated by GHOST may be affected. Here, we analyze the data submitted to the GHOST system during its pilot phase to assess the nature of the data and to identify common quality concerns that can be detected and corrected automatically.'),
(98572, 2018, 'bmc', 'HaploJuice : accurate haplotype assembly from a pool of sequences with known relative concentrations', 'Wong, Thomas K F|Ranjard, Louis|Lin, Yu|Rodrigo, Allen G', 'The Research School of Biology, The Australian National University, Acton ACT, 2601, Australia. Thomas.Wong@anu.edu.au.|The Research School of Biology, The Australian National University, Acton ACT, 2601, Australia.|College of Engineering and Computer Science, The Australian National University, Acton ACT, 2601, Australia.|The Research School of Biology, The Australian National University, Acton ACT, 2601, Australia.', 'Australia|Australia|Australia|Australia', 0, 'Algorithms|Base Sequence|Computer Simulation|Databases, Genetic|Haplotypes|Humans', 'Journal Article', '10.1186/s12859-018-2424-7', '30348075', 'Pooling techniques, where multiple sub-samples are mixed in a single sample, are widely used to take full advantage of high-throughput DNA sequencing. Recently, Ranjard et al. (PLoS ONE 13:0195090, 2018) proposed a pooling strategy without the use of barcodes. Three sub-samples were mixed in different known proportions (i.e. 62.5%, 25% and 12.5%), and a method was developed to use these proportions to reconstruct the three haplotypes effectively.'),
(98573, 2018, 'bmc', '3D Network exploration and visualisation for lifespan data', 'Hühne, Rolf|Kessler, Viktor|Fürstberger, Axel|Kühlwein, Silke|Platzer, Matthias|Sühnel, Jürgen|Lausser, Ludwig|Kestler, Hans A', 'Institute of Medical Systems Biology - Ulm University, Albert-Einstein-Allee 11, Ulm, 89081, Germany.|Institute of Medical Systems Biology - Ulm University, Albert-Einstein-Allee 11, Ulm, 89081, Germany.|Institute of Medical Systems Biology - Ulm University, Albert-Einstein-Allee 11, Ulm, 89081, Germany.|Institute of Medical Systems Biology - Ulm University, Albert-Einstein-Allee 11, Ulm, 89081, Germany.|Leibniz Institute on Aging - Fritz Lipmann Institute, Beutenbergstr. 11, Jena, 07745, Germany.|Leibniz Institute on Aging - Fritz Lipmann Institute, Beutenbergstr. 11, Jena, 07745, Germany.|Institute of Medical Systems Biology - Ulm University, Albert-Einstein-Allee 11, Ulm, 89081, Germany.|Institute of Medical Systems Biology - Ulm University, Albert-Einstein-Allee 11, Ulm, 89081, Germany. hans.kestler@uni-ulm.de.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Aging|Computer Graphics|Databases, Factual|Gene Ontology|Gene Regulatory Networks|Humans|Longevity|Software|User-Computer Interface', 'Journal Article', '10.1186/s12859-018-2393-x', '30352578', 'The Ageing Factor Database AgeFactDB contains a large number of lifespan observations for ageing-related factors like genes, chemical compounds, and other factors such as dietary restriction in different organisms. These data provide quantitative information on the effect of ageing factors from genetic interventions or manipulations of lifespan. Analysis strategies beyond common static database queries are highly desirable for the inspection of complex relationships between AgeFactDB data sets. 3D visualisation can be extremely valuable for advanced data exploration.'),
(98574, 2018, 'bmc', 'wTO: an R package for computing weighted topological overlap and a consensus network with integrated visualization tool', 'Gysi, Deisy Morselli|Voigt, Andre|Fragoso, Tiago de Miranda|Almaas, Eivind|Nowick, Katja', 'Department of Computer Science, Interdisciplinary Center of Bioinformatics, University of Leipzig, Haertelstrasse 16-18, Leipzig, 04109, Germany. deisy@bioinf.uni-leipzig.de.|Department of Biotechnology, NTNU - Norwegian University of Science and Technology, Trondheim, N-7049, Norway.|Fundação Cesgranrio, Rio de Janeiro, Rio de Janeiro, 20261-903, Brazil.|Department of Biotechnology, NTNU - Norwegian University of Science and Technology, Trondheim, N-7049, Norway.|Freie Universität Berlin, Human Biology Group, Institute for Zoology, Department of Biology, Chemistry and Pharmacy, Königin-Luise-Straße 1-3, Berlin, D-14195, Germany.', 'Germany|Norway|Brazil|Norway|Germany', 6, 'Algorithms|Computational Biology|Consensus|Escherichia coli|Gene Ontology|Gene Regulatory Networks|Humans|Metabolic Networks and Pathways|Metagenomics|Oceans and Seas|ROC Curve|Software|Time Factors|Transcription Factors', 'Journal Article', '10.1186/s12859-018-2351-7', '30355288', 'Network analyses, such as of gene co-expression networks, metabolic networks and ecological networks have become a central approach for the systems-level study of biological data. Several software packages exist for generating and analyzing such networks, either from correlation scores or the absolute value of a transformed score called weighted topological overlap (wTO). However, since gene regulatory processes can up- or down-regulate genes, it is of great interest to explicitly consider both positive and negative correlations when constructing a gene co-expression network.'),
(98575, 2018, 'bmc', 'Probabilistic prioritization of candidate pathway association with pathway score', 'Lin, Shu-Ju|Lu, Tzu-Pin|Yu, Qi-You|Hsiao, Chuhsing Kate', 'Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, 10055, Taiwan.|Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, 10055, Taiwan.|Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, 10055, Taiwan.|Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, 10055, Taiwan. ckhsiao@ntu.edu.tw.', '-|-|-|-', 0, 'Bayes Theorem|Breast Neoplasms|Computer Simulation|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Genetic Association Studies|Genetic Markers|Humans|Probability|Signal Transduction', 'Journal Article', '10.1186/s12859-018-2411-z', '30355338', 'Current methods for gene-set or pathway analysis are usually designed to test the enrichment of a single gene-set. Once the analysis is carried out for each of the sets under study, a list of significant sets can be obtained. However, if one wishes to further prioritize the importance or strength of association of these sets, no such quantitative measure is available. Using the magnitude of p-value to rank the pathways may not be appropriate because p-value is not a measure for strength of significance. In addition, when testing each pathway, these analyses are often implicitly affected by the number of differentially expressed genes included in the set and/or affected by the dependence among genes.'),
(98576, 2018, 'bmc', 'BITS 2017: the annual meeting of the Italian Society of Bioinformatics', 'Armano, Giuliano|Fotia, Giorgio|Manconi, Andrea', 'Dept. of Electrical and Electronic Engineer, Univ. of Cagliari, P.zza DArmi, Cagliari, 09123, Italy.|Center for Advanced Studies, Research and Development in Sardinia, Loc. Pixina Manna, Cagliari, 09010 Pula, Italy.|National Research Council, Institute for Biomedical Technologies, Via F.lli Cervi, 93, Segrate, 20090, MI, Italy. andrea.manconi@itb.cnr.it.', 'Italy|Italy|Italy', 0, 'Computational Biology|Congresses as Topic|Humans|Italy|Periodicals as Topic', 'Journal Article', '10.1186/s12859-018-2295-y', '30367567', 'This preface introduces the content of the BioMed Central journal Supplement related to the 14th annual meeting of the Bioinformatics Italian Society, held in Cagliari, Italy, from the 5th to the 7th of July, 2017.'),
(98577, 2018, 'bmc', 'Multiple model species selection for transcriptomics analysis of non-model organisms', 'Pai, Tun-Wen|Li, Kuan-Hung|Yang, Cing-Han|Hu, Chin-Hwa|Lin, Han-Jia|Wang, Wen-Der|Chen, Yet-Ran', 'Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan. twp@mail.ntou.edu.tw.|Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan.|Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan.|Department of Bioscience and Biotechnology, National Taiwan Ocean University, Keelung, Taiwan.|Department of Bioscience and Biotechnology, National Taiwan Ocean University, Keelung, Taiwan.|Department of Bioagricultural Science, National Chiayi University, Chiayi, Taiwan.|Agricultural Biotechnology Research Center, Academia Sinica, Taipei, Taiwan.', '-|-|-|-|-|-|-', 0, 'Animals|Bacteria|Computational Biology|Gene Expression Profiling|Gene Ontology|Genome|Genomics|High-Throughput Nucleotide Sequencing|Humans|Models, Biological|Molecular Sequence Annotation|Plants|Reference Standards|Species Specificity|Transcriptome', 'Journal Article', '10.1186/s12859-018-2278-z', '30367568', 'Transcriptomic sequencing (RNA-seq) related applications allow for rapid explorations due to their high-throughput and relatively fast experimental capabilities, providing unprecedented progress in gene functional annotation, gene regulation analysis, and environmental factor verification. However, with increasing amounts of sequenced reads and reference model species, the selection of appropriate reference species for gene annotation has become a new challenge.'),
(98578, 2018, 'bmc', 'Biomedical event extraction based on GRU integrating attention mechanism', 'Li, Lishuang|Wan, Jia|Zheng, Jieqiong|Wang, Jian', 'School of Computer Science and Technology, Dalian University of Technology, Dalian, China. lilishuang314@163.com.|School of Computer Science and Technology, Dalian University of Technology, Dalian, China.|School of Computer Science and Technology, Dalian University of Technology, Dalian, China.|School of Computer Science and Technology, Dalian University of Technology, Dalian, China.', 'China|China|China|China', 8, 'Attention|Bacteria|Data Mining|Environmental Microbiology|Genes, Bacterial|Humans|Natural Language Processing|Publications', 'Journal Article', '10.1186/s12859-018-2275-2', '30367569', 'Biomedical event extraction is a crucial task in biomedical text mining. As the primary forum for international evaluation of different biomedical event extraction technologies, BioNLP Shared Task represents a trend in biomedical text mining toward fine-grained information extraction (IE). The fourth series of BioNLP Shared Task in 2016 (BioNLP-ST16) proposed three tasks, in which the Bacteria Biotope event extraction (BB) task has been put forward in the earlier BioNLP-ST. Deep learning methods provide an effective way to automatically extract more complex features and achieve notable results in various natural language processing tasks.'),
(98579, 2018, 'bmc', 'Efficient computation of motif discovery on Intel Many Integrated Core (MIC) Architecture', 'Peng, Shaoliang|Cheng, Minxia|Huang, Kaiwen|Cui, YingBo|Zhang, Zhiqiang|Guo, Runxin|Zhang, Xiaoyu|Yang, Shunyun|Liao, Xiangke|Lu, Yutong|Zou, Quan|Shi, Benyun', 'College of Computer Science and Electronic Engineering & National Supercomputing Centre in Changsha, Hunan University, Changsha, 410082, China. pengshaoliang@nudt.edu.cn.|College of Computer Science and Electronic Engineering & National Supercomputing Centre in Changsha, Hunan University, Changsha, 410082, China.|School of Computer Science, National University of Defense Technology, Changsha, 410073, China.|School of Computer Science, National University of Defense Technology, Changsha, 410073, China.|School of Computer Science, National University of Defense Technology, Changsha, 410073, China.|School of Computer Science, National University of Defense Technology, Changsha, 410073, China.|School of Computer Science, National University of Defense Technology, Changsha, 410073, China.|School of Computer Science, National University of Defense Technology, Changsha, 410073, China.|School of Computer Science, National University of Defense Technology, Changsha, 410073, China.|National Supercomputer Center in Guangzhou, Guangzhou, 510275, China.|School of Computer Science and Technology, Tianjin University, Tianjin, 300350, China. zouquan@nclab.net.|School of Cyberspace, Hangzhou Dianzi University, Hangzhou, 310018, China. benyunshi@outlook.com.', 'China|China|China|China|China|China|China|China|China|China|China|China', 0, 'Algorithms|Computational Biology|Computer Graphics|Databases, Genetic|Humans|Internet|Nucleotide Motifs|Promoter Regions, Genetic|Regulatory Elements, Transcriptional|Software|Transcription Factors', 'Journal Article', '10.1186/s12859-018-2276-1', '30367570', 'Novel sequence motifs detection is becoming increasingly essential in computational biology. However, the high computational cost greatly constrains the efficiency of most motif discovery algorithms.'),
(98580, 2018, 'bmc', 'An infrastructure for precision medicine through analysis of big data', 'Moscatelli, Marco|Manconi, Andrea|Pessina, Mauro|Fellegara, Giovanni|Rampoldi, Stefano|Milanesi, Luciano|Casasco, Andrea|Gnocchi, Matteo', 'Institute for Biomedical Technologies - National Research Council (CNR-ITB), via F.lli Cervi 93, Segrate, 20090, MI, Italy. marco.moscatelli@itb.cnr.it.|Institute for Biomedical Technologies - National Research Council (CNR-ITB), via F.lli Cervi 93, Segrate, 20090, MI, Italy.|Centro Diagnostico Italiano, Via Simone Saint Bon 20, Milan, 20147, Italy.|Centro Diagnostico Italiano, Via Simone Saint Bon 20, Milan, 20147, Italy.|Centro Diagnostico Italiano, Via Simone Saint Bon 20, Milan, 20147, Italy.|Institute for Biomedical Technologies - National Research Council (CNR-ITB), via F.lli Cervi 93, Segrate, 20090, MI, Italy.|Centro Diagnostico Italiano, Via Simone Saint Bon 20, Milan, 20147, Italy.|Institute for Biomedical Technologies - National Research Council (CNR-ITB), via F.lli Cervi 93, Segrate, 20090, MI, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 5, 'Algorithms|Bayes Theorem|Big Data|Biopsy|Computer Simulation|Data Analysis|Humans|Machine Learning|Precision Medicine', 'Journal Article', '10.1186/s12859-018-2300-5', '30367571', 'Nowadays, the increasing availability of omics data, due to both the advancements in the acquisition of molecular biology results and in systems biology simulation technologies, provides the bases for precision medicine. Success in precision medicine depends on the access to healthcare and biomedical data. To this end, the digitization of all clinical exams and medical records is becoming a standard in hospitals. The digitization is essential to collect, share, and aggregate large volumes of heterogeneous data to support the discovery of hidden patterns with the aim to define predictive models for biomedical purposes. Patients data sharing is a critical process. In fact, it raises ethical, social, legal, and technological issues that must be properly addressed.'),
(98581, 2018, 'bmc', 'cuRnet: an R package for graph traversing on GPU', 'Bonnici, Vincenzo|Busato, Federico|Aldegheri, Stefano|Akhmedov, Murodzhon|Cascione, Luciano|Carmena, Alberto Arribas|Bertoni, Francesco|Bombieri, Nicola|Kwee, Ivo|Giugno, Rosalba', 'Department of Computer Science, University of Verona, Strada le Grazie, 15, Italy, Verona, Italy.|Department of Computer Science, University of Verona, Strada le Grazie, 15, Italy, Verona, Italy.|Department of Computer Science, University of Verona, Strada le Grazie, 15, Italy, Verona, Italy.|Institute of Oncology Research (IOR), Via Vincenzo Vela 6, Bellinzona, Switzerland.|Institute of Oncology Research (IOR), Via Vincenzo Vela 6, Bellinzona, Switzerland.|Institute of Oncology Research (IOR), Via Vincenzo Vela 6, Bellinzona, Switzerland.|Institute of Oncology Research (IOR), Via Vincenzo Vela 6, Bellinzona, Switzerland.|Department of Computer Science, University of Verona, Strada le Grazie, 15, Italy, Verona, Italy.|Institute of Oncology Research (IOR), Via Vincenzo Vela 6, Bellinzona, Switzerland.|Department of Computer Science, University of Verona, Strada le Grazie, 15, Italy, Verona, Italy. rosalba.giugno@univr.it.', 'Italy|Italy|Italy|Switzerland|Switzerland|Switzerland|Switzerland|Italy|Switzerland|Italy', 0, 'Algorithms|Computational Biology|Computer Graphics|Computing Methodologies', 'Journal Article', '10.1186/s12859-018-2310-3', '30367572', 'R has become the de-facto reference analysis environment in Bioinformatics. Plenty of tools are available as packages that extend the R functionality, and many of them target the analysis of biological networks. Several algorithms for graphs, which are the most adopted mathematical representation of networks, are well-known examples of applications that require high-performance computing, and for which classic sequential implementations are becoming inappropriate. In this context, parallel approaches targeting GPU architectures are becoming pervasive to deal with the execution time constraints. Although R packages for parallel execution on GPUs are already available, none of them provides graph algorithms.'),
(98582, 2018, 'bmc', 'A graph-based approach for proteoform identification and quantification using top-down homogeneous multiplexed tandem mass spectra', 'Zhu, Kaiyuan|Liu, Xiaowen', 'Department of Computer Science, Indiana University Bloomington, 700 N. Woodlawn Avenue, Bloomington, IN, 47408, USA.|Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, 719 Indiana Avenue, Indianapolis, IN, 46202, USA. xwliu@iupui.edu.', 'India|India', 0, 'Algorithms|Chromatography, Liquid|Histones|Humans|Molecular Weight|Protein Processing, Post-Translational|Proteome|Proteomics|Tandem Mass Spectrometry', 'Journal Article', '10.1186/s12859-018-2273-4', '30367573', 'Top-down homogeneous multiplexed tandem mass (HomMTM) spectra are generated from modified proteoforms of the same protein with different post-translational modification patterns. They are frequently observed in the analysis of ultramodified proteins, some proteoforms of which have similar molecular weights and cannot be well separated by liquid chromatography in mass spectrometry analysis.'),
(98583, 2018, 'bmc', 'CamurWeb: a classification software and a large knowledge base for gene expression data of cancer', 'Weitschek, Emanuel|Lauro, Silvia Di|Cappelli, Eleonora|Bertolazzi, Paola|Felici, Giovanni', 'Department of Engineering, Uninettuno International University, Corso Vittorio Emanuele II 39, Rome, 00186, Italy. emanuel@iasi.cnr.it.|Institute of Systems Analysis and Computer Science A. Ruberti, National Research Council, Via dei Taurini 19, Rome, 00185, Italy.|Department of Engineering, Roma Tre University, Via della Vasca Navale 79, Rome, 00146, Italy.|Institute of Systems Analysis and Computer Science A. Ruberti, National Research Council, Via dei Taurini 19, Rome, 00185, Italy.|Institute of Systems Analysis and Computer Science A. Ruberti, National Research Council, Via dei Taurini 19, Rome, 00185, Italy.', 'Italy|Italy|Italy|Italy|Italy', 0, 'Base Sequence|Gene Expression Regulation, Neoplastic|Genes, Neoplasm|Genome, Human|Humans|Knowledge Bases|Neoplasms|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/s12859-018-2299-7', '30367574', 'The high growth of Next Generation Sequencing data currently demands new knowledge extraction methods. In particular, the RNA sequencing gene expression experimental technique stands out for case-control studies on cancer, which can be addressed with supervised machine learning techniques able to extract human interpretable models composed of genes, and their relation to the investigated disease. State of the art rule-based classifiers are designed to extract a single classification model, possibly composed of few relevant genes. Conversely, we aim to create a large knowledge base composed of many rule-based models, and thus determine which genes could be potentially involved in the analyzed tumor. This comprehensive and open access knowledge base is required to disseminate novel insights about cancer.'),
(98584, 2018, 'bmc', 'Big data analysis for evaluating bioinvasion risk', 'Wang, Shengling|Wang, Chenyu|Wang, Shenling|Ma, Liran', 'College of Information Science and Technology, Beijing Normal University, Beijing, 100875, China.|College of Information Science and Technology, Beijing Normal University, Beijing, 100875, China.|College of Information Science and Technology, Beijing Normal University, Beijing, 100875, China. slwang@bnu.edu.cn.|Department of Computer Science, Texas Christian University, Fort Worth, 298850, TX, USA.', 'China|China|China|United States', 0, 'Animals|Aquatic Organisms|Data Mining|Ecosystem|Introduced Species|Models, Theoretical|Oceans and Seas|Ships|Transportation|Waste Disposal, Fluid', 'Journal Article', '10.1186/s12859-018-2272-5', '30367580', 'Global maritime trade plays an important role in the modern transportation industry. It brings significant economic profit along with bioinvasion risk. Species translocate and establish in a non-native area through ballast water and biofouling. Aiming at aquatic bioinvasion issue, people proposed various suggestions for bioinvasion management. Nonetheless, these suggestions only focus on the chance of a port been affected but ignore the ports ability to further spread the invaded species.'),
(98585, 2018, 'bmc', 'ConnectedAlign: a PPI network alignment method for identifying conserved protein complexes across multiple species', 'Gao, Jianliang|Song, Bo|Hu, Xiaohua|Yan, Fengxia|Wang, Jianxin', 'School of Information Science and Engineering, Central South University, Changsha, 410083, China. gaojianliang@csu.edu.cn.|College of Computing & Informatics, Drexel University, Philadelphia, 19104, USA.|College of Computing & Informatics, Drexel University, Philadelphia, 19104, USA.|College of Liberal Arts and Sciences, National University of Defence Technology, Changsha, 410073, China.|School of Information Science and Engineering, Central South University, Changsha, 410083, China.', 'China|United States|United States|China|China', 3, 'Algorithms|Animals|Caenorhabditis elegans|Computational Biology|Drosophila|Humans|Protein Interaction Mapping|Proteins|Saccharomyces cerevisiae|Species Specificity', 'Journal Article', '10.1186/s12859-018-2271-6', '30367584', 'In bioinformatics, network alignment algorithms have been applied to protein-protein interaction (PPI) networks to discover evolutionary conserved substructures at the system level. However, most previous methods aim to maximize the similarity of aligned proteins in pairwise networks, while concerning little about the feature of connectivity in these substructures, such as the protein complexes.'),
(98586, 2018, 'bmc', 'Arena-Idb: a platform to build human non-coding RNA interaction networks', 'Bonnici, Vincenzo|Caro, Giorgio De|Constantino, Giorgio|Liuni, Sabino|DElia, Domenica|Bombieri, Nicola|Licciulli, Flavio|Giugno, Rosalba', 'Department of Computer Science,University of Verona, Strada Le Grazie, Verona, Italy.|Institute for Biomedical Technologies, National Research Council (CNR), Bari, Italy.|Department of Computer Science,University of Verona, Strada Le Grazie, Verona, Italy.|Institute for Biomedical Technologies, National Research Council (CNR), Bari, Italy.|Institute for Biomedical Technologies, National Research Council (CNR), Bari, Italy.|Department of Computer Science,University of Verona, Strada Le Grazie, Verona, Italy.|Institute for Biomedical Technologies, National Research Council (CNR), Bari, Italy.|Department of Computer Science,University of Verona, Strada Le Grazie, Verona, Italy. rosalba.giugno@univr.it.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 6, 'Databases, Genetic|Gene Regulatory Networks|Humans|RNA, Untranslated|Software|User-Computer Interface', 'Journal Article', '10.1186/s12859-018-2298-8', '30367585', 'High throughput technologies have provided the scientific community an unprecedented opportunity for large-scale analysis of genomes. Non-coding RNAs (ncRNAs), for a long time believed to be non-functional, are emerging as one of the most important and large family of gene regulators and key elements for genome maintenance. Functional studies have been able to assign to ncRNAs a wide spectrum of functions in primary biological processes, and for this reason they are assuming a growing importance as a potential new family of cancer therapeutic targets. Nevertheless, the number of functionally characterized ncRNAs is still too poor if compared to the number of new discovered ncRNAs. Thus platforms able to merge information from available resources addressing data integration issues are necessary and still insufficient to elucidate ncRNAs biological roles.'),
(98587, 2018, 'bmc', 'Statistical modelling of CG interdistance across multiple organisms', 'A, Merlotti|Valle I, Faria do|G, Castellani|D, Remondini', 'Department of Physics and Astronomy, University of Bologna, Bologna, Italy.|Center for Complex Network Research and Physics Department, Northeastern University, Boston, MA, USA.|Department of Physics and Astronomy, University of Bologna, Bologna, Italy. gastone.castellani@unibo.it.|Department of Physics and Astronomy, University of Bologna, Bologna, Italy.', 'Italy|United States|Italy|Italy', 0, 'Animals|Chromosomes, Human|DNA Methylation|Dinucleoside Phosphates|Genome Size|Humans|Linear Models|Mammals|Models, Statistical', 'Journal Article', '10.1186/s12859-018-2303-2', '30367587', 'Statistical approaches to genetic sequences have revealed helpful to gain deeper insight into biological and structural functionalities, using ideas coming from information theory and stochastic modelling of symbolic sequences. In particular, previous analyses on CG dinucleotide position along the genome allowed to highlight its epigenetic role in DNA methylation, showing a different distribution tail as compared to other dinucleotides. In this paper we extend the analysis to the whole CG distance distribution over a selected set of higher-order organisms. Then we apply the best fitting probability density function to a large range of organisms (>4400) of different complexity (from bacteria to mammals) and we characterize some emerging global features.'),
(98588, 2018, 'bmc', 'A novel computational method for automatic segmentation, quantification and comparative analysis of immunohistochemically labeled tissue sections', 'Casiraghi, Elena|Huber, Veronica|Frasca, Marco|Cossa, Mara|Tozzi, Matteo|Rivoltini, Licia|Leone, Biagio Eugenio|Villa, Antonello|Vergani, Barbara', 'Department of Computer Science Giovanni Degli Antoni, Università degli Studi di Milano, Via Celoria 18, 20135, Milan, Italy. casiraghi@di.unimi.it.|Unit of Immunotherapy of Human Tumors, Department of Experimental Oncology and Molecular Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.|Department of Computer Science Giovanni Degli Antoni, Università degli Studi di Milano, Via Celoria 18, 20135, Milan, Italy.|Unit of Immunotherapy of Human Tumors, Department of Experimental Oncology and Molecular Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.|Department of medicine and surgery, Vascular Surgery, University of Insubria Hospital, Varese, Italy.|Unit of Immunotherapy of Human Tumors, Department of Experimental Oncology and Molecular Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.|School of Medicine and Surgery, University of Milano Bicocca, Monza, Italy.|School of Medicine and Surgery, University of Milano Bicocca, Monza, Italy.|School of Medicine and Surgery, University of Milano Bicocca, Monza, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 11, 'Algorithms|Biomarkers, Tumor|Computational Biology|Decision Trees|Humans|Image Processing, Computer-Assisted|Immunohistochemistry|Software|Staining and Labeling|Support Vector Machine', 'Comparative Study', '10.1186/s12859-018-2302-3', '30367588', 'In the clinical practice, the objective quantification of histological results is essential not only to define objective and well-established protocols for diagnosis, treatment, and assessment, but also to ameliorate disease comprehension.'),
(98589, 2018, 'bmc', 'Predicting diabetic retinopathy and identifying interpretable biomedical features using machine learning algorithms', 'Tsao, Hsin-Yi|Chan, Pei-Ying|Su, Emily Chia-Yu', 'Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, 106, Taiwan.|Department of Occupational Therapy and Healthy Aging Center, Chang Gung University, Taoyuan, 333, Taiwan.|Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, 106, Taiwan. emilysu@tmu.edu.tw.', '-|-|-', 9, 'Adult|Aged|Aged, 80 and over|Algorithms|Decision Support Systems, Clinical|Diabetes Mellitus, Type 2|Diabetic Retinopathy|Female|Humans|Machine Learning|Male|Middle Aged|Neural Networks (Computer)|ROC Curve|Risk Factors', 'Journal Article', '10.1186/s12859-018-2277-0', '30367589', 'The risk factors of diabetic retinopathy (DR) were investigated extensively in the past studies, but it remains unknown which risk factors were more associated with the DR than others. If we can detect the DR related risk factors more accurately, we can then exercise early prevention strategies for diabetic retinopathy in the most high-risk population. The purpose of this study is to build a prediction model for the DR in type 2 diabetes mellitus using data mining techniques including the support vector machines, decision trees, artificial neural networks, and logistic regressions.'),
(98590, 2018, 'bmc', 'DeepSort: deep convolutional networks for sorting haploid maize seeds', 'Veeramani, Balaji|Raymond, John W|Chanda, Pritam', 'Dow AgroSciences LLC, 9330 Zionsville Rd, Indianapolis, 46268, IN, USA. BVeeramani@dow.com.|Dow AgroSciences LLC, 9330 Zionsville Rd, Indianapolis, 46268, IN, USA.|Dow AgroSciences LLC, 9330 Zionsville Rd, Indianapolis, 46268, IN, USA.', 'India|India|India', 15, 'Algorithms|Genotype|Haploidy|Neural Networks (Computer)|Phenotype|Plant Breeding|Seeds|Zea mays', 'Journal Article', '10.1186/s12859-018-2267-2', '30367590', 'Maize is a leading crop in the modern agricultural industry that accounts for more than 40% grain production worldwide. THe double haploid technique that uses fewer breeding generations for generating a maize line has accelerated the pace of development of superior commercial seed varieties and has been transforming the agricultural industry. In this technique the chromosomes of the haploid seeds are doubled and taken forward in the process while the diploids marked for elimination. Traditionally, selective visual expression of a molecular marker within the embryo region of a maize seed has been used to manually discriminate diploids from haploids. Large scale production of inbred maize lines within the agricultural industry would benefit from the development of computer vision methods for this discriminatory task. However the variability in the phenotypic expression of the molecular marker system and the heterogeneity arising out of the maize genotypes and image acquisition have been an enduring challenge towards such efforts.'),
(98591, 2018, 'bmc', 'Drug response prediction model using a hierarchical structural component modeling method', 'Kim, Sungtae|Choi, Sungkyoung|Yoon, Jung-Hwan|Kim, Youngsoo|Lee, Seungyeoun|Park, Taesung', 'Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 08826, South Korea.|Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 08826, South Korea.|Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul, 03080, South Korea.|Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, 03080, South Korea.|Department of Mathematics and Statistics, Sejong University, Seoul, 05006, South Korea.|Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 08826, South Korea. tspark@stats.snu.ac.kr.', '-|-|-|-|-|-', 1, 'Adult|Aged|Aged, 80 and over|Algorithms|Antineoplastic Agents|Biomarkers, Tumor|Cohort Studies|Female|Humans|Liver Neoplasms|Male|Mass Spectrometry|Middle Aged|Models, Statistical|Proteomics|Sorafenib', 'Journal Article', '10.1186/s12859-018-2270-7', '30367591', 'Component-based structural equation modeling methods are now widely used in science, business, education, and other fields. This method uses unobservable variables, i.e., latent variables, and structural equation model relationships between observable variables. Here, we applied this structural equation modeling method to biologically structured data. To identify candidate drug-response biomarkers, we first used proteomic peptide-level data, as measured by multiple reaction monitoring mass spectrometry (MRM-MS), for liver cancer patients. MRM-MS is a highly sensitive and selective method for proteomic targeted quantitation of peptide abundances in complex biological samples.'),
(98592, 2018, 'bmc', 'On the impact of uncertain gene tree rooting on duplication-transfer-loss reconciliation', 'Kundu, Soumya|Bansal, Mukul S', 'Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, 06269, USA.|Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, 06269, USA. mukul.bansal@uconn.edu.', 'United States|United States', 2, 'Algorithms|Evolution, Molecular|Gene Duplication|Gene Transfer, Horizontal|Genomics|Multigene Family|Phylogeny|Software|Uncertainty', 'Journal Article', '10.1186/s12859-018-2269-0', '30367593', 'Duplication-Transfer-Loss (DTL) reconciliation is a powerful and increasingly popular technique for studying the evolution of microbial gene families. DTL reconciliation requires the use of rooted gene trees to perform the reconciliation with the species tree, and the standard technique for rooting gene trees is to assign a root that results in the minimum reconciliation cost across all rootings of that gene tree. However, even though it is well understood that many gene trees have multiple optimal roots, only a single optimal root is randomly chosen to create the rooted gene tree and perform the reconciliation. This remains an important overlooked and unaddressed problem in DTL reconciliation, leading to incorrect evolutionary inferences. In this work, we perform an in-depth analysis of the impact of uncertain gene tree rooting on the computed DTL reconciliation and provide the first computational tools to quantify and negate the impact of gene tree rooting uncertainty on DTL reconciliation.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(98593, 2018, 'bmc', 'A GPU-based algorithm for fast node label learning in large and unbalanced biomolecular networks', 'Frasca, Marco|Grossi, Giuliano|Gliozzo, Jessica|Mesiti, Marco|Notaro, Marco|Perlasca, Paolo|Petrini, Alessandro|Valentini, Giorgio', 'AnacletoLab - Department of Computer Science, Università degli Studi di Milano, Via Comelico 39, Milano, 20135, Italy. frasca@di.unimi.it.|AnacletoLab - Department of Computer Science, Università degli Studi di Milano, Via Comelico 39, Milano, 20135, Italy.|Department of Dermatology, Fondazione IRCCS Ca Granda,, Ospedale Maggiore Policlinico, Milan, 20122, Italy.|AnacletoLab - Department of Computer Science, Università degli Studi di Milano, Via Comelico 39, Milano, 20135, Italy.|AnacletoLab - Department of Computer Science, Università degli Studi di Milano, Via Comelico 39, Milano, 20135, Italy.|AnacletoLab - Department of Computer Science, Università degli Studi di Milano, Via Comelico 39, Milano, 20135, Italy.|AnacletoLab - Department of Computer Science, Università degli Studi di Milano, Via Comelico 39, Milano, 20135, Italy.|AnacletoLab - Department of Computer Science, Università degli Studi di Milano, Via Comelico 39, Milano, 20135, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 1, 'Algorithms|Animals|Computer Graphics|Gene Ontology|Gene Regulatory Networks|Humans|Mice|Protein Interaction Maps|Proteins|Saccharomyces cerevisiae|Time Factors', 'Journal Article', '10.1186/s12859-018-2301-4', '30367594', 'Several problems in network biology and medicine can be cast into a framework where entities are represented through partially labeled networks, and the aim is inferring the labels (usually binary) of the unlabeled part. Connections represent functional or genetic similarity between entities, while the labellings often are highly unbalanced, that is one class is largely under-represented: for instance in the automated protein function prediction (AFP) for most Gene Ontology terms only few proteins are annotated, or in the disease-gene prioritization problem only few genes are actually known to be involved in the etiology of a given disease. Imbalance-aware approaches to accurately predict node labels in biological networks are thereby required. Furthermore, such methods must be scalable, since input data can be large-sized as, for instance, in the context of multi-species protein networks.'),
(98594, 2018, 'bmc', 'Reproducible bioinformatics project: a community for reproducible bioinformatics analysis pipelines', 'Kulkarni, Neha|Alessandrì, Luca|Panero, Riccardo|Arigoni, Maddalena|Olivero, Martina|Ferrero, Giulio|Cordero, Francesca|Beccuti, Marco|Calogero, Raffaele A', 'Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy.|Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy.|Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy.|Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy.|Department of Oncology, University of Torino, Candiolo, Italy.|Department of Computer Sciences, University of Torino, Torino, Italy.|Department of Computer Sciences, University of Torino, Torino, Italy. francesca.cordero@unito.it.|Department of Computer Sciences, University of Torino, Torino, Italy.|Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy. raffaele.calogero@unito.it.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 12, 'Computational Biology|Humans|MicroRNAs|Reproducibility of Results|Software|User-Computer Interface|Workflow', 'Journal Article', '10.1186/s12859-018-2296-x', '30367595', 'Reproducibility of a research is a key element in the modern science and it is mandatory for any industrial application. It represents the ability of replicating an experiment independently by the location and the operator. Therefore, a study can be considered reproducible only if all used data are available and the exploited computational analysis workflow is clearly described. However, today for reproducing a complex bioinformatics analysis, the raw data and the list of tools used in the workflow could be not enough to guarantee the reproducibility of the results obtained. Indeed, different releases of the same tools and/or of the system libraries (exploited by such tools) might lead to sneaky reproducibility issues.'),
(98595, 2018, 'bmc', 'GRSR: a tool for deriving genome rearrangement scenarios from multiple unichromosomal genome sequences', 'Wang, Dan|Wang, Lusheng', 'Department of Computer Science, City University of Hong Kong, 83 Tat Chee Ave., Hong Kong, Peoples Republic of China.|Department of Computer Science, City University of Hong Kong, 83 Tat Chee Ave., Hong Kong, Peoples Republic of China. cswangl@cityu.edu.hk.', 'China|China', 1, 'Algorithms|Chromosome Mapping|Gene Rearrangement|Genome, Bacterial|Mycobacterium tuberculosis|Shewanella|Translocation, Genetic', 'Journal Article', '10.1186/s12859-018-2268-1', '30367596', 'Genome rearrangements describe changes in the genetic linkage relationship of large chromosomal regions, involving reversals, transpositions, block interchanges, deletions, insertions, fissions, fusions and translocations etc. Many algorithms for calculating rearrangement scenarios between two genomes have been proposed. Very often, the calculated rearrangement scenario is not unique for the same pair of permutations. Hence, how to decide which calculated rearrangement scenario is more biologically meaningful becomes an essential task. Up to now, several mechanisms for genome rearrangements have been studied. One important theory is that genome rearrangement may be mediated by repeats, especially for reversal events. Many reversal regions are found to be flanked by a pair of inverted repeats. As a result, whether there are repeats at the breakpoints of the calculated rearrangement events can shed a light on deciding whether the calculated rearrangement events is biologically meaningful. To our knowledge, there is no tool which can automatically identify rearrangement events and check whether there exist repeats at the breakpoints of each calculated rearrangement event.'),
(98596, 2018, 'bmc', 'Tigmint: correcting assembly errors using linked reads from large molecules', 'Jackman, Shaun D|Coombe, Lauren|Chu, Justin|Warren, Rene L|Vandervalk, Benjamin P|Yeo, Sarah|Xue, Zhuyi|Mohamadi, Hamid|Bohlmann, Joerg|Jones, Steven J M|Birol, Inanc', 'BC Cancer Genome Sciences Centre, Vancouver, V5Z 4S6, BC, Canada. sjackman@bcgsc.ca.|BC Cancer Genome Sciences Centre, Vancouver, V5Z 4S6, BC, Canada.|BC Cancer Genome Sciences Centre, Vancouver, V5Z 4S6, BC, Canada.|BC Cancer Genome Sciences Centre, Vancouver, V5Z 4S6, BC, Canada.|BC Cancer Genome Sciences Centre, Vancouver, V5Z 4S6, BC, Canada.|BC Cancer Genome Sciences Centre, Vancouver, V5Z 4S6, BC, Canada.|BC Cancer Genome Sciences Centre, Vancouver, V5Z 4S6, BC, Canada.|BC Cancer Genome Sciences Centre, Vancouver, V5Z 4S6, BC, Canada.|University of British Columbia, Michael Smith Laboratories, Vancouver, V6T 1Z4, BC, Canada.|BC Cancer Genome Sciences Centre, Vancouver, V5Z 4S6, BC, Canada.|BC Cancer Genome Sciences Centre, Vancouver, V5Z 4S6, BC, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada', 10, 'Chromosomes, Human|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Nanopores|Repetitive Sequences, Nucleic Acid|Software', 'Journal Article', '10.1186/s12859-018-2425-6', '30367597', 'Genome sequencing yields the sequence of many short snippets of DNA (reads) from a genome. Genome assembly attempts to reconstruct the original genome from which these reads were derived. This task is difficult due to gaps and errors in the sequencing data, repetitive sequence in the underlying genome, and heterozygosity. As a result, assembly errors are common. In the absence of a reference genome, these misassemblies may be identified by comparing the sequencing data to the assembly and looking for discrepancies between the two. Once identified, these misassemblies may be corrected, improving the quality of the assembled sequence. Although tools exist to identify and correct misassemblies using Illumina paired-end and mate-pair sequencing, no such tool yet exists that makes use of the long distance information of the large molecules provided by linked reads, such as those offered by the 10x Genomics Chromium platform. We have developed the tool Tigmint to address this gap.'),
(98597, 2018, 'bmc', 'BMCMDA: a novel model for predicting human microbe-disease associations via binary matrix completion', 'Shi, Jian-Yu|Huang, Hua|Zhang, Yan-Ning|Cao, Jiang-Bo|Yiu, Siu-Ming', 'School of Life Sciences, Northwestern Polytechnical University, Xian, 70072, China. jianyushi@nwpu.edu.cn.|School of Software and Microelectronics, Northwestern Polytechnical University, Xian, 70072, China.|School of Computer Science, Northwestern Polytechnical University, Xian, 70072, China.|School of Life Sciences, Northwestern Polytechnical University, Xian, 70072, China.|Department of Computer Science, The University of Hong Kong, Hong Kong, 999077, China.', 'China|China|China|China|China', 8, 'Algorithms|Bacteria|Bacterial Physiological Phenomena|Computational Biology|Disease|Host-Pathogen Interactions|Humans|Microbiota|Models, Biological|Risk Factors', 'Journal Article', '10.1186/s12859-018-2274-3', '30367598', 'Human Microbiome Project reveals the significant mutualistic influence between human body and microbes living in it. Such an influence lead to an interesting phenomenon that many noninfectious diseases are closely associated with diverse microbes. However, the identification of microbe-noninfectious disease associations (MDAs) is still a challenging task, because of both the high cost and the limitation of microbe cultivation. Thus, there is a need to develop fast approaches to screen potential MDAs. The growing number of validated MDAs enables us to meet the demand in a new insight. Computational approaches, especially machine learning, are promising to predict MDA candidates rapidly among a large number of microbe-disease pairs with the advantage of no limitation on microbe cultivation. Nevertheless, a few computational efforts at predicting MDAs are made so far.'),
(98598, 2018, 'bmc', 'Feature selection of gene expression data for Cancer classification using double RBF-kernels', 'Liu, Shenghui|Xu, Chunrui|Zhang, Yusen|Liu, Jiaguo|Yu, Bin|Liu, Xiaoping|Dehmer, Matthias', 'School of Mathematics and Statistics, Shandong University at Weihai, Weihai, 264209, China.|School of Mathematics and Statistics, Shandong University at Weihai, Weihai, 264209, China.|School of Mathematics and Statistics, Shandong University at Weihai, Weihai, 264209, China. zhangys@sdu.edu.cn.|School of Mathematics and Statistics, Shandong University at Weihai, Weihai, 264209, China.|College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, 266061, China.|School of Mathematics and Statistics, Shandong University at Weihai, Weihai, 264209, China. xpliu@sdu.edu.cn.|Institute for Intelligent Production, Faculty for Management, University of Applied Sciences Upper Austria, Steyr Campus, Steyr, Austria.', 'China|China|China|China|China|China|Austria', 14, 'Algorithms|Computational Biology|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Neoplasm Proteins|Neoplasms|Phenotype', 'Journal Article', '10.1186/s12859-018-2400-2', '30373514', 'Using knowledge-based interpretation to analyze omics data can not only obtain essential information regarding various biological processes, but also reflect the current physiological status of cells and tissue. The major challenge to analyze gene expression data, with a large number of genes and small samples, is to extract disease-related information from a massive amount of redundant data and noise. Gene selection, eliminating redundant and irrelevant genes, has been a key step to address this problem.'),
(98599, 2018, 'bmc', 'Feature related multi-view nonnegative matrix factorization for identifying conserved functional modules in multiple biological networks', 'Wang, Peizhuo|Gao, Lin|Hu, Yuxuan|Li, Feng', 'School of Computer Science and Technology, Xidian University, Xian, 710071, China.|School of Computer Science and Technology, Xidian University, Xian, 710071, China. lgao@mail.xidian.edu.cn.|School of Computer Science and Technology, Xidian University, Xian, 710071, China.|School of Computer Science and Technology, Xidian University, Xian, 710071, China.', 'China|China|China|China', 0, 'Algorithms|Cluster Analysis|Computational Biology|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Gene Regulatory Networks|Humans|Neoplasms|Protein Interaction Maps', 'Journal Article', '10.1186/s12859-018-2434-5', '30373534', 'Comprehensive analyzing multi-omics biological data in different conditions is important for understanding biological mechanism in system level. Multiple or multi-layer network model gives us a new insight into simultaneously analyzing these data, for instance, to identify conserved functional modules in multiple biological networks. However, because of the larger scale and more complicated structure of multiple networks than single network, how to accurate and efficient detect conserved functional biological modules remains a significant challenge.'),
(98600, 2018, 'bmc', 'A fully automated pipeline for quantitative genotype calling from next generation sequencing data in autopolyploids', 'Pereira, Guilherme S|Garcia, Antonio Augusto F|Margarido, Gabriel R A', 'University of São Paulo, Luiz de Queiroz College of Agriculture, Department of Genetics, Av Pádua Dias, 11, Piracicaba, 13400-970, Brazil.|University of São Paulo, Luiz de Queiroz College of Agriculture, Department of Genetics, Av Pádua Dias, 11, Piracicaba, 13400-970, Brazil.|University of São Paulo, Luiz de Queiroz College of Agriculture, Department of Genetics, Av Pádua Dias, 11, Piracicaba, 13400-970, Brazil. gramarga@usp.br.', 'Brazil|Brazil|Brazil', 9, 'Automation, Laboratory|Genetic Markers|Genotype|Genotyping Techniques|High-Throughput Nucleotide Sequencing|Medicago sativa|Polymorphism, Single Nucleotide|Polyploidy|Software|Solanum tuberosum', 'Journal Article', '10.1186/s12859-018-2433-6', '30382832', 'Genotyping-by-sequencing (GBS) has been used broadly in genetic studies for several species, especially those with agricultural importance. However, its use is still limited in autopolyploid species because genotype calling software generally fails to properly distinguish heterozygous classes based on allele dosage.'),
(98601, 2018, 'bmc', 'ISU FLUture: a veterinary diagnostic laboratory web-based platform to monitor the temporal genetic patterns of Influenza A virus in swine', 'Zeller, Michael A|Anderson, Tavis K|Walia, Rasna W|Vincent, Amy L|Gauger, Phillip C', 'Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA, USA.|Virus and Prion Research Unit, National Animal Disease Center, USDA-ARS, Ames, IA, USA.|Virus and Prion Research Unit, National Animal Disease Center, USDA-ARS, Ames, IA, USA.|Virus and Prion Research Unit, National Animal Disease Center, USDA-ARS, Ames, IA, USA.|Department of Veterinary Diagnostic & Production Animal Medicine, Iowa State University, 1575 Vet Med, 1850 Christensen Dr, Ames, IA, 50011-1134, USA. pcgauger@iastate.edu.', 'United States|United States|United States|United States|United States', 5, 'Animals|Influenza A virus|Internet|Laboratories|Molecular Diagnostic Techniques|Orthomyxoviridae Infections|Phylogeny|Swine|Swine Diseases|United States', 'Journal Article', '10.1186/s12859-018-2408-7', '30382842', 'Influenza A Virus (IAV) causes respiratory disease in swine and is a zoonotic pathogen. Uncontrolled IAV in swine herds not only affects animal health, it also impacts production through increased costs associated with treatment and prevention efforts. The Iowa State University Veterinary Diagnostic Laboratory (ISU VDL) diagnoses influenza respiratory disease in swine and provides epidemiological analyses on samples submitted by veterinarians.'),
(98602, 2018, 'bmc', 'What we can see from very small size sample of metagenomic sequences', 'Kwak, Jaesik|Park, Joonhong', 'Graduate Program in Technology Policy, Yonsei University, 50 Yonsei Ro, Seodaemun Gu, Seoul, 038722, South Korea.|School of Civil and Environmental Engineering, Yonsei University, 50 Yonsei Ro, Seodaemun Gu, Seoul, 038722, South Korea. parkj@yonsei.ac.kr.', '-|-', 0, 'Base Composition|Base Sequence|Metagenome|Metagenomics|Molecular Sequence Annotation|Phylogeny|Sample Size', 'Journal Article', '10.1186/s12859-018-2431-8', '30390617', 'Since the analysis of a large number of metagenomic sequences costs heavy computing resources and takes long time, we examined a selected small part of metagenomic sequences as samples of the entire full sequences, both for a mock community and for 10 different existing metagenomics case studies. A mock community with 10 bacterial strains was prepared, and their mixed genome were sequenced by Hiseq. The hits of BLAST search for reference genome of each strain were counted. Each of 176 different small parts selected from these sequences were also searched by BLAST and their hits were also counted, in order to compare them to the original search results from the full sequences. We also prepared small parts of sequences which were selected from 10 publicly downloadable research data of MG-RAST service, and analyzed these samples with MG-RAST.'),
(98603, 2018, 'bmc', 'A comparative study of survival models for breast cancer prognostication revisited: the benefits of multi-gene models', 'Grzadkowski, Michal R|Sendorek, Dorota H|Png, Christine|Huang, Vincent|Boutros, Paul C', 'Ontario Institute for Cancer Research, Toronto, Canada.|Ontario Institute for Cancer Research, Toronto, Canada.|Ontario Institute for Cancer Research, Toronto, Canada.|Ontario Institute for Cancer Research, Toronto, Canada.|Ontario Institute for Cancer Research, Toronto, Canada. paul.boutros@oicr.on.ca.', 'Canada|Canada|Canada|Canada|Canada', 1, 'Algorithms|Biomarkers, Tumor|Breast Neoplasms|Databases, Genetic|Female|Humans|Models, Genetic|Prognosis|Survival Analysis', 'Comparative Study', '10.1186/s12859-018-2430-9', '30390622', 'The development of clinical -omic biomarkers for predicting patient prognosis has mostly focused on multi-gene models. However, several studies have described significant weaknesses of multi-gene biomarkers. Indeed, some high-profile reports have even indicated that multi-gene biomarkers fail to consistently outperform simple single-gene ones. Given the continual improvements in -omics technologies and the availability of larger, better-powered datasets, we revisited this single-gene hypothesis using new techniques and datasets.'),
(98604, 2018, 'bmc', 'Adaptively capturing the heterogeneity of expression for cancer biomarker identification', 'Xie, Xin-Ping|Xie, Yu-Feng|Liu, Yi-Tong|Wang, Hong-Qiang', 'School of Mathematics and Physics, Anhui Jianzhu University, Hefei, 230022, Anhui, China.|School of Mathematics and Physics, Anhui Jianzhu University, Hefei, 230022, Anhui, China.|School of Mathematics and Physics, Anhui Jianzhu University, Hefei, 230022, Anhui, China.|Institute of Intelligent Machines, Hefei Institutes of Physical Science, CAS, 350 Shushanhu Road, P.O.Box 1130, Hefei, 230031, Anhui, China. hqwang126@126.com.', 'China|China|China|China', 0, 'Biomarkers, Tumor|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Genetic Heterogeneity|Humans|Models, Genetic|Neoplasms|Oligonucleotide Array Sequence Analysis|Probability|Sequence Analysis, RNA|Software|Transcriptome', 'Journal Article', '10.1186/s12859-018-2437-2', '30390627', 'Identifying cancer biomarkers from transcriptomics data is of importance to cancer research. However, transcriptomics data are often complex and heterogeneous, which complicates the identification of cancer biomarkers in practice. Currently, the heterogeneity still remains a challenge for detecting subtle but consistent changes of gene expression in cancer cells.'),
(98605, 2018, 'bmc', 'SIFR annotator: ontology-based semantic annotation of French biomedical text and clinical notes', 'Tchechmedjiev, Andon|Abdaoui, Amine|Emonet, Vincent|Zevio, Stella|Jonquet, Clement', 'Laboratory of Informatics, Robotics and Microelectronics of Montpellier (LIRMM), University of Montpellier, CNRS, 161, rue Ada, 34095, Montpellier cedex 5, France. andon.tchechmedjiev@lirmm.fr.|Laboratory of Informatics, Robotics and Microelectronics of Montpellier (LIRMM), University of Montpellier, CNRS, 161, rue Ada, 34095, Montpellier cedex 5, France.|Laboratory of Informatics, Robotics and Microelectronics of Montpellier (LIRMM), University of Montpellier, CNRS, 161, rue Ada, 34095, Montpellier cedex 5, France.|Laboratory of Informatics, Robotics and Microelectronics of Montpellier (LIRMM), University of Montpellier, CNRS, 161, rue Ada, 34095, Montpellier cedex 5, France.|Laboratory of Informatics, Robotics and Microelectronics of Montpellier (LIRMM), University of Montpellier, CNRS, 161, rue Ada, 34095, Montpellier cedex 5, France.', 'France|France|France|France|France', 2, 'Abstracting and Indexing as Topic|Biological Ontologies|Data Analysis|France|Gene Expression Profiling|Health Records, Personal|Humans|Information Storage and Retrieval|MEDLINE|Medical Informatics|Natural Language Processing|Semantics', 'Journal Article', '10.1186/s12859-018-2429-2', '30400805', 'Despite a wide adoption of English in science, a significant amount of biomedical data are produced in other languages, such as French. Yet a majority of natural language processing or semantic tools as well as domain terminologies or ontologies are only available in English, and cannot be readily applied to other languages, due to fundamental linguistic differences. However, semantic resources are required to design semantic indexes and transform biomedical (text)data into knowledge for better information mining and retrieval.'),
(98606, 2018, 'bmc', 'Assessing the impact of exact reads on reducing the error rate of read mapping', 'Salari, Farzaneh|Zare-Mirakabad, Fatemeh|Sadeghi, Mehdi|Rokni-Zadeh, Hassan', 'Mathematics and Computer Science Department, Amirkabir University of Technology (Tehran polytechnic), Tehran, Iran.|Mathematics and Computer Science Department, Amirkabir University of Technology (Tehran polytechnic), Tehran, Iran. f.zare@aut.ac.ir.|National Institute of Genetic Engineering and Biotechnology, Tehran, Iran.|Department of Biotechnology and Molecular Medicine, Zanjan University of Medical Sciences, Zanjan, Iran.', '-|-|-|-', 0, 'Algorithms|Chromosome Mapping|Computational Biology|Escherichia coli|Escherichia coli Proteins|Genome, Bacterial|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-018-2432-7', '30400807', 'Nowadays, according to valuable resources of high-quality genome sequences, reference-based assembly methods with high accuracy and efficiency are strongly required. Many different algorithms have been designed for mapping reads onto a genome sequence which try to enhance the accuracy of reconstructed genomes. In this problem, one of the challenges occurs when some reads are aligned to multiple locations due to repetitive regions in the genomes.'),
(98607, 2018, 'bmc', 'Single sample scoring of molecular phenotypes', 'Foroutan, Momeneh|Bhuva, Dharmesh D|Lyu, Ruqian|Horan, Kristy|Cursons, Joseph|Davis, Melissa J', 'University of Melbourne Department of Surgery, St. Vincents Hospital, Melbourne, VIC, 3065, Australia.|Division of Bioinformatics, Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, 3051, Australia.|Division of Bioinformatics, Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, 3051, Australia.|Division of Bioinformatics, Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, 3051, Australia.|Division of Bioinformatics, Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, 3051, Australia. cursons.j@wehi.edu.au.|Division of Bioinformatics, Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, 3051, Australia. davis.m@wehi.edu.au.', 'Australia|Australia|Australia|Australia|Australia|Australia', 17, 'Computational Biology|Gene Expression Profiling|Humans|Neoplasms|Phenotype|Precision Medicine|Transcriptome', 'Journal Article', '10.1186/s12859-018-2435-4', '30400809', 'Gene set scoring provides a useful approach for quantifying concordance between sample transcriptomes and selected molecular signatures. Most methods use information from all samples to score an individual sample, leading to unstable scores in small data sets and introducing biases from sample composition (e.g. varying numbers of samples for different cancer subtypes). To address these issues, we have developed a truly single sample scoring method, and associated R/Bioconductor package singscore ( https://bioconductor.org/packages/singscore ).'),
(98608, 2018, 'bmc', 'SyNDI: synchronous network data integration framework', 'Lindfors, Erno|van Dam, Jesse C J|Lam, Carolyn Ming Chi|Zondervan, Niels A|Martins Dos Santos, Vitor A P|Suarez-Diez, Maria', 'LifeGlimmer GmbH, Markelstrasse 38, 12163, Berlin, Germany. lindfors@lifeglimmer.com.|Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Stippeneng 4, 6708 WE, Wageningen, The Netherlands.|LifeGlimmer GmbH, Markelstrasse 38, 12163, Berlin, Germany.|Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Stippeneng 4, 6708 WE, Wageningen, The Netherlands.|LifeGlimmer GmbH, Markelstrasse 38, 12163, Berlin, Germany.|Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Stippeneng 4, 6708 WE, Wageningen, The Netherlands.', 'Germany|Netherlands|Germany|Netherlands|Germany|Netherlands', 0, 'Algorithms|Animals|Computational Biology|Gene Expression Regulation, Bacterial|Gene Regulatory Networks|Humans|Mice|Models, Biological|Mycobacterium tuberculosis|Software|Staphylococcus aureus', 'Journal Article', '10.1186/s12859-018-2426-5', '30400817', 'Systems biology takes a holistic approach by handling biomolecules and their interactions as big systems. Network based approach has emerged as a natural way to model these systems with the idea of representing biomolecules as nodes and their interactions as edges. Very often the input data come from various sorts of omics analyses. Those resulting networks sometimes describe a wide range of aspects, for example different experiment conditions, species, tissue types, stimulating factors, mutants, or simply distinct interaction features of the same network produced by different algorithms. For these scenarios, synchronous visualization of more than one distinct network is an excellent mean to explore all the relevant networks efficiently. In addition, complementary analysis methods are needed and they should work in a workflow manner in order to gain maximal biological insights.'),
(98609, 2018, 'bmc', 'Detection of long non-coding RNA homology, a comparative study on alignment and alignment-free metrics', 'Noviello, Teresa M R|Di Liddo, Antonella|Ventola, Giovanna M|Spagnuolo, Antonietta|DAniello, Salvatore|Ceccarelli, Michele|Cerulo, Luigi', 'Dep. of Science and Technology, University of Sannio, via PortArsa, 11, Benevento, 82100, Italy.|Buchmann Institute for Molecular Life Sciences, Goethe University, Max-von-Laue-Straße 13, Frankfurt am Main, 60438, Germany.|Genomix4Life S.r.l., Via Salvador Allende, Baronissi (SA), 84081, Italy.|Dep. of Biology and Evolution of Marine Organisms, Stazione Zoologica A. Dohrn, Villa Comunale, Napoli, 80121, Italy.|Dep. of Biology and Evolution of Marine Organisms, Stazione Zoologica A. Dohrn, Villa Comunale, Napoli, 80121, Italy.|Dep. of Science and Technology, University of Sannio, via PortArsa, 11, Benevento, 82100, Italy.|Dep. of Science and Technology, University of Sannio, via PortArsa, 11, Benevento, 82100, Italy. lcerulo@unisannio.it.', 'Italy|Germany|Italy|Italy|Italy|Italy|Italy', 0, 'Animals|Conserved Sequence|Gene Expression Regulation|Genome|Humans|Mice|RNA, Long Noncoding|Sequence Alignment|Zebrafish', 'Comparative Study', '10.1186/s12859-018-2441-6', '30400819', 'Long non-coding RNAs (lncRNAs) represent a novel class of non-coding RNAs having a crucial role in many biological processes. The identification of long non-coding homologs among different species is essential to investigate such roles in model organisms as homologous genes tend to retain similar molecular and biological functions. Alignment-based metrics are able to effectively capture the conservation of transcribed coding sequences and then the homology of protein coding genes. However, unlike protein coding genes the poor sequence conservation of long non-coding genes makes the identification of their homologs a challenging task.'),
(98610, 2018, 'bmc', 'Deblender: a semi-/unsupervised multi-operational computational method for complete deconvolution of expression data from heterogeneous samples', 'Dimitrakopoulou, Konstantina|Wik, Elisabeth|Akslen, Lars A|Jonassen, Inge', 'Centre for Cancer Biomarkers CCBIO, Department of Informatics, University of Bergen, Bergen, Norway.|Centre for Cancer Biomarkers CCBIO, Department of Clinical Medicine, Section for Pathology, University of Bergen, Bergen, Norway.|Centre for Cancer Biomarkers CCBIO, Department of Clinical Medicine, Section for Pathology, University of Bergen, Bergen, Norway.|Centre for Cancer Biomarkers CCBIO, Department of Informatics, University of Bergen, Bergen, Norway. Inge.Jonassen@uib.no.', 'Norway|Norway|Norway|Norway', 2, 'Algorithms|Computational Biology|Gene Expression|Humans', 'Journal Article', '10.1186/s12859-018-2442-5', '30404611', 'Towards discovering robust cancer biomarkers, it is imperative to unravel the cellular heterogeneity of patient samples and comprehend the interactions between cancer cells and the various cell types in the tumor microenvironment. The first generation of partial computational deconvolution methods required prior information either on the cell/tissue type proportions or the cell/tissue type-specific expression signatures and the number of involved cell/tissue types. The second generation of complete approaches allowed estimating both of the cell/tissue type proportions and cell/tissue type-specific expression profiles directly from the mixed gene expression data, based on known (or automatically identified) cell/tissue type-specific marker genes.'),
(98611, 2018, 'bmc', 'WebNetCoffee: a web-based application to identify functionally conserved proteins from Multiple PPI networks', 'Hu, Jialu|Gao, Yiqun|He, Junhao|Zheng, Yan|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.|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|China|China', 4, 'Computational Biology|Humans|Protein Interaction Mapping', 'Journal Article', '10.1186/s12859-018-2443-4', '30419809', 'The discovery of functionally conserved proteins is a tough and important task in system biology. Global network alignment provides a systematic framework to search for these proteins from multiple protein-protein interaction (PPI) networks. Although there exist many web servers for network alignment, no one allows to perform global multiple network alignment tasks on users test datasets.'),
(98612, 2018, 'bmc', 'hsegHMM: hidden Markov model-based allele-specific copy number alteration analysis accounting for hypersegmentation', 'Choo-Wosoba, Hyoyoung|Albert, Paul S|Zhu, Bin', 'Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA.|Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA.|Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA. bin.zhu@nih.gov.', 'United States|United States|United States', 0, 'Alleles|DNA Copy Number Variations|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-018-2412-y', '30428830', 'Somatic copy number alternation (SCNA) is a common feature of the cancer genome and is associated with cancer etiology and prognosis. The allele-specific SCNA analysis of a tumor sample aims to identify the allele-specific copy numbers of both alleles, adjusting for the ploidy and the tumor purity. Next generation sequencing platforms produce abundant read counts at the base-pair resolution across the exome or whole genome which is susceptible to hypersegmentation, a phenomenon where numerous regions with very short length are falsely identified as SCNA.'),
(98613, 2018, 'bmc', 'Empirical assessment of the impact of sample number and read depth on RNA-Seq analysis workflow performance', 'Baccarella, Alyssa|Williams, Claire R|Parrish, Jay Z|Kim, Charles C', 'Division of Experimental Medicine, Department of Medicine, University of California, San Francisco, California, 94143, USA. alyssabaccarella@gmail.com.|Department of Biology, University of Washington, Seattle, WA, 98195, USA.|Department of Biology, University of Washington, Seattle, WA, 98195, USA.|Division of Experimental Medicine, Department of Medicine, University of California, San Francisco, California, 94143, USA.', 'United States|United States|United States|United States', 9, 'Gene Expression Profiling|High-Throughput Nucleotide Sequencing|Humans|RNA|Sequence Analysis, RNA|Workflow', 'Journal Article', '10.1186/s12859-018-2445-2', '30428853', 'RNA-Sequencing analysis methods are rapidly evolving, and the tool choice for each step of one common workflow, differential expression analysis, which includes read alignment, expression modeling, and differentially expressed gene identification, has a dramatic impact on performance characteristics. Although a number of workflows are emerging as high performers that are robust to diverse input types, the relative performance characteristics of these workflows when either read depth or sample number is limited-a common occurrence in real-world practice-remain unexplored.'),
(98614, 2018, 'bmc', 'Learning protein binding affinity using privileged information', 'Abbasi, Wajid Arshad|Asif, Amina|Ben-Hur, Asa|Minhas, Fayyaz Ul Amir Afsar', 'Biomedical Informatics Research Laboratory (BIRL), Department of Computer and Information Sciences (DCIS), Pakistan Institute of Engineering and Applied Sciences (PIEAS), Nilore, ISL, 45650, Pakistan.|Biomedical Informatics Research Laboratory (BIRL), Department of Computer and Information Sciences (DCIS), Pakistan Institute of Engineering and Applied Sciences (PIEAS), Nilore, ISL, 45650, Pakistan.|Department of Computer Science, Colorado State University (CSU), Fort Collins, CO, 80523, USA. asa@cs.colostate.edu.|Biomedical Informatics Research Laboratory (BIRL), Department of Computer and Information Sciences (DCIS), Pakistan Institute of Engineering and Applied Sciences (PIEAS), Nilore, ISL, 45650, Pakistan. fayyazafsar@gmail.com.', 'Pakistan|Pakistan|United States|Pakistan', 3, 'Algorithms|Amino Acid Sequence|Computational Biology|Ligands|Machine Learning|Protein Binding|Proteins|ROC Curve|Reproducibility of Results|Support Vector Machine', 'Journal Article', '10.1186/s12859-018-2448-z', '30442086', 'Determining protein-protein interactions and their binding affinity are important in understanding cellular biological processes, discovery and design of novel therapeutics, protein engineering, and mutagenesis studies. Due to the time and effort required in wet lab experiments, computational prediction of binding affinity from sequence or structure is an important area of research. Structure-based methods, though more accurate than sequence-based techniques, are limited in their applicability due to limited availability of protein structure data.'),
(98615, 2018, 'bmc', 'Modified Pearson correlation coefficient for two-color imaging in spherocylindrical cells', 'Mohapatra, Sonisilpa|Weisshaar, James C', 'Department of Chemistry, University of Wisconsin-Madison, Madison, WI, 53706, USA. smohapa2@jhmi.edu.|Department of Chemistry, University of Wisconsin-Madison, Madison, WI, 53706, USA.', 'United States|United States', 6, 'Cells|Color|Correlation of Data|Humans|Imaging, Three-Dimensional|Microscopy, Fluorescence', 'Journal Article', '10.1186/s12859-018-2444-3', '30445904', 'The revolution in fluorescence microscopy enables sub-diffraction-limit (superresolution) localization of hundreds or thousands of copies of two differently labeled proteins in the same live cell. In typical experiments, fluorescence from the entire three-dimensional (3D) cell body is projected along the z-axis of the microscope to form a 2D image at the camera plane. For imaging of two different species, here denoted red and green, a significant biological question is the extent to which the red and green spatial distributions are positively correlated, anti-correlated, or uncorrelated. A commonly used statistic for assessing the degree of linear correlation between two image matrices R and G is the Pearson Correlation Coefficient (PCC). PCC should vary from -xe2x80x891 (perfect anti-correlation) to 0 (no linear correlation) to +xe2x80x891 (perfect positive correlation). However, in the special case of spherocylindrical bacterial cells such as E. coli or B. subtilis, we show that the PCC fails both qualitatively and quantitatively. PCC returns the same +xe2x80x891 value for 2D projections of distributions that are either perfectly correlated in 3D or completely uncorrelated in 3D. The PCC also systematically underestimates the degree of anti-correlation between the projections of two perfectly anti-correlated 3D distributions. The problem is that the projection of a random spatial distribution within the 3D spherocylinder is non-random in 2D, whereas PCC compares every matrix element of R or G with the constant mean value [Formula: see text] or [Formula: see text]. We propose a modified Pearson Correlation Coefficient (MPCC) that corrects this problem for spherocylindrical cell geometry by using the proper reference matrix for comparison with R and G. Correct behavior of MPCC is confirmed for a variety of numerical simulations and on experimental distributions of HU and RNA polymerase in live E. coli cells. The MPCC concept should be generalizable to other cell shapes.'),
(98616, 2018, 'bmc', 'RefCell: multi-dimensional analysis of image-based high-throughput screens based on typical cells', 'Shen, Yang|Kubben, Nard|Candia, Julián|Morozov, Alexandre V|Misteli, Tom|Losert, Wolfgang', 'Department of Physics and Institute for Physical Science and Technology, University of Maryland, College Park, MD, 20742, USA.|National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA.|Trans-NIH Center for Human Immunology (CHI), National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20892, USA.|Department of Physics and Astronomy and Center for Quantitative Biology, Rutgers University, Piscataway, NJ, 08854, USA.|National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA.|Department of Physics and Institute for Physical Science and Technology, University of Maryland, College Park, MD, 20742, USA. wlosert@umd.edu.', 'United States|United States|United States|United States|United States|United States', 0, 'High-Throughput Screening Assays|Humans|RNA, Small Interfering', 'Journal Article', '10.1186/s12859-018-2454-1', '30445906', 'Image-based high-throughput screening (HTS) reveals a high level of heterogeneity in single cells and multiple cellular states may be observed within a single population. Currently available high-dimensional analysis methods are successful in characterizing cellular heterogeneity, but suffer from the curse of dimensionality and non-standardized outputs.'),
(98617, 2018, 'bmc', 'The poly-omics of ageing through individual-based metabolic modelling', 'Yaneske, Elisabeth|Angione, Claudio', 'Department of Computer Science and Information Systems, Teesside University, Borough Road, Middlesbrough, UK. e.yaneske@tees.ac.uk.|Department of Computer Science and Information Systems, Teesside University, Borough Road, Middlesbrough, UK.', 'United Kingdom|United Kingdom', 6, 'Adult|Aging|Cluster Analysis|Female|Genomics|Humans|Male|Middle Aged|Models, Biological|Principal Component Analysis|Regression Analysis|T-Lymphocytes|Young Adult', 'Journal Article', '10.1186/s12859-018-2383-z', '30453872', 'Ageing can be classified in two different ways, chronological ageing and biological ageing. While chronological age is a measure of the time that has passed since birth, biological (also known as transcriptomic) ageing is defined by how time and the environment affect an individual in comparison to other individuals of the same chronological age. Recent research studies have shown that transcriptomic age is associated with certain genes, and that each of those genes has an effect size. Using these effect sizes we can calculate the transcriptomic age of an individual from their age-associated gene expression levels. The limitation of this approach is that it does not consider how these changes in gene expression affect the metabolism of individuals and hence their observable cellular phenotype.'),
(98618, 2018, 'bmc', 'Expression analysis of RNA sequencing data from human neural and glial cell lines depends on technical replication and normalization methods', 'Knight, V Bleu|Serrano, Elba E', 'Department of Biology, New Mexico State University, Las Cruces, NM, USA.|Department of Biology, New Mexico State University, Las Cruces, NM, USA. serrano@nmsu.edu.', 'Mexico|Mexico', 0, 'Base Sequence|Cell Line|Gene Expression Profiling|Gene Expression Regulation|Humans|Imaging, Three-Dimensional|Metabolic Networks and Pathways|Neural Stem Cells|Neuroglia|Neurons|Principal Component Analysis|RNA|Reproducibility of Results|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-018-2382-0', '30453873', 'The potential for astrocyte participation in central nervous system recovery is highlighted by in vitro experiments demonstrating their capacity to transdifferentiate into neurons. Understanding astrocyte plasticity could be advanced by comparing astrocytes with stem cells. RNA sequencing (RNA-seq) is ideal for comparing differences across cell types. However, this novel multi-stage process has the potential to introduce unwanted technical variation at several points in the experimental workflow. Quantitative understanding of the contribution of experimental parameters to technical variation would facilitate the design of robust RNA-Seq experiments.'),
(98619, 2018, 'bmc', 'TransFlow: a modular framework for assembling and assessing accurate de novo transcriptomes in non-model organisms', 'Seoane, Pedro|Espigares, Marina|Carmona, Rosario|Polonio, Álvaro|Quintana, Julia|Cretazzo, Enrico|Bota, Josefina|Pérez-García, Alejandro|Dios Alché, Juan de|Gómez, Luis|Claros, M Gonzalo', 'Departmento de Biología Molecular y Bioquímica, Universidad de Málaga, Campus de Teatinos s/n, Malaga, 29071, Spain.|Departmento de Biología Molecular y Bioquímica, Universidad de Málaga, Campus de Teatinos s/n, Malaga, 29071, Spain.|Plant Reproductive Biology Laboratory, Department of Biochemistry, Cell and Molecular Biology of Plants. Estación Experimental del Zaidín. CSIC, Prof. Albareda, 1, Granada, 18160, Spain.|Departamento de Microbiología, and Instituto de Hortofruticultura Subtropical y Mediterránea La Mayora, Universidad de Málaga, Consejo Superior de Investigaciones Científicas (IHSM-UMA-CSIC), Campus de Teatinos s/n, Malaga, 29071, Spain.|Department of Chemistry and Biochemistry, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA, 01609-2280, USA.|Instituto Andaluz de Investigación y Formación Agraria (IFAPA), Centro de Churriana, Cortijo de la Cruz s/n, Churriana, 29140, Spain.|Grup de Recerca en Biologia de les Plantes en Condicions Mediterrànies, Departament de Biologia, Universitat de les Illes Balears, Carretera de Valldemossa, km 7.5, Palma de Mallorca, 07122, Spain.|Departamento de Microbiología, and Instituto de Hortofruticultura Subtropical y Mediterránea La Mayora, Universidad de Málaga, Consejo Superior de Investigaciones Científicas (IHSM-UMA-CSIC), Campus de Teatinos s/n, Malaga, 29071, Spain.|Plant Reproductive Biology Laboratory, Department of Biochemistry, Cell and Molecular Biology of Plants. Estación Experimental del Zaidín. CSIC, Prof. Albareda, 1, Granada, 18160, Spain.|Departamento de Sistemas y Recursos Naturales, ETSI Forestal, de Montes y del Medio Natural, Universidad Politécnica de Madrid, Ciudad Universitaria, Madrid, 28040, Spain.|Departmento de Biología Molecular y Bioquímica, Universidad de Málaga, Campus de Teatinos s/n, Malaga, 29071, Spain. claros@uma.es.', 'Spain|Spain|Spain|Spain|United States|Spain|Spain|Spain|Spain|Spain|Spain', 5, 'Base Pairing|Fungi|Gene Expression Profiling|Plants|Principal Component Analysis|Reproducibility of Results|Sequence Analysis, RNA|Software|Transcriptome|Workflow', 'Journal Article', '10.1186/s12859-018-2384-y', '30453874', 'The advances in high-throughput sequencing technologies are allowing more and more de novo assembling of transcriptomes from many new organisms. Some degree of automation and evaluation is required to warrant reproducibility, repetitivity and the selection of the best possible transcriptome. Workflows and pipelines are becoming an absolute requirement for such a purpose, but the issue of assembling evaluation for de novo transcriptomes in organisms lacking a sequenced genome remains unsolved. An automated, reproducible and flexible framework called TransFlow to accomplish this task is described.'),
(98620, 2018, 'bmc', 'Computational discovery of direct associations between GO terms and protein domains', 'Alborzi, Seyed Ziaeddin|Ritchie, David W|Devignes, Marie-Dominique', 'Université de Lorraine, CNRS, Inria, LORIA, Nancy, F-54500, France.|Université de Lorraine, CNRS, Inria, LORIA, Nancy, F-54500, France.|Université de Lorraine, CNRS, Inria, LORIA, Nancy, F-54500, France. marie-dominique.devignes@loria.fr.', 'France|France|France', 0, 'Algorithms|Amino Acid Sequence|Area Under Curve|Computational Biology|Databases, Protein|Gene Ontology|Molecular Sequence Annotation|Protein Domains|Proteins', 'Journal Article', '10.1186/s12859-018-2380-2', '30453875', 'Families of related proteins and their different functions may be described systematically using common classifications and ontologies such as Pfam and GO (Gene Ontology), for example. However, many proteins consist of multiple domains, and each domain, or some combination of domains, can be responsible for a particular molecular function. Therefore, identifying which domains should be associated with a specific function is a non-trivial task.'),
(98621, 2018, 'bmc', 'The predictive performance of short-linear motif features in the prediction of calmodulin-binding proteins', 'Li, Yixun|Maleki, Mina|Carruthers, Nicholas J|Stemmer, Paul M|Ngom, Alioune|Rueda, Luis', 'School of Computer Science, University of Windsor, Windsor, Ontario, Canada.|School of Computer Science, University of Windsor, Windsor, Ontario, Canada.|Inst. of Env. Health Sci., Wayne State University, Detroit, MI, USA.|Inst. of Env. Health Sci., Wayne State University, Detroit, MI, USA.|School of Computer Science, University of Windsor, Windsor, Ontario, Canada.|School of Computer Science, University of Windsor, Windsor, Ontario, Canada. lrueda@uwindsor.ca.', 'Canada|Canada|United States|United States|Canada|Canada', 3, 'Amino Acid Motifs|Amino Acid Sequence|Bayes Theorem|Calcium|Calmodulin-Binding Proteins|Computational Biology|Humans|Probability|Protein Structure, Quaternary|Reproducibility of Results|Support Vector Machine', 'Journal Article', '10.1186/s12859-018-2378-9', '30453876', 'The prediction of calmodulin-binding (CaM-binding) proteins plays a very important role in the fields of biology and biochemistry, because the calmodulin protein binds and regulates a multitude of protein targets affecting different cellular processes. Computational methods that can accurately identify CaM-binding proteins and CaM-binding domains would accelerate research in calcium signaling and calmodulin function. Short-linear motifs (SLiMs), on the other hand, have been effectively used as features for analyzing protein-protein interactions, though their properties have not been utilized in the prediction of CaM-binding proteins.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(98622, 2018, 'bmc', 'DNA sequences alignment in multi-GPUs: acceleration and energy payoff', 'Pérez-Serrano, Jesús|Sandes, Edans|Magalhaes Alves de Melo, Alba Cristina|Ujaldón, Manuel', 'Computer Architecture Department, University of Malaga, Malaga, Spain.|Computer Science Department, University of Brasilia, Brasilia, Brazil.|Computer Science Department, University of Brasilia, Brasilia, Brazil.|Computer Architecture Department, University of Malaga, Malaga, Spain. ujaldon@uma.es.', 'Spain|Brazil|Brazil|Spain', 2, 'Acceleration|Algorithms|Animals|Base Sequence|Computer Graphics|Electric Power Supplies|Humans|Pan troglodytes|Sequence Alignment|Time Factors', 'Journal Article', '10.1186/s12859-018-2389-6', '30453877', 'We present a performance per watt analysis of CUDAlign 4.0, a parallel strategy to obtain the optimal pairwise alignment of huge DNA sequences in multi-GPU platforms using the exact Smith-Waterman method.'),
(98623, 2018, 'bmc', 'Automated selection of homologs to track the evolutionary history of proteins', 'Mier, Pablo|Pérez-Pulido, Antonio J|Andrade-Navarro, Miguel A', 'Faculty of Biology, Johannes Gutenberg University Mainz, Hans-Dieter-Hüsch-Weg 15, 55128, Mainz, Germany. munoz@uni-mainz.de.|Universidad Pablo de Olavide, Sevilla, Spain.|Faculty of Biology, Johannes Gutenberg University Mainz, Hans-Dieter-Hüsch-Weg 15, 55128, Mainz, Germany.', 'Germany|Spain|Germany', 3, 'Databases, Protein|Evolution, Molecular|Humans|Proteins|Proteome|Software', 'Journal Article', '10.1186/s12859-018-2457-y', '30453878', 'The selection of distant homologs of a query protein under study is a usual and useful application of protein sequence databases. Such sets of homologs are often applied to investigate the function of a protein and the degree to which experimental results can be transferred from one organism to another. In particular, a variety of databases facilitates static browsing for orthologs. However, these resources have a limited power when identifying orthologs between taxonomically distant species. In addition, in some situations, for a given query protein, it is advantageous to compare the sets of orthologs from different specific organisms: this recursive step-wise search might give an idea of the evolutionary path of the protein as a series of consecutive steps, for example gaining or losing domains. However, a step-wise orthology search is a time-consuming task if the number of steps is high.'),
(98624, 2018, 'bmc', 'Evaluating the impact of topological protein features on the negative examples selection', 'Boldi, Paolo|Frasca, Marco|Malchiodi, Dario', 'Department of Computer Science, Università degli Studi di Milano, Via Comelico 39, Milano, 20135, Italy.|Department of Computer Science, Università degli Studi di Milano, Via Comelico 39, Milano, 20135, Italy. frasca@di.unimi.it.|Department of Computer Science, Università degli Studi di Milano, Via Comelico 39, Milano, 20135, Italy.', 'Italy|Italy|Italy', 2, 'Algorithms|Animals|Gene Ontology|Gene Regulatory Networks|Humans|Mice|Proteins|Proteome|Saccharomyces cerevisiae', 'Journal Article', '10.1186/s12859-018-2385-x', '30453879', 'Supervised machine learning methods when applied to the problem of automated protein-function prediction (AFP) require the availability of both positive examples (i.e., proteins which are known to possess a given protein function) and negative examples (corresponding to proteins not associated with that function). Unfortunately, publicly available proteome and genome data sources such as the Gene Ontology rarely store the functions not possessed by a protein. Thus the negative selection, consisting in identifying informative negative examples, is currently a central and challenging problem in AFP. Several heuristics have been proposed through the years to solve this problem, nevertheless, despite their effectiveness, to the best of our knowledge no previous existing work studied which protein features are more relevant to this task, that is, which protein features help more in discriminating reliable and unreliable negatives.'),
(98625, 2018, 'bmc', 'Comparing the performance of selected variant callers using synthetic data and genome segmentation', 'Bian, Xiaopeng|Zhu, Bin|Wang, Mingyi|Hu, Ying|Chen, Qingrong|Nguyen, Cu|Hicks, Belynda|Meerzaman, Daoud', 'Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD, 20850, USA. bianxi@mail.nih.gov.|Cancer Genomics Research Laboratory(CGR), Division of Cancer Epidemiology and Genetics, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, 8717 Grovemont Circle, Gaithersburg, MD, 20877, USA.|Cancer Genomics Research Laboratory(CGR), Division of Cancer Epidemiology and Genetics, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, 8717 Grovemont Circle, Gaithersburg, MD, 20877, USA.|Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD, 20850, USA.|Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD, 20850, USA.|Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD, 20850, USA.|Cancer Genomics Research Laboratory(CGR), Division of Cancer Epidemiology and Genetics, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, 8717 Grovemont Circle, Gaithersburg, MD, 20877, USA.|Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD, 20850, USA. meerzamd@mail.nih.gov.', 'United States|United States|United States|United States|United States|United States|United States|United States', 4, 'Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Molecular Sequence Annotation|Mutation|Polymorphism, Single Nucleotide|Precision Medicine', 'Journal Article', '10.1186/s12859-018-2440-7', '30453880', 'High-throughput sequencing has rapidly become an essential part of precision cancer medicine. But validating results obtained from analyzing and interpreting genomic data remains a rate-limiting factor. The gold standard, of course, remains manual validation by expert panels, which is not without its weaknesses, namely high costs in both funding and time as well as the necessarily selective nature of manual validation. But it may be possible to develop more economical, complementary means of validation. In this study we employed four synthetic data sets (variants with known mutations spiked into specific genomic locations) of increasing complexity to assess the sensitivity, specificity, and balanced accuracy of five open-source variant callers: FreeBayes v1.0, VarDict v11.5.1, MuTect v1.1.7, MuTect2, and MuSE v1.0rc. FreeBayes, VarDict, and MuTect were run in bcbio-next gen, and the results were integrated into a single Ensemble call set. The known mutations provided a level of ground truth against which we evaluated variant-caller performance. We further facilitated the comparison and evaluation by segmenting the whole genome into 10,000,000 base-pair fragments which yielded 316 segments.'),
(98626, 2018, 'bmc', 'Gene characteristics predicting missense, nonsense and frameshift mutations in tumor samples', 'Gorlov, Ivan P|Pikielny, Claudio W|Frost, Hildreth R|Her, Stephanie C|Cole, Michael D|Strohbehn, Samuel D|Wallace-Bradley, David|Kimmel, Marek|Gorlova, Olga Y|Amos, Christopher I', 'The Geisel School of Medicine, Department of Biomedical Data Science, Dartmouth College, HB7936, One Medical Center Dr., Dartmouth-Hitchcock Medical Center, Beirut, NH, 03756, Lebanon. ivan.p.gorlov@dartmouth.edu.|The Geisel School of Medicine, Department of Biomedical Data Science, Dartmouth College, HB7936, One Medical Center Dr., Dartmouth-Hitchcock Medical Center, Beirut, NH, 03756, Lebanon.|The Geisel School of Medicine, Department of Biomedical Data Science, Dartmouth College, HB7936, One Medical Center Dr., Dartmouth-Hitchcock Medical Center, Beirut, NH, 03756, Lebanon.|The Geisel School of Medicine, Department of Biomedical Data Science, Dartmouth College, HB7936, One Medical Center Dr., Dartmouth-Hitchcock Medical Center, Beirut, NH, 03756, Lebanon.|The Geisel School of Medicine, Department of Biomedical Data Science, Dartmouth College, HB7936, One Medical Center Dr., Dartmouth-Hitchcock Medical Center, Beirut, NH, 03756, Lebanon.|The Geisel School of Medicine, Department of Biomedical Data Science, Dartmouth College, HB7936, One Medical Center Dr., Dartmouth-Hitchcock Medical Center, Beirut, NH, 03756, Lebanon.|Department of Statistics, Rice University, M.S. 138, 6100 Main Street, Houston, TX, 77005, USA.|Department of Statistics, Rice University, M.S. 138, 6100 Main Street, Houston, TX, 77005, USA.|The Geisel School of Medicine, Department of Biomedical Data Science, Dartmouth College, HB7936, One Medical Center Dr., Dartmouth-Hitchcock Medical Center, Beirut, NH, 03756, Lebanon.|The Geisel School of Medicine, Department of Biomedical Data Science, Dartmouth College, HB7936, One Medical Center Dr., Dartmouth-Hitchcock Medical Center, Beirut, NH, 03756, Lebanon.', 'Lebanon|Lebanon|Lebanon|Lebanon|Lebanon|Lebanon|United States|United States|Lebanon|Lebanon', 2, 'Codon, Nonsense|Frameshift Mutation|Humans|Mutation Rate|Mutation, Missense|Neoplasm Proteins|Neoplasms', 'Journal Article', '10.1186/s12859-018-2455-0', '30453881', 'Because driver mutations provide selective advantage to the mutant clone, they tend to occur at a higher frequency in tumor samples compared to selectively neutral (passenger) mutations. However, mutation frequency alone is insufficient to identify cancer genes because mutability is influenced by many gene characteristics, such as size, nucleotide composition, etc. The goal of this study was to identify gene characteristics associated with the frequency of somatic mutations in the gene in tumor samples.'),
(98627, 2018, 'bmc', 'Automated shape-based clustering of 3D immunoglobulin protein structures in chronic lymphocytic leukemia', 'Polychronidou, Eleftheria|Kalamaras, Ilias|Agathangelidis, Andreas|Sutton, Lesley-Ann|Yan, Xiao-Jie|Bikos, Vasilis|Vardi, Anna|Mochament, Konstantinos|Chiorazzi, Nicholas|Belessi, Chrysoula|Rosenquist, Richard|Ghia, Paolo|Stamatopoulos, Kostas|Vlamos, Panayiotis|Chailyan, Anna|Overby, Nanna|Marcatili, Paolo|Hatzidimitriou, Anastasia|Tzovaras, Dimitrios', 'Information Technologies Institute, Centre for Research and Technology Hellas, 6th km Harilaou-Thermi Road, Thessaloniki, Greece. epolyc@iti.gr.|Information Technologies Institute, Centre for Research and Technology Hellas, 6th km Harilaou-Thermi Road, Thessaloniki, Greece.|Institute of Applied Biosciences, Centre for Research and Technology Hellas, 6th km Harilaou-Thermi Road, Thessaloniki, Greece.|Department of Immunology, Technical University of Denmark,Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden.|Karches Center for Chronic Lymphocytic Leukemia Research, The Feinstein Institute for Medical Research, Manhasset, NY, USA.|Masaryk University, Central European Institute of Technology, Brno, Czech Republic.|Hematology Department and HCT Unit, G. Papanikolaou Hospital, Thessaloniki, Greece.|Information Technologies Institute, Centre for Research and Technology Hellas, 6th km Harilaou-Thermi Road, Thessaloniki, Greece.|Karches Center for Chronic Lymphocytic Leukemia Research, The Feinstein Institute for Medical Research, Manhasset, NY, USA.|Nikea General Hospital, Hematology Department, Piraeus, Greece.|Department of Immunology, Technical University of Denmark,Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden.|IRCCS San Raffaele Scientific Institute and Università, VitaSalute, San Raffaele, Division of Experimental Oncology, Milan, Italy.|Institute of Applied Biosciences, Centre for Research and Technology Hellas, 6th km Harilaou-Thermi Road, Thessaloniki, Greece.|Department of Informatics,Ionian University, Corfu, Greece.|Carlsberg Research Laboratory, Copenhagen, Denmark.|Center for Biological Sequence Analysis, Technical University of Denmark, Copenhagen, Denmark.|Center for Biological Sequence Analysis, Technical University of Denmark, Copenhagen, Denmark.|Institute of Applied Biosciences, Centre for Research and Technology Hellas, 6th km Harilaou-Thermi Road, Thessaloniki, Greece.|Information Technologies Institute, Centre for Research and Technology Hellas, 6th km Harilaou-Thermi Road, Thessaloniki, Greece.', 'Greece|Greece|Greece|Denmark|United States|United States|Greece|Greece|United States|Greece|Denmark|Italy|Greece|Greece|Denmark|Denmark|', 4, 'Amino Acid Sequence|Automation|Databases, Protein|Humans|Imaging, Three-Dimensional|Immunoglobulins|Leukemia, Lymphocytic, Chronic, B-Cell|Molecular Sequence Annotation', 'Journal Article', '10.1186/s12859-018-2381-1', '30453883', 'Although the etiology of chronic lymphocytic leukemia (CLL), the most common type of adult leukemia, is still unclear, strong evidence implicates antigen involvement in disease ontogeny and evolution. Primary and 3D structure analysis has been utilised in order to discover indications of antigenic pressure. The latter has been mostly based on the 3D models of the clonotypic B cell receptor immunoglobulin (BcR IG) amino acid sequences. Therefore, their accuracy is directly dependent on the quality of the model construction algorithms and the specific methods used to compare the ensuing models. Thus far, reliable and robust methods that can group the IG 3D models based on their structural characteristics are missing.'),
(98628, 2018, 'bmc', 'SVM-RFE: selection and visualization of the most relevant features through non-linear kernels', 'Sanz, Hector|Valim, Clarissa|Vegas, Esteban|Oller, Josep M|Reverter, Ferran', 'Department of Genetics, Microbiology and Statistics, Faculty of Biology, Universitat de Barcelona, Diagonal, 643, 08028, Barcelona, Catalonia, Spain. hsrodenas@gmail.com.|Department of Osteopathic Medical Specialties, Michigan State University, 909 Fee Road, Room B 309 West Fee Hall, East Lansing, MI, 48824, USA.|Department of Genetics, Microbiology and Statistics, Faculty of Biology, Universitat de Barcelona, Diagonal, 643, 08028, Barcelona, Catalonia, Spain.|Department of Genetics, Microbiology and Statistics, Faculty of Biology, Universitat de Barcelona, Diagonal, 643, 08028, Barcelona, Catalonia, Spain.|Department of Genetics, Microbiology and Statistics, Faculty of Biology, Universitat de Barcelona, Diagonal, 643, 08028, Barcelona, Catalonia, Spain.', 'Spain|United States|Spain|Spain|Spain', 0, 'Algorithms|Biomarkers, Tumor|Computer Graphics|Humans|Liver Cirrhosis, Biliary|Lung Neoplasms|Lymphoma, Large B-Cell, Diffuse|Support Vector Machine|Survival Rate', 'Journal Article', '10.1186/s12859-018-2451-4', '30453885', 'Support vector machines (SVM) are a powerful tool to analyze data with a number of predictors approximately equal or larger than the number of observations. However, originally, application of SVM to analyze biomedical data was limited because SVM was not designed to evaluate importance of predictor variables. Creating predictor models based on only the most relevant variables is essential in biomedical research. Currently, substantial work has been done to allow assessment of variable importance in SVM models but this work has focused on SVM implemented with linear kernels. The power of SVM as a prediction model is associated with the flexibility generated by use of non-linear kernels. Moreover, SVM has been extended to model survival outcomes. This paper extends the Recursive Feature Elimination (RFE) algorithm by proposing three approaches to rank variables based on non-linear SVM and SVM for survival analysis.'),
(98629, 2018, 'bmc', 'Flexible docking-based molecular dynamics simulation of natural product compounds and Ebola virus Nucleocapsid (EBOV NP): a computational approach to discover new drug for combating Ebola', 'Nasution, Mochammad Arfin Fardiansyah|Toepak, Erwin Prasetya|Alkaff, Ahmad Husein|Tambunan, Usman Sumo Friend', 'Bioinformatics Research Group, Department of Chemistry, Faculty of Mathematics and Natural Science, Universitas Indonesia, Kampus UI, Depok, 16424, Indonesia.|Bioinformatics Research Group, Department of Chemistry, Faculty of Mathematics and Natural Science, Universitas Indonesia, Kampus UI, Depok, 16424, Indonesia.|Bioinformatics Research Group, Department of Chemistry, Faculty of Mathematics and Natural Science, Universitas Indonesia, Kampus UI, Depok, 16424, Indonesia.|Bioinformatics Research Group, Department of Chemistry, Faculty of Mathematics and Natural Science, Universitas Indonesia, Kampus UI, Depok, 16424, Indonesia. usman@ui.ac.id.', 'Indonesia|Indonesia|Indonesia|Indonesia', 7, 'Biological Products|Carcinogenicity Tests|Drug Discovery|Ebolavirus|Hemorrhagic Fever, Ebola|Humans|Ligands|Molecular Docking Simulation|Molecular Dynamics Simulation|Mutagenicity Tests|Nucleocapsid|Thermodynamics', 'Journal Article', '10.1186/s12859-018-2387-8', '30453886', 'Ebola still remains as one of the most problematic infectious diseases in Africa with a high rate of mortality. Although this disease has been known for an almost half-century, there are no vaccines and drugs available in the market to treat Ebola. Zaire ebolavirus (EBOV), a single-stranded RNA virus which belongs to Filoviridae family and Mononegavirales order, is one of the virus causing Ebola. As one of seven proteins that EBOV encodes, Ebola virus nucleoprotein (EBOV NP) plays an imperative role in EBOV proliferation cycle. Therefore, the development of a new Ebola treatment can be targeted towards EBOV NP.'),
(98630, 2018, 'bmc', 'Deep learning architectures for prediction of nucleosome positioning from sequences data', 'Di Gangi, Mattia|Lo Bosco, Giosuè|Rizzo, Riccardo', 'Fondazione Bruno Kessler, Via Sommarive, 18, Trento, 38123, Italy.|Dipartimento di Matematica e Informatica, Università degli studi di Palermo, Via Archirafi, 34, Palermo, 90123, Italy. giosue.lobosco@unipa.it.|CNR-ICAR, National Research Council of Italy, Via Ugo La Malfa, 153, Palermo, 90146, Italy.', 'Italy|Italy|Italy', 0, 'Animals|Base Sequence|Databases, Nucleic Acid|Deep Learning|Humans|Neural Networks (Computer)|Nucleosomes|ROC Curve|Reproducibility of Results|Saccharomyces cerevisiae', 'Journal Article', '10.1186/s12859-018-2386-9', '30453896', 'Nucleosomes are DNA-histone complex, each wrapping about 150 pairs of double-stranded DNA. Their function is fundamental for one of the primary functions of Chromatin i.e. packing the DNA into the nucleus of the Eukaryote cells. Several biological studies have shown that the nucleosome positioning influences the regulation of cell type-specific gene activities. Moreover, computational studies have shown evidence of sequence specificity concerning the DNA fragment wrapped into nucleosomes, clearly underlined by the organization of particular DNA substrings. As the main consequence, the identification of nucleosomes on a genomic scale has been successfully performed by computational methods using a sequence features representation.'),
(98631, 2018, 'bmc', 'TMFUF: a triple matrix factorization-based unified framework for predicting comprehensive drug-drug interactions of new drugs', 'Shi, Jian-Yu|Huang, Hua|Li, Jia-Xin|Lei, Peng|Zhang, Yan-Ning|Dong, Kai|Yiu, Siu-Ming', 'School of Life Sciences, Northwestern Polytechnical University, Xian, China. jianyushi@nwpu.edu.cn.|School of Software and Microelectronics, Northwestern Polytechnical University, Xian, China.|School of Life Sciences, Northwestern Polytechnical University, Xian, China.|Department of Chinese Medicine, Shaanxi Provincial Peoples Hospital, Xian, China.|School of Computer Science, Northwestern Polytechnical University, Xian, China.|School of Life Sciences, Northwestern Polytechnical University, Xian, China.|Department of Computer Science, the University of Hong Kong, Hong Kong, China. smyiu@cs.hku.hk.', 'China|China|China|China|China|China|China', 6, 'Algorithms|Drug Interactions|Humans|Least-Squares Analysis|ROC Curve|Reproducibility of Results', 'Journal Article', '10.1186/s12859-018-2379-8', '30453924', 'A significant number of adverse drug reactions is caused by unexpected Drug-drug interactions (DDIs). The identification of DDIs becomes crucial before the co-prescription of multiple drugs is made. Such a task in clinics or in drug discovery usually requires high costs and numerous limitations, while computational approaches are able to predict potential DDIs effectively by utilizing diverse drug attributes (e.g. side effects). Nevertheless, theyre incapable when required to predict enhancive and degressive DDIs, which change increasingly and decreasingly the pharmacological behavior of interacting drugs respectively. The pharmacological change of DDIs is one of the most important factors when making a multi-drug prescription.'),
(98632, 2018, 'bmc', 'Insights into mammalian transcription control by systematic analysis of ChIP sequencing data', 'Devailly, Guillaume|Joshi, Anagha', 'Division of Developmental Biology, the Roslin Institute, University of Edinburgh, Easter Bush Campus, Midlothian, EH25 9RG, UK.|Division of Developmental Biology, the Roslin Institute, University of Edinburgh, Easter Bush Campus, Midlothian, EH25 9RG, UK. anagha.joshi@roslin.ed.ac.uk.', 'United Kingdom|United Kingdom', 0, 'Animals|Base Sequence|Chromatin Immunoprecipitation|Data Analysis|Gene Expression Regulation|Genetic Loci|Genetic Predisposition to Disease|Genome|Humans|Mammals|Mice|Nucleotide Motifs|Promoter Regions, Genetic|Sequence Analysis, DNA|Transcription Factors|Transcription Initiation Site|Transcription, Genetic', 'Journal Article', '10.1186/s12859-018-2377-x', '30453943', 'Transcription regulation is a major controller of gene expression dynamics during development and disease, where transcription factors (TFs) modulate expression of genes through direct or indirect DNA interaction. ChIP sequencing has become the most widely used technique to get a genome wide view of TF occupancy in a cell type of interest, mainly due to established standard protocols and a rapid decrease in the cost of sequencing. The number of available ChIP sequencing data sets in public domain is therefore ever increasing, including data generated by individual labs together with consortia such as the ENCODE project.'),
(98633, 2018, 'bmc', 'Computational prediction of inter-species relationships through omics data analysis and machine learning', 'Leite, Diogo Manuel Carvalho|Brochet, Xavier|Resch, Grégory|Que, Yok-Ai|Neves, Aitana|Peña-Reyes, Carlos', 'School of Business and Engineering Vaud (HEIG-VD), University of Applied Sciences Western Switzerland (HES-SO), Route. de Cheseaux 1, Yverdon-Les-Bains, 1400, Switzerland.|School of Business and Engineering Vaud (HEIG-VD), University of Applied Sciences Western Switzerland (HES-SO), Route. de Cheseaux 1, Yverdon-Les-Bains, 1400, Switzerland.|Department of Fundamental Microbiology, University of Lausanne, Lausanne, 1015, Switzerland.|Department of Intensive Care Medicine, Bern University Hospital (Inselspital), Freiburgstrasse, Bern, 3010, Switzerland.|SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.|School of Business and Engineering Vaud (HEIG-VD), University of Applied Sciences Western Switzerland (HES-SO), Route. de Cheseaux 1, Yverdon-Les-Bains, 1400, Switzerland. carlos.pena@heig-vd.ch.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 6, 'Algorithms|Bacteria|Bacteriophages|Computational Biology|Data Analysis|Genomics|Machine Learning|Proteins|Species Specificity', 'Journal Article', '10.1186/s12859-018-2388-7', '30453987', 'Antibiotic resistance and its rapid dissemination around the world threaten the efficacy of currently-used medical treatments and call for novel, innovative approaches to manage multi-drug resistant infections. Phage therapy, i.e., the use of viruses (phages) to specifically infect and kill bacteria during their life cycle, is one of the most promising alternatives to antibiotics. It is based on the correct matching between a target pathogenic bacteria and the therapeutic phage. Nevertheless, correctly matching them is a major challenge. Currently, there is no systematic method to efficiently predict whether phage-bacterium interactions exist and these pairs must be empirically tested in laboratory. Herein, we present our approach for developing a computational model able to predict whether a given phage-bacterium pair can interact based on their genome.'),
(98634, 2018, 'bmc', 'ASGAL: aligning RNA-Seq data to a splicing graph to detect novel alternative splicing events', 'Denti, Luca|Rizzi, Raffaella|Beretta, Stefano|Vedova, Gianluca Della|Previtali, Marco|Bonizzoni, Paola', 'Department of Informatics, Systems, and Communication, University of Milano - Bicocca, Milan, Italy.|Department of Informatics, Systems, and Communication, University of Milano - Bicocca, Milan, Italy.|Department of Informatics, Systems, and Communication, University of Milano - Bicocca, Milan, Italy.|Department of Informatics, Systems, and Communication, University of Milano - Bicocca, Milan, Italy.|Department of Informatics, Systems, and Communication, University of Milano - Bicocca, Milan, Italy.|Department of Informatics, Systems, and Communication, University of Milano - Bicocca, Milan, Italy. bonizzoni@disco.unimib.it.', 'Italy|Italy|Italy|Italy|Italy|Italy', 5, 'Alternative Splicing|Humans|RNA|RNA Splicing|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-018-2436-3', '30458725', 'While the reconstruction of transcripts from a sample of RNA-Seq data is a computationally expensive and complicated task, the detection of splicing events from RNA-Seq data and a gene annotation is computationally feasible. This latter task, which is adequate for many transcriptome analyses, is usually achieved by aligning the reads to a reference genome, followed by comparing the alignments with a gene annotation, often implicitly represented by a graph: the splicing graph.'),
(98635, 2018, 'bmc', 'Drug repositioning using drug-disease vectors based on an integrated network', 'Lee, Taekeon|Yoon, Youngmi', 'Department of Computer Engineering, Gachon University, 5-22Ho, IT college, 1324 Seongnam-daero, Seongnam-si, 13120, South Korea.|Department of Computer Engineering, Gachon University, 5-22Ho, IT college, 1324 Seongnam-daero, Seongnam-si, 13120, South Korea. ymyoon@gachon.ac.kr.', '-|-', 0, 'Animals|Computational Biology|Disease Vectors|Drug Repositioning|Gene Expression Regulation|Gene Regulatory Networks|Humans', 'Journal Article', '10.1186/s12859-018-2490-x', '30463505', 'Diverse interactions occur between biomolecules, such as activation, inhibition, expression, or repression. However, previous network-based studies of drug repositioning have employed interaction on the binary protein-protein interaction (PPI) network without considering the characteristics of the interactions. Recently, some studies of drug repositioning using gene expression data found that associations between drug and disease genes are useful information for identifying novel drugs to treat diseases. However, the gene expression profiles for drugs and diseases are not always available. Although gene expression profiles of drugs and diseases are available, existing methods cannot use the drugs or diseases, when differentially expressed genes in the profiles are not included in their network.'),
(98636, 2018, 'bmc', 'A statistical method to identify recombination in bacterial genomes based on SNP incompatibility', 'Lai, Yi-Pin|Ioerger, Thomas R', 'Department of Computer Science & Engineering, Texas A&M University, College Station, TX 77843, USA.|Department of Computer Science & Engineering, Texas A&M University, College Station, TX 77843, USA. ioerger@cs.tamu.edu.', 'United States|United States', 1, 'Genome, Bacterial|Phylogeny|Polymorphism, Single Nucleotide|Recombination, Genetic', 'Journal Article', '10.1186/s12859-018-2456-z', '30466385', 'Phylogeny estimation for bacteria is likely to reflect their true evolutionary histories only if they are highly clonal. However, recombination events could occur during evolution for some species. The reconstruction of phylogenetic trees from an alignment without considering recombination could be misleading, since the relationships among strains in some parts of the genome might be different than in others. Using a single, global tree can create the appearance of homoplasy in recombined regions. Hence, the identification of recombination breakpoints is essential to better understand the evolutionary relationships of isolates among a bacterial population.'),
(98637, 2018, 'bmc', 'knnAUC: an open-source R package for detecting nonlinear dependence between one continuous variable and one binary variable', 'Li, Yi|Liu, Xiaoyu|Ma, Yanyun|Wang, Yi|Zhou, Weichen|Hao, Meng|Yuan, Zhenghong|Liu, Jie|Xiong, Momiao|Shugart, Yin Yao|Wang, Jiucun|Jin, Li', 'Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Fudan University, Shanghai, China.|Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Fudan University, Shanghai, China.|Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Fudan University, Shanghai, China.|Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Fudan University, Shanghai, China.|State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China.|Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Fudan University, Shanghai, China.|Shanghai Public Health Clinical Center, Fudan University, Shanghai, China.|Key Laboratory of Medical Molecular Virology of MOE/MOH, Shanghai Medical School, Fudan University, Shanghai, China.|Human Genetics Center, School of Public Health, University of Texas Houston Health Sciences Center, Houston, TX, USA.|Unit on Statistical Genomics, Division of Intramural Division Programs, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA. yinyao21043@gmail.com.|Six Industrial Research Institute, Fudan University, Shanghai, China. jcwang@fudan.edu.cn.|Six Industrial Research Institute, Fudan University, Shanghai, China. lijin@fudan.edu.cn.', 'China|China|China|China|China|China|China|China|United States|United States|China|China', 0, 'Cluster Analysis|Computational Biology|Humans|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-018-2427-4', '30466390', 'Testing the dependence of two variables is one of the fundamental tasks in statistics. In this work, we developed an open-source R package (knnAUC) for detecting nonlinear dependence between one continuous variable X and one binary dependent variables Y (0 or 1).'),
(98638, 2018, 'bmc', 'PCRdrive: the largest qPCR assay archive to date and endless potential for lab workflow revitalization', 'Burger, Florian|Angioni, Michele|Russo, Gianluca|Schad, Martina|Kallarackal, Jim', 'OakLabs GmbH, Neuendorfstr. 16B, Hennigsdorf, 16761, Germany.|OakLabs GmbH, Neuendorfstr. 16B, Hennigsdorf, 16761, Germany.|OakLabs GmbH, Neuendorfstr. 16B, Hennigsdorf, 16761, Germany.|OakLabs GmbH, Neuendorfstr. 16B, Hennigsdorf, 16761, Germany.|OakLabs GmbH, Neuendorfstr. 16B, Hennigsdorf, 16761, Germany. kallarackal@oak-labs.com.', 'Germany|Germany|Germany|Germany|Germany', 0, 'DNA Primers|Real-Time Polymerase Chain Reaction|Workflow', 'Journal Article', '10.1186/s12859-018-2452-3', '30466398', 'Primer design is a crucial step in establishing specific and sensitive qPCR assays. Even though numerous tools for primer design exist, the majority of resulting assays still requires extensive testing and optimisation or does not allow for high quality target amplification. We developed a workflow for designing qPCR assays. Unlike other tools, we compute a PCR assay including primer design, concentrations and the optimal PCR program.'),
(98639, 2018, 'bmc', 'EpiViewer: an epidemiological application for exploring time series data', 'Thorve, Swapna|Wilson, Mandy L|Lewis, Bryan L|Swarup, Samarth|Vullikanti, Anil Kumar S|Marathe, Madhav V', 'Department of Computer Science, Virginia Tech, Blacksburg, Virginia, USA.|Biocomplexity Institute, University of Virginia, Charlottesville, Virginia, USA.|Biocomplexity Institute, University of Virginia, Charlottesville, Virginia, USA.|Biocomplexity Institute, University of Virginia, Charlottesville, Virginia, USA.|Department of Computer Science, University of Virginia, Charlottesville, Virginia, USA.|Department of Computer Science, University of Virginia, Charlottesville, Virginia, USA. mvm7hz@virginia.edu.', 'United States|United States|United States|United States|United States|United States', 0, 'Humans|Information Dissemination|Software', 'Journal Article', '10.1186/s12859-018-2439-0', '30466409', 'Visualization plays an important role in epidemic time series analysis and forecasting. Viewing time series data plotted on a graph can help researchers identify anomalies and unexpected trends that could be overlooked if the data were reviewed in tabular form, these details can influence a researchers recommended course of action or choice of simulation models. However, there are challenges in reviewing data sets from multiple data sources - data can be aggregated in different ways (e.g., incidence vs. cumulative), measure different criteria (e.g., infection counts, hospitalizations, and deaths), or represent different geographical scales (e.g., nation, HHS Regions, or states), which can make a direct comparison between time series difficult. In the face of an emerging epidemic, the ability to visualize time series from various sources and organizations and to reconcile these datasets based on different criteria could be key in developing accurate forecasts and identifying effective interventions. Many tools have been developed for visualizing temporal data, however, none yet supports all the functionality needed for easy collaborative visualization and analysis of epidemic data.'),
(98640, 2018, 'bmc', 'pyHIVE, a health-related image visualization and engineering system using Python', 'Zhang, Ruochi|Zhao, Ruixue|Zhao, Xinyang|Wu, Di|Zheng, Weiwei|Feng, Xin|Zhou, Fengfeng', 'BioKnow Health Informatics Lab, College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, Jilin, China.|BioKnow Health Informatics Lab, College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, Jilin, China.|BioKnow Health Informatics Lab, College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, Jilin, China.|BioKnow Health Informatics Lab, College of Software, Jilin University, Changchun, 130012, Jilin, China.|BioKnow Health Informatics Lab, College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, Jilin, China.|BioKnow Health Informatics Lab, College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, Jilin, China. 573175490@qq.com.|BioKnow Health Informatics Lab, College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, Jilin, China. FengfengZhou@gmail.com.', 'China|China|China|China|China|China|China', 3, 'Algorithms|Endoscopy, Gastrointestinal|Humans|Image Processing, Computer-Assisted|Principal Component Analysis|Programming Languages', 'Journal Article', '10.1186/s12859-018-2477-7', '30477418', 'Imaging is one of the major biomedical technologies to investigate the status of a living object. But the biomedical image based data mining problem requires extensive knowledge across multiple disciplinaries, e.g. biology, mathematics and computer science, etc. RESULTS: pyHIVE (a Health-related Image Visualization and Engineering system using Python) was implemented as an image processing system, providing five widely used image feature engineering algorithms. A standard binary classification pipeline was also provided to help researchers build data models immediately after the data is collected. pyHIVE may calculate five widely-used image feature engineering algorithms efficiently using multiple computing cores, and also featured the modules of Principal Component Analysis (PCA) based preprocessing and normalization.'),
(98641, 2018, 'bmc', 'COMBImage: a modular parallel processing framework for pairwise drug combination analysis that quantifies temporal changes in label-free video microscopy movies', '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 Immunology, Genetics and Pathology, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden.|Department of Medical Sciences, Cancer Pharmacology and Computational Medicine, Uppsala University, Uppsala, Sweden.', 'Sweden|Sweden|Sweden|Sweden|Sweden', 2, 'Algorithms|Antineoplastic Combined Chemotherapy Protocols|Brain Neoplasms|Cell Survival|Drug Discovery|Drug Therapy, Combination|Glioblastoma|Humans|Image Processing, Computer-Assisted|Microscopy, Video|Motion Pictures', 'Journal Article', '10.1186/s12859-018-2458-x', '30477419', 'Large-scale pairwise drug combination analysis has lately gained momentum in drug discovery and development projects, mainly due to the employment of advanced experimental-computational pipelines. This is fortunate as drug combinations are often required for successful treatment of complex diseases. Furthermore, most new drugs cannot totally replace the current standard-of-care medication, but rather have to enter clinical use as add-on treatment. However, there is a clear deficiency of computational tools for label-free and temporal image-based drug combination analysis that go beyond the conventional but relatively uninformative end point measurements.'),
(98642, 2018, 'bmc', 'SMuRF: a novel tool to identify regulatory elements enriched for somatic point mutations', 'Guilhamon, Paul|Lupien, Mathieu', 'Princess Margaret Cancer Centre, The MaRS Center, University Health Network, 101 College Street, Toronto, ON, M5G 1L7, Canada.|Princess Margaret Cancer Centre, The MaRS Center, University Health Network, 101 College Street, Toronto, ON, M5G 1L7, Canada. Mathieu.Lupien@uhnresearch.ca.', 'Canada|Canada', 1, 'Genomics|Humans|Liver Neoplasms|Point Mutation|Regulatory Sequences, Nucleic Acid|Software', 'Journal Article', '10.1186/s12859-018-2501-y', '30477433', 'Single Nucleotide Variants (SNVs), including somatic point mutations and Single Nucleotide Polymorphisms (SNPs), in noncoding cis-regulatory elements (CREs) can affect gene regulation and lead to disease development. Several approaches have been developed to identify highly mutated regions, but these do not take into account the specific genomic context, and thus likelihood of mutation, of CREs.'),
(98643, 2018, 'bmc', 'Coding Pronys method in MATLAB and applying it to biomedical signal filtering', 'Fernxc3xa1ndez Rodrxc3xadguez, A|de Santiago Rodrigo, L|Lxc3xb3pez Guillxc3xa9n, E|Rodrxc3xadguez Ascariz, J M|Miguel Jimxc3xa9nez, J M|Boquete, Luciano', 'Grupo de Ingenierxc3xada Biomxc3xa9dica, Departamento de Electrxc3xb3nica, Universidad de Alcalxc3xa1, Plaza de S. Diego, s/n, 28801, Alcalxc3xa1 de Henares, Spain.|Grupo de Ingenierxc3xada Biomxc3xa9dica, Departamento de Electrxc3xb3nica, Universidad de Alcalxc3xa1, Plaza de S. Diego, s/n, 28801, Alcalxc3xa1 de Henares, Spain.|Grupo de Ingenierxc3xada Biomxc3xa9dica, Departamento de Electrxc3xb3nica, Universidad de Alcalxc3xa1, Plaza de S. Diego, s/n, 28801, Alcalxc3xa1 de Henares, Spain.|Grupo de Ingenierxc3xada Biomxc3xa9dica, Departamento de Electrxc3xb3nica, Universidad de Alcalxc3xa1, Plaza de S. Diego, s/n, 28801, Alcalxc3xa1 de Henares, Spain.|Grupo de Ingenierxc3xada Biomxc3xa9dica, Departamento de Electrxc3xb3nica, Universidad de Alcalxc3xa1, Plaza de S. Diego, s/n, 28801, Alcalxc3xa1 de Henares, Spain.|Grupo de Ingenierxc3xada Biomxc3xa9dica, Departamento de Electrxc3xb3nica, Universidad de Alcalxc3xa1, Plaza de S. Diego, s/n, 28801, Alcalxc3xa1 de Henares, Spain. luciano.boquete@uah.es.', 'Spain|Spain|Spain|Spain|Spain|Spain', 0, 'Adult|Algorithms|Evoked Potentials, Visual|Female|Fourier Analysis|Humans|Least-Squares Analysis|Male|Multiple Sclerosis|Programming Languages|Young Adult', 'Journal Article', '10.1186/s12859-018-2473-y', '30477444', 'The response of many biomedical systems can be modelled using a linear combination of damped exponential functions. The approximation parameters, based on equally spaced samples, can be obtained using Pronys method and its variants (e.g. the matrix pencil method). This paper provides a tutorial on the main polynomial Prony and matrix pencil methods and their implementation in MATLAB and analyses how they perform with synthetic and multifocal visual-evoked potential (mfVEP) signals. This paper briefly describes the theoretical basis of four polynomial Prony approximation methods: classic, least squares (LS), total least squares (TLS) and matrix pencil method (MPM). In each of these cases, implementation uses general MATLAB functions. The features of the various options are tested by approximating a set of synthetic mathematical functions and evaluating filtering performance in the Prony domain when applied to mfVEP signals to improve diagnosis of patients with multiple sclerosis (MS).'),
(98644, 2018, 'bmc', 'dbMPIKT: a database of kinetic and thermodynamic mutant protein interactions', 'Liu, Quanya|Chen, Peng|Wang, Bing|Zhang, Jun|Li, Jinyan', 'Institute of Physical Science and Information Technology, Anhui University, Hefei, 230601, Anhui, China.|Institute of Physical Science and Information Technology, Anhui University, Hefei, 230601, Anhui, China. pchen.ustc10@yahoo.com.|School of Electrical and Information Engineering, Anhui University of Technology, Maanshan, 243032, Anhui, China.|School of Electronic Engineering & Automation, Anhui University, Hefei, 230601, Anhui, China.|Advanced Analytics Institute and Centre for Health Technologies, University of Technology, Broadway, Sydney, NSW, 2007, Australia.', 'China|China|China|China|Australia', 6, 'Databases, Protein|Internet|Kinetics|Mutant Proteins|Mutation|Protein Binding|Protein Interaction Maps|Thermodynamics', 'Journal Article', '10.1186/s12859-018-2493-7', '30482172', 'Protein-protein interactions (PPIs) play important roles in biological functions. Studies of the effects of mutants on protein interactions can provide further understanding of PPIs. Currently, many databases collect experimental mutants to assess protein interactions, but most of these databases are old and have not been updated for several years.'),
(98645, 2018, 'bmc', 'Correction to: cuRnet: an R package for graph traversing on GPU', 'Bonnici, Vincenzo|Busato, Federico|Aldegheri, Stefano|Akhmedov, Murodzhon|Cascione, Luciano|Carmena, Alberto Arribas|Bertoni, Francesco|Bombieri, Nicola|Kwee, Ivo|Giugno, Rosalba', 'Department of Computer Science, University of Verona, Strada le Grazie, 15, Verona, Italy.|Department of Computer Science, University of Verona, Strada le Grazie, 15, Verona, Italy.|Department of Computer Science, University of Verona, Strada le Grazie, 15, Verona, Italy.|Institute of Oncology Research (IOR), Via Vincenzo Vela 6, Bellinzona, Switzerland.|Institute of Oncology Research (IOR), Via Vincenzo Vela 6, Bellinzona, Switzerland.|Institute of Oncology Research (IOR), Via Vincenzo Vela 6, Bellinzona, Switzerland.|Institute of Oncology Research (IOR), Via Vincenzo Vela 6, Bellinzona, Switzerland.|Department of Computer Science, University of Verona, Strada le Grazie, 15, Verona, Italy.|Institute of Oncology Research (IOR), Via Vincenzo Vela 6, Bellinzona, Switzerland.|Department of Computer Science, University of Verona, Strada le Grazie, 15, Verona, Italy. rosalba.giugno@univr.it.', 'Italy|Italy|Italy|Switzerland|Switzerland|Switzerland|Switzerland|Italy|Switzerland|Italy', 0, '-', 'Journal Article', '10.1186/s12859-018-2484-8', '30482173', 'After publication of this supplement article [1], it was brought to our attention that reference 10 and reference 12 in the article are incorrect.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(98646, 2018, 'bmc', 'Developing reproducible bioinformatics analysis workflows for heterogeneous computing environments to support African genomics', 'Baichoo, Shakuntala|Souilmi, Yassine|Panji, Sumir|Botha, Gerrit|Meintjes, Ayton|Hazelhurst, Scott|Bendou, Hocine|Beste, Eugene de|Mpangase, Phelelani T|Souiai, Oussema|Alghali, Mustafa|Yi, Long|OConnor, Brian D|Crusoe, Michael|Armstrong, Don|Aron, Shaun|Joubert, Fourie|Ahmed, Azza E|Mbiyavanga, Mamana|Heusden, Peter van|Magosi, Lerato E|Zermeno, Jennie|Mainzer, Liudmila Sergeevna|Fadlelmola, Faisal M|Jongeneel, C Victor|Mulder, Nicola', 'Department of Digital Technologies, University of Mauritius, Reduit, Mauritius. shakunb@uom.ac.mu.|Australian Centre for Ancient DNA, University of Adelaide, Adelaide, South Australia, Australia.|Computational Biology Division, Department of Integrative Medical Biosciences, IDM, University of Cape Town, Cape Town, South Africa.|Computational Biology Division, Department of Integrative Medical Biosciences, IDM, University of Cape Town, Cape Town, South Africa.|Computational Biology Division, Department of Integrative Medical Biosciences, IDM, University of Cape Town, Cape Town, South Africa.|School of Electrical & Information Engineering, University of the Witwatersrand, Johannesburg, South Africa.|South African National Bioinformatics Institute, University of the Western Cape, Bellville, Cape Town, South Africa.|South African National Bioinformatics Institute, University of the Western Cape, Bellville, Cape Town, South Africa.|Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa.|Institut Pasteur De Tunis, University Tunis El manar, Tunis, Tunisia.|Center for Bioinformatics & Systems Biology, Faculty of Science, University of Khartoum, Khartoum, Sudan.|South African National Bioinformatics Institute, University of the Western Cape, Bellville, Cape Town, South Africa.|Genomics Institute, University of California, Santa Cruz, California, USA.|Common Workflow Language project, Software Freedom Conservancy, New York City, NY, USA.|Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.|Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa.|Centre for Bioinformatics and Computational Biology, Department of Biochemistry, Genetics and Microbiology, University of Pretoria, Pretoria, South Africa.|Center for Bioinformatics & Systems Biology, Faculty of Science, University of Khartoum, Khartoum, Sudan.|Computational Biology Division, Department of Integrative Medical Biosciences, IDM, University of Cape Town, Cape Town, South Africa.|South African National Bioinformatics Institute, University of the Western Cape, Bellville, Cape Town, South Africa.|Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.|National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.|Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.|Center for Bioinformatics & Systems Biology, Faculty of Science, University of Khartoum, Khartoum, Sudan.|Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.|Computational Biology Division, Department of Integrative Medical Biosciences, IDM, University of Cape Town, Cape Town, South Africa.', 'Mauritius|Australia|South Africa|South Africa|South Africa|South Africa|South Africa|South Africa|So', 4, 'Africa|Computational Biology|Genomics|Humans|Reproducibility of Results', 'Journal Article', '10.1186/s12859-018-2446-1', '30486782', 'The Pan-African bioinformatics network, H3ABioNet, comprises 27 research institutions in 17 African countries. H3ABioNet is part of the Human Health and Heredity in Africa program (H3Africa), an African-led research consortium funded by the US National Institutes of Health and the UK Wellcome Trust, aimed at using genomics to study and improve the health of Africans. A key role of H3ABioNet is to support H3Africa projects by building bioinformatics infrastructure such as portable and reproducible bioinformatics workflows for use on heterogeneous African computing environments. Processing and analysis of genomic data is an example of a big data application requiring complex interdependent data analysis workflows. Such bioinformatics workflows take the primary and secondary input data through several computationally-intensive processing steps using different software packages, where some of the outputs form inputs for other steps. Implementing scalable, reproducible, portable and easy-to-use workflows is particularly challenging.'),
(98647, 2018, 'bmc', 'PanDelos: a dictionary-based method for pan-genome content discovery', 'Bonnici, Vincenzo|Giugno, Rosalba|Manca, Vincenzo', 'Department of Computer Science, University of Verona, Strada le Grazie, 15, Verona, 37134, Italy. vincenzo.bonnici@univr.it.|Department of Computer Science, University of Verona, Strada le Grazie, 15, Verona, 37134, Italy.|Department of Computer Science, University of Verona, Strada le Grazie, 15, Verona, 37134, Italy.', 'Italy|Italy|Italy', 0, 'Bacteria|Databases, Genetic|Dictionaries as Topic|Gene Duplication|Genome, Bacterial|Phylogeny|Software|Time Factors', 'Journal Article', '10.1186/s12859-018-2417-6', '30497358', 'Pan-genome approaches afford the discovery of homology relations in a set of genomes, by determining how some gene families are distributed among a given set of genomes. The retrieval of a complete gene distribution among a class of genomes is an NP-hard problem because computational costs increase with the number of analyzed genomes, in fact, all-against-all gene comparisons are required to completely solve the problem. In presence of phylogenetically distant genomes, due to the variability introduced in gene duplication and transmission, the task of recognizing homologous genes becomes even more difficult. A challenge on this field is that of designing fast and adaptive similarity measures in order to find a suitable pan-genome structure of homology relations.'),
(98648, 2018, 'bmc', 'CiliateGEM: an open-project and a tool for predictions of ciliate metabolic variations and experimental condition design', 'Mancini, Alessio|Eyassu, Filmon|Conway, Maxwell|Occhipinti, Annalisa|Liò, Pietro|Angione, Claudio|Pucciarelli, Sandra', 'School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy.|Department of Computer Science and Information Systems, Teesside University, Middlesbrough, UK.|Computer Laboratory, University of Cambridge, Cambridge, UK.|Computer Laboratory, University of Cambridge, Cambridge, UK.|Computer Laboratory, University of Cambridge, Cambridge, UK.|Department of Computer Science and Information Systems, Teesside University, Middlesbrough, UK.|School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy. sandra.pucciarelli@unicam.it.', 'Italy|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|Italy', 1, 'Animals|Biomass|Phylogeny|Research Design|Software|Tetrahymena thermophila', 'Journal Article', '10.1186/s12859-018-2422-9', '30497359', 'The study of cell metabolism is becoming central in several fields such as biotechnology, evolution/adaptation and human disease investigations. Here we present CiliateGEM, the first metabolic network reconstruction draft of the freshwater ciliate Tetrahymena thermophila. We also provide the tools and resources to simulate different growth conditions and to predict metabolic variations. CiliateGEM can be extended to other ciliates in order to set up a meta-model, i.e. a metabolic network reconstruction valid for all ciliates. Ciliates are complex unicellular eukaryotes of presumably monophyletic origin, with a phylogenetic position that is equal from plants and animals. These cells represent a new concept of unicellular system with a high degree of species, population biodiversity and cell complexity. Ciliates perform in a single cell all the functions of a pluricellular organism, including locomotion, feeding, digestion, and sexual processes.'),
(98649, 2018, 'bmc', 'Challenging popular tools for the annotation of genetic variations with a real case, pathogenic mutations of lysosomal alpha-galactosidase', 'Cimmaruta, Chiara|Citro, Valentina|Andreotti, Giuseppina|Liguori, Ludovica|Cubellis, Maria Vittoria|Hay Mele, Bruno', 'Dipartimento di Biologia, Complesso di Monte SantAngelo, Università Federico II, VIA Cinthia, 80126, Napoli, Italy.|Dipartimento di Biologia, Complesso di Monte SantAngelo, Università Federico II, VIA Cinthia, 80126, Napoli, Italy.|Istituto di Chimica Biomolecolare, Consiglio Nazionale delle Ricerche, Pozzuoli, Italy.|Dipartimento di Scienze e Tecnologie Ambientali, Biologiche e Farmaceutiche, Università della Campania Luigi Vanvitelli, Caserta, Italy.|Dipartimento di Biologia, Complesso di Monte SantAngelo, Università Federico II, VIA Cinthia, 80126, Napoli, Italy. cubellis@unina.it.|Dipartimento di Biologia, Complesso di Monte SantAngelo, Università Federico II, VIA Cinthia, 80126, Napoli, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy', 0, 'Fabry Disease|Humans|Lysosomes|Molecular Sequence Annotation|Mutation, Missense|Phenotype|alpha-Galactosidase', 'Journal Article', '10.1186/s12859-018-2416-7', '30497360', 'Severity gradation of missense mutations is a big challenge for exome annotation. Predictors of deleteriousness that are most frequently used to filter variants found by next generation sequencing, produce qualitative predictions, but also numerical scores. It has never been tested if these scores correlate with disease severity.'),
(98650, 2018, 'bmc', 'miRTissue: a web application for the analysis of miRNA-target interactions in human tissues', 'Fiannaca, Antonino|La Rosa, Massimo|La Paglia, Laura|Urso, Alfonso', 'CNR-ICAR, National Research Council of Italy, Via Ugo La Malfa, 153, Palermo, 90146, Italy. antonino.fiannaca@icar.cnr.it.|CNR-ICAR, National Research Council of Italy, Via Ugo La Malfa, 153, Palermo, 90146, Italy.|CNR-ICAR, National Research Council of Italy, Via Ugo La Malfa, 153, Palermo, 90146, Italy.|CNR-ICAR, National Research Council of Italy, Via Ugo La Malfa, 153, Palermo, 90146, Italy.', 'Italy|Italy|Italy|Italy', 0, 'Biomarkers, Tumor|Computational Biology|Gene Expression Regulation, Neoplastic|Humans|Internet|MicroRNAs|Neoplasms|Organ Specificity|Protein Interaction Maps|Software', 'Journal Article', '10.1186/s12859-018-2418-5', '30497361', 'microRNAs act as regulators of gene expression interacting with their gene targets. Current bioinformatics services, such as databases of validated miRNA-target interactions and prediction tools, usually provide interactions without any information about what tissue that interaction is more likely to appear nor information about the type of interactions, causing mRNA degradation or translation inhibition respectively.'),
(98651, 2018, 'bmc', 'GLOSSary: the GLobal Ocean 16S subunit web accessible resource', 'Tangherlini, M|Miralto, M|Colantuono, C|Sangiovanni, M|Dell Anno, A|Corinaldesi, C|Danovaro, R|Chiusano, M L', 'Stazione Zoologica Anton Dohrn, Villa Comunale, 80121, Naples, Italy. michael.tangherlini@szn.it.|Stazione Zoologica Anton Dohrn, Villa Comunale, 80121, Naples, Italy.|Stazione Zoologica Anton Dohrn, Villa Comunale, 80121, Naples, Italy.|Stazione Zoologica Anton Dohrn, Villa Comunale, 80121, Naples, Italy.|Dipartimento di Scienze della Vita e dellAmbiente, Polytechnic University of Marche, Via Brecce Bianche, 60131, Ancona, Italy.|Dipartimento di Scienze e Ingegneria della Materia, dellAmbiente ed Urbanistica, Polytechnic University of Marche, Via Brecce Bianche, 60131, Ancona, Italy.|Stazione Zoologica Anton Dohrn, Villa Comunale, 80121, Naples, Italy.|Stazione Zoologica Anton Dohrn, Villa Comunale, 80121, Naples, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 2, 'Computational Biology|Geography|Internet|Metagenomics|Oceans and Seas|RNA, Ribosomal, 16S|Software|User-Computer Interface', 'Journal Article', '10.1186/s12859-018-2423-8', '30497362', 'Environmental metagenomics is a challenging approach that is exponentially spreading in the scientific community to investigate taxonomic diversity and possible functions of the biological components. The massive amount of sequence data produced, often endowed with rich environmental metadata, needs suitable computational tools to fully explore the embedded information. Bioinformatics plays a key role in providing methodologies to manage, process and mine molecular data, integrated with environmental metagenomics collections. One such relevant example is represented by the Tara Ocean Project.'),
(98652, 2018, 'bmc', 'SEDE-GPS: socio-economic data enrichment based on GPS information', 'Sperlea, Theodor|Füser, Stefan|Boenigk, Jens|Heider, Dominik', 'Faculty of Mathematics and Computer Science, University of Marburg, Hans-Meerwein-Str. 6, Marburg (Lahn), D-35032, Germany.|Faculty of Mathematics and Computer Science, University of Marburg, Hans-Meerwein-Str. 6, Marburg (Lahn), D-35032, Germany.|Biodiversity Department, Center for Water and Environmental Research, University of Duisburg-Essen, Essen, D-45141, Germany.|Faculty of Mathematics and Computer Science, University of Marburg, Hans-Meerwein-Str. 6, Marburg (Lahn), D-35032, Germany. dominik.heider@uni-marburg.de.', 'Germany|Germany|Germany|Germany', 0, 'Algorithms|Biodiversity|Geographic Information Systems|Lakes|Machine Learning|Microbiota|Socioeconomic Factors', 'Journal Article', '10.1186/s12859-018-2419-4', '30497363', 'Microbes are essentail components of all ecosystems because they drive many biochemical processes and act as primary producers. In freshwater ecosystems, the biodiversity in and the composition of microbial communities can be used as indicators for environmental quality. Recently, some environmental features have been identified that influence microbial ecosystems. However, the impact of human action on lake microbiomes is not well understood. This is, in part, due to the fact that environmental data is, albeit theoretically accessible, not easily available.'),
(98653, 2018, 'bmc', 'Efficient computation of spaced seed hashing with block indexing', 'Girotto, Samuele|Comin, Matteo|Pizzi, Cinzia', 'Department of Information Engineering, University of Padova, via Gradenigo 6/A, Padova, Italy.|Department of Information Engineering, University of Padova, via Gradenigo 6/A, Padova, Italy. comin@dei.unipd.it.|Department of Information Engineering, University of Padova, via Gradenigo 6/A, Padova, Italy. cinzia.pizzi@dei.unipd.it.', 'Italy|Italy|Italy', 4, 'Algorithms|Base Sequence|Computational Biology|Metagenomics|Time Factors', 'Journal Article', '10.1186/s12859-018-2415-8', '30497364', 'Spaced-seeds, i.e. patterns in which some fixed positions are allowed to be wild-cards, play a crucial role in several bioinformatics applications involving substrings counting and indexing, by often providing better sensitivity with respect to k-mers based approaches. K-mers based approaches are usually fast, being based on efficient hashing and indexing that exploits the large overlap between consecutive k-mers. Spaced-seeds hashing is not as straightforward, and it is usually computed from scratch for each position in the input sequence. Recently, the FSH (Fast Spaced seed Hashing) approach was proposed to improve the time required for computation of the spaced seed hashing of DNA sequences with a speed-up of about 1.5 with respect to standard hashing computation.'),
(98654, 2018, 'bmc', 'Multilevel comparative bioinformatics to investigate evolutionary relationships and specificities in gene annotations: an example for tomato and grapevine', 'Ambrosino, Luca|Ruggieri, Valentino|Bostan, Hamed|Miralto, Marco|Vitulo, Nicola|Zouine, Mohamed|Barone, Amalia|Bouzayen, Mondher|Frusciante, Luigi|Pezzotti, Mario|Valle, Giorgio|Chiusano, Maria Luisa', 'Department of Agriculture, University of Naples Federico II,, Portici, Naples, Italy.|Department of Agriculture, University of Naples Federico II,, Portici, Naples, Italy.|Department of Agriculture, University of Naples Federico II,, Portici, Naples, Italy.|Department of Agriculture, University of Naples Federico II,, Portici, Naples, Italy.|Department of Biotechnology, University of Verona, Verona, Italy.|Génomique et Biotechnologie des Fruits, UMR990 INRA / INP-Toulouse, Université de Toulouse, Castanet-Tolosan, France.|Department of Agriculture, University of Naples Federico II,, Portici, Naples, Italy.|Génomique et Biotechnologie des Fruits, UMR990 INRA / INP-Toulouse, Université de Toulouse, Castanet-Tolosan, France.|Department of Agriculture, University of Naples Federico II,, Portici, Naples, Italy.|Department of Biotechnology, University of Verona, Verona, Italy.|CRIBI Biotechnology Centre, University of Padova, Padova, Italy.|Department of Agriculture, University of Naples Federico II,, Portici, Naples, Italy. chiusano@unina.it.', 'Italy|Italy|Italy|Italy|Italy|France|Italy|France|Italy|Italy|Italy|Italy', 0, 'Biological Evolution|Computational Biology|Gene Expression Regulation, Plant|Gene Regulatory Networks|Genome, Plant|Lycopersicon esculentum|Molecular Sequence Annotation|Multilevel Analysis|Phylogeny|RNA, Messenger|Transcription Factors|Vitis', 'Comparative Study', '10.1186/s12859-018-2420-y', '30497367', 'Omics approaches may provide useful information for a deeper understanding of speciation events, diversification and function innovation. This can be achieved by investigating the molecular similarities at sequence level between species, allowing the definition of ortholog and paralog genes. However, the spreading of sequenced genome, often endowed with still preliminary annotations, requires suitable bioinformatics to be appropriately exploited in this framework.'),
(98655, 2018, 'bmc', 'Distinguishing crystallographic from biological interfaces in protein complexes: role of intermolecular contacts and energetics for classification', 'Elez, Katarina|Bonvin, Alexandre M J J|Vangone, Anna', 'Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584 CH, Utrecht, The Netherlands.|Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584 CH, Utrecht, The Netherlands. a.m.j.j.bonvin@uu.nl.|Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584 CH, Utrecht, The Netherlands. a.vangone@gmail.com.', 'Netherlands|Netherlands|Netherlands', 4, 'Algorithms|Crystallography, X-Ray|Databases, Protein|Energy Metabolism|Machine Learning|Proteins|Reproducibility of Results|Static Electricity', 'Journal Article', '10.1186/s12859-018-2414-9', '30497368', 'Study of macromolecular assemblies is fundamental to understand functions in cells. X-ray crystallography is the most common technique to solve their 3D structure at atomic resolution. In a crystal, however, both biologically-relevant interfaces and non-specific interfaces resulting from crystallographic packing are observed. Due to the complexity of the biological assemblies currently tackled, classifying those interfaces, i.e. distinguishing biological from crystal lattice interfaces, is not trivial and often prone to errors. In this context, analyzing the physico-chemical characteristics of biological/crystal interfaces can help researchers identify possible features that distinguish them and gain a better understanding of the systems.'),
(98656, 2018, 'bmc', 'SWIM tool application to expression data of glioblastoma stem-like cell lines, corresponding primary tumors and conventional glioma cell lines', 'Fiscon, Giulia|Conte, Federica|Paci, Paola', 'Institute for Systems Analysis and Computer Science Antonio Ruberti, National Research Council, Via dei Taurini 19, Rome, 00185, Italy.|Institute for Systems Analysis and Computer Science Antonio Ruberti, National Research Council, Via dei Taurini 19, Rome, 00185, Italy.|Institute for Systems Analysis and Computer Science Antonio Ruberti, National Research Council, Via dei Taurini 19, Rome, 00185, Italy. paola.paci@iasi.cnr.it.', 'Italy|Italy|Italy', 6, 'Adult|Brain Neoplasms|Cell Line, Tumor|Cluster Analysis|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Gene Regulatory Networks|Genes, Neoplasm|Genes, Switch|Glioblastoma|Glioma|Humans|MicroRNAs|Neoplastic Stem Cells|Software|Survival Analysis|Up-Regulation', 'Journal Article', '10.1186/s12859-018-2421-x', '30497369', 'It is well-known that glioblastoma contains self-renewing, stem-like subpopulation with the ability to sustain tumor growth. These cells - called cancer stem-like cells - share certain phenotypic characteristics with untransformed stem cells and are resistant to many conventional cancer therapies, which might explain the limitations in curing human malignancies. Thus, the identification of genes controlling the differentiation of these stem-like cells is becoming a successful therapeutic strategy, owing to the promise of novel targets for treating malignancies.'),
(98657, 2018, 'bmc', 'Multi-omic analysis of signalling factors in inflammatory comorbidities', 'Xiao, Hui|Bartoszek, Krzysztof|Lio, Pietro', 'Computer Laboratory, University of Cambridge, Cambridge, UK.|Department of Computer and Information Science, Linkxc3xb6ping University, Linkxc3xb6ping, Sweden.|Computer Laboratory, University of Cambridge, Cambridge, UK. pl219@cam.ac.uk.', 'United Kingdom|Sweden|United Kingdom', 0, 'Comorbidity|DNA Methylation|Gene Expression Profiling|Gene Expression Regulation|Gene Regulatory Networks|Genetic Association Studies|Genomics|Humans|Inflammation|Multigene Family|Phylogeny|Principal Component Analysis|Receptors, Tumor Necrosis Factor|Signal Transduction', 'Journal Article', '10.1186/s12859-018-2413-x', '30497370', 'Inflammation is a core element of many different, systemic and chronic diseases that usually involve an important autoimmune component. The clinical phase of inflammatory diseases is often the culmination of a long series of pathologic events that started years before. The systemic characteristics and related mechanisms could be investigated through the multi-omic comparative analysis of many inflammatory diseases. Therefore, it is important to use molecular data to study the genesis of the diseases. Here we propose a new methodology to study the relationships between inflammatory diseases and signalling molecules whose dysregulation at molecular levels could lead to systemic pathological events observed in inflammatory diseases.'),
(98658, 2018, 'bmc', 'Learning the optimal scale for GWAS through hierarchical SNP aggregation', 'Guinot, Florent|Szafranski, Marie|Ambroise, Christophe|Samson, Franck', 'UMR 8071 LaMME - UEVE, CNRS, ENSIIE, USC INRA, 23 bd de France, Evry, 91000, France. florent.guinot@genopole.cnrs.fr.|UMR 8071 LaMME - UEVE, CNRS, ENSIIE, USC INRA, 23 bd de France, Evry, 91000, France.|UMR 8071 LaMME - UEVE, CNRS, ENSIIE, USC INRA, 23 bd de France, Evry, 91000, France.|UMR 8071 LaMME - UEVE, CNRS, ENSIIE, USC INRA, 23 bd de France, Evry, 91000, France.', 'France|France|France|France', 6, 'Algorithms|Area Under Curve|Case-Control Studies|Computer Simulation|Gene Frequency|Genome-Wide Association Study|Humans|Linkage Disequilibrium|Numerical Analysis, Computer-Assisted|Phenotype|Polymorphism, Single Nucleotide|ROC Curve|Spondylitis, Ankylosing', 'Journal Article', '10.1186/s12859-018-2475-9', '30497371', 'Genome-Wide Association Studies (GWAS) seek to identify causal genomic variants associated with rare human diseases. The classical statistical approach for detecting these variants is based on univariate hypothesis testing, with healthy individuals being tested against affected individuals at each locus. Given that an individuals genotype is characterized by up to one million SNPs, this approach lacks precision, since it may yield a large number of false positives that can lead to erroneous conclusions about genetic associations with the disease. One way to improve the detection of true genetic associations is to reduce the number of hypotheses to be tested by grouping SNPs.'),
(98659, 2018, 'bmc', 'PanoromiX: a time-course network medicine platform integrating molecular assays and pathophenotypic data', 'Yang, Ruoting|Watson, Daniel|Williams, Joshua|Kumar, Raina|Campbell, Ross|Mudunuri, Uma|Hammamieh, Rasha|Jett, Marti', 'Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, 21702-5010, USA. ruoting.yang@nih.gov.|Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, 21702-5010, USA.|Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, 21702-5010, USA.|Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, 21702-5010, USA.|Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, 21702-5010, USA.|Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, 21702-5010, USA.|Integrative Systems Biology Program, US Army Center for Environmental Health Research, 568 Doughten Drive, Fort Detrick, MD, 21702-5010, USA.|Integrative Systems Biology Program, US Army Center for Environmental Health Research, 568 Doughten Drive, Fort Detrick, MD, 21702-5010, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Biological Assay|Gene Regulatory Networks|Phenotype|Prions|Software|Time Factors', 'Journal Article', '10.1186/s12859-018-2494-6', '30497372', 'Network medicine aims to map molecular perturbations of any given diseases onto complex networks with functional interdependencies that underlie a pathological phenotype. Furthermore, investigating the time dimension of disease progression from a network perspective is key to gaining key insights to the disease process and to identify diagnostic or therapeutic targets. Existing platforms are ineffective to modularize the large complex systems into subgroups and consolidate heterogeneous data to web-based interactive animation.'),
(98660, 2018, 'bmc', 'Purge Haplotigs: allelic contig reassignment for third-gen diploid genome assemblies', 'Roach, Michael J|Schmidt, Simon A|Borneman, Anthony R', 'The Australian Wine Research Institute, PO Box 197, Glen Osmond, SA, 5064, Australia. michael.roach@awri.com.au.|The Australian Wine Research Institute, PO Box 197, Glen Osmond, SA, 5064, Australia.|The Australian Wine Research Institute, PO Box 197, Glen Osmond, SA, 5064, Australia.', 'Australia|Australia|Australia', 55, 'Alleles|Arabidopsis|Contig Mapping|Diploidy|Genome, Plant|Haploidy|Haplotypes|Heterozygote|High-Throughput Nucleotide Sequencing|Homozygote|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1186/s12859-018-2485-7', '30497373', 'Recent developments in third-gen long read sequencing and diploid-aware assemblers have resulted in the rapid release of numerous reference-quality assemblies for diploid genomes. However, assembly of highly heterozygous genomes is still problematic when regional heterogeneity is so high that haplotype homology is not recognised during assembly. This results in regional duplication rather than consolidation into allelic variants and can cause issues with downstream analysis, for example variant discovery, or haplotype reconstruction using the diploid assembly with unpaired allelic contigs.'),
(98661, 2018, 'bmc', 'Representativeness of variation benchmark datasets', 'Schaafsma, Gerard C P|Vihinen, Mauno', 'Protein Structure and Bioinformatics, Department of Experimental Medical Science, Lund University, BMC B13, SE-221 84, Lund, Sweden.|Protein Structure and Bioinformatics, Department of Experimental Medical Science, Lund University, BMC B13, SE-221 84, Lund, Sweden. mauno.vihinen@med.lu.se.', 'Sweden|Sweden', 0, 'Benchmarking|Chromosomes|Databases as Topic|Databases, Protein|Gene Frequency|Gene Ontology|Genetic Variation|Humans|Molecular Sequence Annotation|Protein Domains|Proteins', 'Journal Article', '10.1186/s12859-018-2478-6', '30497376', 'Benchmark datasets are essential for both method development and performance assessment. These datasets have numerous requirements, representativeness being one. In the case of variant tolerance/pathogenicity prediction, representativeness means that the dataset covers the space of variations and their effects.'),
(98662, 2018, 'bmc', 'Aggregation of experts: an application in the field of interactomics (detection of interactions on the basis of genomic data)', 'Abo Alchamlat, Sinan|Farnir, Frédéric', 'Department of Biostatistics, Faculty of Veterinary Medicine, University of Liège, Sart Tilman B43, 4000, Liege, Belgium.|Department of Biostatistics, Faculty of Veterinary Medicine, University of Liège, Sart Tilman B43, 4000, Liege, Belgium. f.farnir@ulg.ac.be.', 'Belgium|Belgium', 0, 'Arthritis, Rheumatoid|Computational Biology|Epistasis, Genetic|Genome-Wide Association Study|Genomics|Humans|Models, Genetic|Phenotype|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/s12859-018-2447-0', '30497383', 'Despite the successful mapping of genes involved in the determinism of numerous traits, a large part of the genetic variation remains unexplained. A possible explanation is that the simple models used in many studies might not properly fit the actual underlying situations. Consequently, various methods have attempted to deal with the simultaneous mapping of genomic regions, assuming that these regions might interact, leading to a complex determinism for various traits. Despite some successes, no gold standard methodology has emerged. Actually, combining several interaction mapping methods might be a better strategy, leading to positive results over a larger set of situations. Our work is a step in that direction.'),
(98663, 2018, 'bmc', 'Integrated genome sizing (IGS) approach for the parallelization of whole genome analysis', 'Sona, Peter|Hong, Jong Hui|Lee, Sunho|Kim, Byong Joon|Hong, Woon-Young|Jung, Jongcheol|Kim, Han-Na|Kim, Hyung-Lae|Christopher, David|Herviou, Laurent|Im, Young Hwan|Lee, Kwee-Yum|Kim, Tae Soon|Jung, Jongsun', 'Genome Data Integration Center, Syntekabio Incorporated, Techno-2ro B-512, Yuseong-gu, Daejeon, Republic of Korea, 34025.|Genome Data Integration Center, Syntekabio Incorporated, Techno-2ro B-512, Yuseong-gu, Daejeon, Republic of Korea, 34025.|Genome Data Integration Center, Syntekabio Incorporated, Techno-2ro B-512, Yuseong-gu, Daejeon, Republic of Korea, 34025.|Genome Data Integration Center, Syntekabio Incorporated, Techno-2ro B-512, Yuseong-gu, Daejeon, Republic of Korea, 34025.|Genome Data Integration Center, Syntekabio Incorporated, Techno-2ro B-512, Yuseong-gu, Daejeon, Republic of Korea, 34025.|Genome Data Integration Center, Syntekabio Incorporated, Techno-2ro B-512, Yuseong-gu, Daejeon, Republic of Korea, 34025.|PGM21 (Personalized Genomic Medicine 21), Ewha Womans University Medical Center, 1071, Anyang Cheon-ro, Yangcheon-gu, Seoul, 158-710, Korea.|PGM21 (Personalized Genomic Medicine 21), Ewha Womans University Medical Center, 1071, Anyang Cheon-ro, Yangcheon-gu, Seoul, 158-710, Korea.|Bioinformatics Solutions, 900 N McCarthy Blvd., Milpitas, CA, 95035, USA.|Bioinformatics Solutions, 900 N McCarthy Blvd., Milpitas, CA, 95035, USA.|Bioinformatics Solutions, 900 N McCarthy Blvd., Milpitas, CA, 95035, USA.|Genome Data Integration Center, Syntekabio Incorporated, Techno-2ro B-512, Yuseong-gu, Daejeon, Republic of Korea, 34025.|Genome Data Integration Center, Syntekabio Incorporated, Techno-2ro B-512, Yuseong-gu, Daejeon, Republic of Korea, 34025.|Genome Data Integration Center, Syntekabio Incorporated, Techno-2ro B-512, Yuseong-gu, Daejeon, Republic of Korea, 34025. jung@syntekabio.com.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Genome Size|Genomics|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-018-2499-1', '30509173', 'The use of whole genome sequence has increased recently with rapid progression of next-generation sequencing (NGS) technologies. However, storing raw sequence reads to perform large-scale genome analysis pose hardware challenges. Despite advancement in genome analytic platforms, efficient approaches remain relevant especially as applied to the human genome. In this study, an Integrated Genome Sizing (IGS) approach is adopted to speed up multiple whole genome analysis in high-performance computing (HPC) environment. The approach splits a genome (GRCh37) into 630 chunks (fragments) wherein multiple chunks can simultaneously be parallelized for sequence analyses across cohorts.'),
(98664, 2018, 'bmc', 'Full-length title: NRPPUR database search and in vitro analysis identify an NRPS-PKS biosynthetic gene cluster with a potential antibiotic effect', 'Fritz, Shirley|Rajaonison, Andriamiharimamy|Chabrol, Olivier|Raoult, Didier|Rolain, Jean-Marc|Merhej, Vicky', 'IRD, APHM, MEPHI, IHU-Méditerranée Infection, Aix Marseille University, Marseille, France.|IRD, APHM, MEPHI, IHU-Méditerranée Infection, Aix Marseille University, Marseille, France.|CNRS, Centrale Marseille, Aix Marseille University, I2M, Marseille, France.|IRD, APHM, MEPHI, IHU-Méditerranée Infection, Aix Marseille University, Marseille, France.|IRD, APHM, MEPHI, IHU-Méditerranée Infection, Aix Marseille University, Marseille, France.|IRD, APHM, MEPHI, IHU-Méditerranée Infection, Aix Marseille University, Marseille, France. vicky.merhej@univ-amu.fr.', 'France|France|France|France|France|France', 0, 'Anti-Bacterial Agents|Databases, Factual|Humans|Multigene Family|Peptide Synthases', 'Journal Article', '10.1186/s12859-018-2479-5', '30509188', 'Growing concern about the emergence of antibiotic resistance is compelling the pharmaceutical industry to search for new antimicrobial agents. The availability of genome sequences has enabled the development of computational mining as an important tool in the discovery of natural products with antibiotic effect.'),
(98665, 2018, 'bmc', 'Shortest path counting in probabilistic biological networks', 'Ren, Yuanfang|Ay, Ahmet|Kahveci, Tamer', 'Department of Computer and Information Science and Engineering, University of Florida, Gainesville, 32611, FL, USA.|Departments of Biology and Mathematics, Colgate University, Hamilton, 13346, NY, USA.|Department of Computer and Information Science and Engineering, University of Florida, Gainesville, 32611, FL, USA. tamer@cise.ufl.edu.', 'United States|United States|United States', 2, 'Biological Products|Gene Regulatory Networks|Humans', 'Journal Article', '10.1186/s12859-018-2480-z', '30514202', 'Biological regulatory networks, representing the interactions between genes and their products, control almost every biological activity in the cell. Shortest path search is critical to apprehend the structure of these networks, and to detect their key components. Counting the number of shortest paths between pairs of genes in biological networks is a polynomial time problem. The fact that biological interactions are uncertain events however drastically complicates the problem, as it makes the topology of a given network uncertain.'),
(98666, 2018, 'bmc', 'Automated generation of genome-scale metabolic draft reconstructions based on KEGG', 'Karlsen, Emil|Schulz, Christian|Almaas, Eivind', 'Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Høgskoleringen 1, Trondheim, 7491, Norway.|Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Høgskoleringen 1, Trondheim, 7491, Norway.|Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Høgskoleringen 1, Trondheim, 7491, Norway. eivind.almaas@ntnu.no.', 'Norway|Norway|Norway', 0, 'Computational Biology|Databases, Genetic|Genome|Metabolic Networks and Pathways|Molecular Sequence Annotation', 'Journal Article', '10.1186/s12859-018-2472-z', '30514205', 'Constraint-based modeling is a widely used and powerful methodology to assess the metabolic phenotypes and capabilities of an organism. The starting point and cornerstone of all such modeling is a genome-scale metabolic network reconstruction. The creation, further development, and application of such networks is a growing field of research thanks to a plethora of readily accessible computational tools. While the majority of studies are focused on single-species analyses, typically of a microbe, the computational study of communities of organisms is gaining attention. Similarly, reconstructions that are unified for a multi-cellular organism have gained in popularity. Consequently, the rapid generation of genome-scale metabolic reconstructed networks is crucial. While multiple web-based or stand-alone tools are available for automated network reconstruction, there is, however, currently no publicly available tool that allows the swift assembly of draft reconstructions of community metabolic networks and consolidated metabolic networks for a specified list of organisms.'),
(98667, 2018, 'bmc', 'Structure-function analysis of Sedolisins: evolution of tripeptidyl peptidase and endopeptidase subfamilies in fungi', 'Orts, Facundo|Ten Have, Arjen', 'Instituto de Investigaciones Biolxc3xb3gicas (IIB-CONICET-UNMdP), Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Mar del Plata, CC 1245, 7600, Mar del Plata, Argentina.|Instituto de Investigaciones Biolxc3xb3gicas (IIB-CONICET-UNMdP), Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Mar del Plata, CC 1245, 7600, Mar del Plata, Argentina. tenhave.arjen@gmail.com.', 'Argentina|Argentina', 0, 'Amino Acid Sequence|Aminopeptidases|Carboxypeptidases|Dipeptidyl-Peptidases and Tripeptidyl-Peptidases|Fungi|Phylogeny|Serine Endopeptidases', 'Journal Article', '10.1186/s12859-018-2404-y', '30514213', 'Sedolisins are acid proteases that are related to the basic subtilisins. They have been identified in all three superkingdoms but are not ubiquitous, although fungi that secrete acids as part of their lifestyle can have up to six paralogs. Both TriPeptidyl Peptidase (TPP) and endopeptidase activity have been identified and it has been suggested that these correspond to separate subfamilies.'),
(98668, 2018, 'bmc', 'Cluster oligonucleotide signatures for rapid identification by sequencing', 'Zahariev, Manuel|Chen, Wen|Visagie, Cobus M|Lévesque, C André', 'Ottawa R&D Centre, Agriculture & Agri-Food Canada, 960 Carling Ave., Ottawa, ON, K1A 0C6, Canada. mz@alumni.sfu.ca.|Skwez Technology Corp, Box 3674, Garibaldi Highlands, BC, V0N 1T0, Canada. Wen.Chen@arc.gc.ca.|The Agricultural Research Counci -PPRI, P/Bag X134, Queenswood, 0121, South Africa.|Sidney Laboratory Project - Science, Canadian Food Inspection Agency, Floor 2E, Room 233, 59 Camelot Drive, Ottawa, ON, K1A 0Y9, Canada.', 'Canada|Canada|South Africa|Canada', 0, 'High-Throughput Nucleotide Sequencing|Metagenomics|Oligonucleotides|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-018-2363-3', '30522439', 'Oligonucleotide signatures (signatures) have been widely used for studying microbial diversity and function in wet-lab settings, but using them for accurate in silico identification of organisms from high-throughput sequencing (HTS) data is only a proof of concept. Existing signature design programs for sequence signatures (signatures matching exactly one sequence) or clade signatures (signatures matching every sequence in a phylogenetic clade) are not able to identify all possible polymorphic sites for sequences with high similarity and perform poorly when handling large genome sequencing datasets.'),
(98669, 2018, 'bmc', 'Antimicrobial peptide similarity and classification through rough set theory using physicochemical boundaries', 'Boone, Kyle|Camarda, Kyle|Spencer, Paulette|Tamerler, Candan', 'Bioengineering Program, Institute of Bioengineering Research, University of Kansas, Learned Hall, Room 5109, 1530 W 15th Street, Lawrence, KS, 66045, USA.|Chemical and Petroleum Engineering Department, University of Kansas, Learned Hall, Room 4154, 1530 West 15th Street, Lawrence, KS, 66045, USA.|Mechanical Engineering Department, Bioengineering Program, Institute of Bioengineering Research, University of Kansas, Learned Hall, Room 3111, 1530 West 15th Street, Lawrence, KS, 66045, USA.|Mechanical Engineering Department, Bioengineering Program, Institute of Bioengineering Research, University of Kansas, Learned Hall, Room 3135A, 1530 W 15th St, Lawrence, KS, 66045, USA. ctamerler@ku.edu.', 'United States|United States|United States|United States', 0, 'Antimicrobial Cationic Peptides|Chemical Phenomena|Models, Molecular', 'Journal Article', '10.1186/s12859-018-2514-6', '30522443', 'Antimicrobial peptides attract considerable interest as novel agents to combat infections. Their long-time potency across bacteria, viruses and fungi as part of diverse innate immune systems offers a solution to overcome the rising concerns from antibiotic resistance. With the rapid increase of antimicrobial peptides reported in the databases, peptide selection becomes a challenge. We propose similarity analyses to describe key properties that distinguish between active and non-active peptide sequences building upon the physicochemical properties of antimicrobial peptides. We used an iterative supervised machine learning approach to classify active peptides from inactive peptides with low false discovery rates in a relatively short computational search time.'),
(98670, 2018, 'bmc', 'Using Kinect to classify Parkinsons disease stages related to severity of gait impairment', 'Dranca, Lacramioara|de Abetxuko Ruiz de Mendarozketa, Lopez|Goxc3xb1i, Alfredo|Illarramendi, Arantza|Navalpotro Gomez, Irene|Delgado Alvarado, Manuel|Rodrxc3xadguez-Oroz, Marxc3xada Cruz', 'Centro Universitario de la Defensa, ctra. Huesca, Zaragoza, Spain.|University of the Basque Country (UPV/EHU), Paseo Manuel Lardizabal 1, Donostia-San Sebastian, 20018, Spain.|University of the Basque Country (UPV/EHU), Paseo Manuel Lardizabal 1, Donostia-San Sebastian, 20018, Spain. alfredo@ehu.eus.|University of the Basque Country (UPV/EHU), Paseo Manuel Lardizabal 1, Donostia-San Sebastian, 20018, Spain.|Neurodegenerative Disorders Area, Biodonostia Health Research Institute, Begiristain Doktorea Pasealekua, Donostia-San Sebastian, 20014, Spain.|Neurodegenerative Disorders Area, Biodonostia Health Research Institute, Begiristain Doktorea Pasealekua, Donostia-San Sebastian, 20014, Spain.|Neurodegenerative Disorders Area, Biodonostia Health Research Institute, Begiristain Doktorea Pasealekua, Donostia-San Sebastian, 20014, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain', 6, 'Aged|Algorithms|Bayes Theorem|Female|Gait|Humans|Male|Parkinson Disease|Probability|Software', 'Journal Article', '10.1186/s12859-018-2488-4', '30526473', '-'),
(98671, 2018, 'bmc', 'A new sequence logo plot to highlight enrichment and depletion', 'Dey, Kushal K|Xie, Dongyue|Stephens, Matthew', 'Department of Statistics, University of Chicago, Chicago, 60637, USA. kkdey@uchicago.edu.|Department of Statistics, University of Chicago, Chicago, 60637, USA.|Department of Statistics, University of Chicago, Chicago, 60637, USA.', 'United States|United States|United States', 0, 'Amino Acid Sequence|Base Sequence|Bayes Theorem|DNA|Humans|Sequence Alignment|Software', 'Journal Article', '10.1186/s12859-018-2489-3', '30526486', 'Sequence logo plots have become a standard graphical tool for visualizing sequence motifs in DNA, RNA or protein sequences. However standard logo plots primarily highlight enrichment of symbols, and may fail to highlight interesting depletions. Current alternatives that try to highlight depletion often produce visually cluttered logos.'),
(98672, 2018, 'bmc', 'GOnet: a tool for interactive Gene Ontology analysis', 'Pomaznoy, Mikhail|Ha, Brendan|Peters, Bjoern', 'Department of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA, USA. mikhail@lji.org.|Department of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA, USA.|Department of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA, USA.', 'United States|United States|United States', 0, 'Algorithms|Animals|Gene Ontology|Humans|Mice|Software', 'Journal Article', '10.1186/s12859-018-2533-3', '30526489', 'Biological interpretation of gene/protein lists resulting from -omics experiments can be a complex task. A common approach consists of reviewing Gene Ontology (GO) annotations for entries in such lists and searching for enrichment patterns. Unfortunately, there is a gap between machine-readable output of GO software and its human-interpretable form. This gap can be bridged by allowing users to simultaneously visualize and interact with term-term and gene-term relationships.'),
(98673, 2018, 'bmc', 'BiSpark: a Spark-based highly scalable aligner for bisulfite sequencing data', 'Soe, Seokjun|Park, Yoonjae|Chae, Heejoon', 'Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea.|Seoul National University, Seoul, Republic of Korea.|Division of Computer Science, Sookmyung Womens University, Seoul, Republic of Korea. heechae@sookmyung.ac.kr.', '-|-|-', 0, 'Algorithms|DNA Methylation|Humans|Sequence Alignment|Sequence Analysis, DNA|Software|Sulfites', 'Journal Article', '10.1186/s12859-018-2498-2', '30526492', 'Bisulfite sequencing is one of the major high-resolution DNA methylation measurement method. Due to the selective nucleotide conversion on unmethylated cytosines after treatment with sodium bisulfite, processing bisulfite-treated sequencing reads requires additional steps which need high computational demands. However, a dearth of efficient aligner that is designed for bisulfite-treated sequencing becomes a bottleneck of large-scale DNA methylome analyses.'),
(98674, 2018, 'bmc', 'Subject level clustering using a negative binomial model for small transcriptomic studies', 'Li, Qian|Noel-MacDonnell, Janelle R|Koestler, Devin C|Goode, Ellen L|Fridley, Brooke L', 'Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, 12902 Magnolia Drive, Tampa, FL, 33612, USA.|Childrens Mercy Hospital, Kansas City, MO, USA.|Department of Biostatistics, University of Kansas Medical Center, Kansas City, KS, USA.|Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.|Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, 12902 Magnolia Drive, Tampa, FL, 33612, USA. brooke.fridley@moffitt.org.', 'United States|United States|United States|United States|United States', 1, 'Cluster Analysis|Female|Humans|Male|Models, Statistical|Normal Distribution|Transcriptome', 'Journal Article', '10.1186/s12859-018-2556-9', '30541426', 'Unsupervised clustering represents one of the most widely applied methods in analysis of high-throughput omics data. A variety of unsupervised model-based or parametric clustering methods and non-parametric clustering methods have been proposed for RNA-seq count data, most of which perform well for large samples, e.g. Nxe2x80x89xe2x89xa5xe2x80x89500. A common issue when analyzing limited samples of RNA-seq count data is that the data follows an over-dispersed distribution, and thus a Negative Binomial likelihood model is often used. Thus, we have developed a Negative Binomial model-based (NBMB) clustering approach for application to RNA-seq studies.'),
(98675, 2018, 'bmc', 'A multi-omics approach for identifying important pathways and genes in human cancer', 'Frost, H Robert|Amos, Christopher I', 'Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, 03755, NH, USA. rob.frost@dartmouth.edu.|Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, 03755, NH, USA.', 'United States|United States', 6, 'Genomics|Humans|Neoplasms', 'Journal Article', '10.1186/s12859-018-2476-8', '30541428', 'Cancer develops when pathways controlling cell survival, cell fate or genome maintenance are disrupted by the somatic alteration of key driver genes. Understanding how pathway disruption is driven by somatic alterations is thus essential for an accurate characterization of cancer biology and identification of therapeutic targets. Unfortunately, current cancer pathway analysis methods fail to fully model the relationship between somatic alterations and pathway activity.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(98676, 2018, 'bmc', 'Accelerating a cross-correlation score function to search modifications using a single GPU', 'Kim, Hyunwoo|Han, Sunggeun|Um, Jung-Ho|Park, Kyongseok', 'Research Data Hub Center, Korea Institute of Science and Technology Information, Daejeon, 34141, Republic of Korea. pardess@kisti.re.kr.|KISTI Scientific Data School, Korea Institute of Science and Technology Information, Daejeon, 34141, Republic of Korea.|Research Data Hub Center, Korea Institute of Science and Technology Information, Daejeon, 34141, Republic of Korea.|Super Computing Cloud Center, Korea Institute of Science and Technology Information, Daejeon, 34141, Republic of Korea.', '-|-|-|-', 0, 'Algorithms|Peptides|Software', 'Journal Article', '10.1186/s12859-018-2559-6', '30541430', 'A cross-correlation (XCorr) score function is one of the most popular score functions utilized to search peptide identifications in databases, and many computer programs, such as SEQUEST, Comet, and Tide, currently use this score function. Recently, the HiXCorr algorithm was developed to speed up this score function for high-resolution spectra by improving the preprocessing step of the tandem mass spectra. However, despite the development of the HiXCorr algorithm, the score function is still slow because candidate peptides increase when post-translational modifications (PTMs) are considered in the search.'),
(98677, 2018, 'bmc', 'VarGenius executes cohort-level DNA-seq variant calling and annotation and allows to manage the resulting data through a PostgreSQL database', 'Musacchia, F|Ciolfi, A|Mutarelli, M|Bruselles, A|Castello, R|Pinelli, M|Basu, S|Banfi, S|Casari, G|Tartaglia, M|Nigro, V|TUDP', 'Telethon Institute for Genetics and Medicine, Viale Campi Flegrei, 34, 80078, Pozzuoli (Naples), Italy. f.musacchia@tigem.it.|Genetics and Rare Diseases Research Division, Bambino Gesxc3xb9 Childrens Hospital, Istituto di Ricovero e Cura a Carattere Scientifico, Rome, Italy.|Telethon Institute for Genetics and Medicine, Viale Campi Flegrei, 34, 80078, Pozzuoli (Naples), Italy.|Department of Oncology and Molecular Medicine, Istituto Superiore di Sanitxc3xa0, Rome, Italy.|Telethon Institute for Genetics and Medicine, Viale Campi Flegrei, 34, 80078, Pozzuoli (Naples), Italy.|Telethon Institute for Genetics and Medicine, Viale Campi Flegrei, 34, 80078, Pozzuoli (Naples), Italy.|Department of Medical Biochemistry and Cell Biology Institue of Biomedicine, The Sahlgrenska Academy University of Gothenburg, Gothenburg, Sweden.|Telethon Institute for Genetics and Medicine, Viale Campi Flegrei, 34, 80078, Pozzuoli (Naples), Italy.|Telethon Institute for Genetics and Medicine, Viale Campi Flegrei, 34, 80078, Pozzuoli (Naples), Italy.|Genetics and Rare Diseases Research Division, Bambino Gesxc3xb9 Childrens Hospital, Istituto di Ricovero e Cura a Carattere Scientifico, Rome, Italy.|Telethon Institute for Genetics and Medicine, Viale Campi Flegrei, 34, 80078, Pozzuoli (Naples), Italy.|Telethon Institute for Genetics and Medicine, Viale Campi Flegrei, 34, 80078, Pozzuoli (Naples), Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Sweden|Italy|Italy|Italy|Italy|Italy', 0, 'Computational Biology|Databases, Factual|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-018-2532-4', '30541431', 'Targeted resequencing has become the most used and cost-effective approach for identifying causative mutations of Mendelian diseases both for diagnostics and research purposes. Due to very rapid technological progress, NGS laboratories are expanding their capabilities to address the increasing number of analyses. Several open source tools are available to build a generic variant calling pipeline, but a tool able to simultaneously execute multiple analyses, organize, and categorize the samples is still missing.'),
(98678, 2018, 'bmc', 'A statistical framework for detecting mislabeled and contaminated samples using shallow-depth sequence data', 'Chan, Ariel W|Williams, Amy L|Jannink, Jean-Luc', 'Section of Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, 407 Bradfield Hall, Ithaca, NY, 14853, USA. ac2278@cornell.edu.|Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY, 14853, USA.|RW Holley Center for Agriculture and Health, United States Department of Agriculture -- Agricultural Research Service, School of Integrative Plant Sciences, Cornell University, 258 Emerson Hall, Ithaca, NY, 14853, USA.', 'United States|United States|United States', 1, 'Databases, Nucleic Acid|Humans|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-018-2512-8', '30541436', 'Researchers typically sequence a given individual multiple times, either re-sequencing the same DNA sample (technical replication) or sequencing different DNA samples collected on the same individual (biological replication) or both. Before merging the data from these replicate sequence runs, it is important to verify that no errors, such as DNA contamination or mix-ups, occurred during the data collection pipeline. Methods to detect such errors exist but are often ad hoc, cannot handle missing data and several require phased data. Because they require some combination of genotype calling, imputation, and haplotype phasing, these methods are unsuitable for error detection in low- to moderate-depth sequence data where such tasks are difficult to perform accurately. Additionally, because most existing methods employ a pairwise-comparison approach for error detection rather than joint analysis of the putative replicates, results may be difficult to interpret.'),
(98679, 2018, 'bmc', 'A nearest-neighbors network model for sequence data reveals new insight into genotype distribution of a pathogen', 'Catanese, Helen N|Brayton, Kelly A|Gebremedhin, Assefaw H', 'School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, USA.|School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, USA.|School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, USA. assefaw.gebremedhin@wsu.edu.', 'United States|United States|United States', 0, 'Amino Acid Sequence|Cluster Analysis|Genotype|Network Meta-Analysis|Proteins', 'Journal Article', '10.1186/s12859-018-2453-2', '30541438', 'Sequence similarity networks are useful for classifying and characterizing biologically important proteins. Threshold-based approaches to similarity network construction using exact distance measures are prohibitively slow to compute and rely on the difficult task of selecting an appropriate threshold, while similarity networks based on approximate distance calculations compromise useful structural information.'),
(98680, 2018, 'bmc', 'Mind the gaps: overlooking inaccessible regions confounds statistical testing in genome analysis', 'Domanska, Diana|Kanduri, Chakravarthi|Simovski, Boris|Sandve, Geir Kjetil', 'Department of Informatics, University of Oslo, Oslo, Norway. dianadom@ifi.uio.no.|Department of Informatics, University of Oslo, Oslo, Norway.|Department of Informatics, University of Oslo, Oslo, Norway.|Department of Informatics, University of Oslo, Oslo, Norway.', 'Norway|Norway|Norway|Norway', 1, 'Confounding Factors (Epidemiology)|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Statistics as Topic', 'Journal Article', '10.1186/s12859-018-2438-1', '30547739', 'The current versions of reference genome assemblies still contain gaps represented by stretches of Ns. Since high throughput sequencing reads cannot be mapped to those gap regions, the regions are depleted of experimental data. Moreover, several technology platforms assay a targeted portion of the genomic sequence, meaning that regions from the unassayed portion of the genomic sequence cannot be detected in those experiments. We here refer to all such regions as inaccessible regions, and hypothesize that ignoring these regions in the null model may increase false findings in statistical testing of colocalization of genomic features.'),
(98681, 2018, 'bmc', 'Towards a supervised classification of neocortical interneuron morphologies', 'Mihaljevixc4x87, Bojan|Larraxc3xb1aga, Pedro|Benavides-Piccione, Ruth|Hill, Sean|DeFelipe, Javier|Bielza, Concha', 'Departamento de Inteligencia Artificial, Universidad Politxc3xa9cnica de Madrid, Boadilla del Monte, 28660, Spain. bmihaljevic@fi.upm.es.|Departamento de Inteligencia Artificial, Universidad Politxc3xa9cnica de Madrid, Boadilla del Monte, 28660, Spain.|Laboratorio Cajal de Circuitos Corticales, Universidad Politxc3xa9cnica de Madrid and Instituto Cajal (CSIC), Pozuelo de Alarcxc3xb3n, 28223, Spain.|Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, M5T 1R8, Canada.|Laboratorio Cajal de Circuitos Corticales, Universidad Politxc3xa9cnica de Madrid and Instituto Cajal (CSIC), Pozuelo de Alarcxc3xb3n, 28223, Spain.|Departamento de Inteligencia Artificial, Universidad Politxc3xa9cnica de Madrid, Boadilla del Monte, 28660, Spain.', 'Spain|Spain|Spain|Canada|Spain|Spain', 5, 'Algorithms|Animals|Cells, Cultured|Dendrites|Interneurons|Male|Neocortex|Rats|Rats, Wistar', 'Journal Article', '10.1186/s12859-018-2470-1', '30558530', 'The challenge of classifying cortical interneurons is yet to be solved. Data-driven classification into established morphological types may provide insight and practical value.'),
(98682, 2018, 'bmc', 'NIPTeR: an R package for fast and accurate trisomy prediction in non-invasive prenatal testing', 'Johansson, Lennart F|de Weerd, Hendrik A|de Boer, Eddy N|van Dijk, Freerk|Te Meerman, Gerard J|Sijmons, Rolf H|Sikkema-Raddatz, Birgit|Swertz, Morris A', 'Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands. l.johansson@umcg.nl.|Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.|Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.|Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.|Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.|Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.|Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.|Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Algorithms|Female|High-Throughput Nucleotide Sequencing|Humans|Maternal Serum Screening Tests|Predictive Value of Tests|Pregnancy|Prenatal Diagnosis|Trisomy', 'Journal Article', '10.1186/s12859-018-2557-8', '30558531', 'Various algorithms have been developed to predict fetal trisomies using cell-free DNA in non-invasive prenatal testing (NIPT). As basis for prediction, a control group of non-trisomy samples is needed. Prediction accuracy is dependent on the characteristics of this group and can be improved by reducing variability between samples and by ensuring the control group is representative for the sample analyzed.'),
(98683, 2018, 'bmc', 'Correction to: Evaluating the impact of topological protein features on the negative examples selection', 'Boldi, Paolo|Frasca, Marco|Malchiodi, Dario', 'Department of Computer Science, Università degli Studi di Milano, Via Comelico 39, 20135, Milano, Italy.|Department of Computer Science, Università degli Studi di Milano, Via Comelico 39, 20135, Milano, Italy. frasca@di.unimi.it.|Department of Computer Science, Università degli Studi di Milano, Via Comelico 39, 20135, Milano, Italy.', 'Italy|Italy|Italy', 0, '-', 'Journal Article', '10.1186/s12859-018-2545-z', '30558532', 'After publication of the original article [1], it was noticed that the dagger symbol indicating equal contribution wasnt added next to the names of all authors.'),
(98684, 2018, 'bmc', 'Gene co-expression networks from RNA sequencing of dairy cattle identifies genes and pathways affecting feed efficiency', 'Salleh, S M|Mazzoni, G|Lxc3xb8vendahl, P|Kadarmideen, H N', 'Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, DK-1870, Frederiksberg C, Denmark.|Department of Bio and Health Informatics, Technical University of Denmark, DK-2800, Kgs. Lyngby, Denmark.|Department of Molecular Biology and Genetics - Center for Quantitative Genetics and Genomics, Aarhus University, AU Foulum, DK-8830, Tjele, Denmark.|Department of Bio and Health Informatics, Technical University of Denmark, DK-2800, Kgs. Lyngby, Denmark. hajak@dtu.dk.', 'Denmark|Denmark|Denmark|Denmark', 10, 'Animal Feed|Animals|Cattle|Computational Biology|Diet|Female|Gene Expression Regulation|Gene Regulatory Networks|Genome|High-Throughput Nucleotide Sequencing|Liver|Metabolic Networks and Pathways|Phenotype|Sequence Analysis, RNA|Transcriptome', 'Journal Article', '10.1186/s12859-018-2553-z', '30558534', 'Selection for feed efficiency is crucial for overall profitability and sustainability in dairy cattle production. Key regulator genes and genetic markers derived from co-expression networks underlying feed efficiency could be included in the genomic selection of the best cows. The present study identified co-expression networks associated with high and low feed efficiency and their regulator genes in Danish Holstein and Jersey cows. RNA-sequencing data from Holstein and Jersey cows with high and low residual feed intake (RFI) and treated with two diets (low and high concentrate) were used. Approximately 26 million and 25 million pair reads were mapped to bovine reference genome for Jersey and Holstein breed, respectively. Subsequently, the gene count expressions data were analysed using a Weighted Gene Co-expression Network Analysis (WGCNA) approach. Functional enrichment analysis from Ingenuityxc2xae Pathway Analysis (IPAxc2xae), ClueGO application and STRING of these modules was performed to identify relevant biological pathways and regulatory genes.'),
(98685, 2018, 'bmc', 'Analyzing the similarity of samples and genes by MG-PCC algorithm, t-SNE-SS and t-SNE-SG maps', 'Jia, Xingang|Han, Qiuhong|Lu, Zuhong', 'School of Mathematics, Southeast University, Nanjing, 210096, Peoples Republic of China. hanqh15@163.com.|Department of Mathematics, Nanjing Forestry University, Nanjing, 210037, Peoples Republic of China.|State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, Peoples Republic of China.', 'China|China|China', 1, 'Algorithms|Biomarkers, Tumor|Computational Biology|Gene Expression Regulation, Neoplastic|Genetic Variation|Humans|Neoplasms', 'Journal Article', '10.1186/s12859-018-2495-5', '30558536', 'For analyzing these gene expression data sets under different samples, clustering and visualizing samples and genes are important methods. However, it is difficult to integrate clustering and visualizing techniques when the similarities of samples and genes are defined by PCC(Person correlation coefficient) measure.'),
(98686, 2018, 'bmc', 'PASNet: pathway-associated sparse deep neural network for prognosis prediction from high-throughput data', 'Hao, Jie|Kim, Youngsoon|Kim, Tae-Kyung|Kang, Mingon', 'Kennesaw State University, Kennesaw, USA.|Kennesaw State University, Marietta, USA.|University of Texas Southwestern Medical Center, Dallas, USA.|Kennesaw State University, Kennesaw, USA. mkang9@kennesaw.edu.', 'United States|United States|United States|United States', 0, 'Area Under Curve|Biomarkers, Tumor|Glioblastoma|High-Throughput Nucleotide Sequencing|Humans|Neural Networks (Computer)|Predictive Value of Tests|Prognosis|Software', 'Journal Article', '10.1186/s12859-018-2500-z', '30558539', 'Predicting prognosis in patients from large-scale genomic data is a fundamentally challenging problem in genomic medicine. However, the prognosis still remains poor in many diseases. The poor prognosis may be caused by high complexity of biological systems, where multiple biological components and their hierarchical relationships are involved. Moreover, it is challenging to develop robust computational solutions with high-dimension, low-sample size data.'),
(98687, 2018, 'bmc', 'GLUE: a flexible software system for virus sequence data', 'Singer, Joshua B|Thomson, Emma C|McLauchlan, John|Hughes, Joseph|Gifford, Robert J', 'MRC-University of Glasgow Centre for Virus Research, Glasgow, Scotland, UK. josh.singer@glasgow.ac.uk.|MRC-University of Glasgow Centre for Virus Research, Glasgow, Scotland, UK.|MRC-University of Glasgow Centre for Virus Research, Glasgow, Scotland, UK.|MRC-University of Glasgow Centre for Virus Research, Glasgow, Scotland, UK.|MRC-University of Glasgow Centre for Virus Research, Glasgow, Scotland, UK. robert.gifford@glasgow.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 14, 'Algorithms|Amino Acid Sequence|Base Sequence|Drug Resistance, Viral|Genome, Viral|Genotype|Genotyping Techniques|Hepacivirus|Humans|Likelihood Functions|Sequence Alignment|Software|Viral Proteins', 'Journal Article', '10.1186/s12859-018-2459-9', '30563445', 'Virus genome sequences, generated in ever-higher volumes, can provide new scientific insights and inform our responses to epidemics and outbreaks. To facilitate interpretation, such data must be organised and processed within scalable computing resources that encapsulate virology expertise. GLUE (Genes Linked by Underlying Evolution) is a data-centric bioinformatics environment for building such resources. The GLUE core data schema organises sequence data along evolutionary lines, capturing not only nucleotide data but associated items such as alignments, genotype definitions, genome annotations and motifs. Its flexible design emphasises applicability to different viruses and to diverse needs within research, clinical or public health contexts.'),
(98688, 2018, 'bmc', 'iDEP: an integrated web application for differential expression and pathway analysis of RNA-Seq data', 'Ge, Steven Xijin|Son, Eun Wo|Yao, Runan', 'Department of Mathematics and Statistics, South Dakota State University, Box 2225, Brookings, SD, 57007, USA. gexijin@gmail.com.|Department of Mathematics and Statistics, South Dakota State University, Box 2225, Brookings, SD, 57007, USA.|Department of Mathematics and Statistics, South Dakota State University, Box 2225, Brookings, SD, 57007, USA.', 'United States|United States|United States', 0, 'Animals|B-Lymphocytes|Cell Proliferation|Cells, Cultured|Computational Biology|Fibroblasts|Gene Expression Profiling|Gene Expression Regulation|High-Throughput Nucleotide Sequencing|Homeodomain Proteins|Humans|Lung|Mice|RNA, Small Interfering|Sequence Analysis, RNA|Software|Transcription Factors|Transcriptome|Tumor Suppressor Protein p53', 'Journal Article', '10.1186/s12859-018-2486-6', '30567491', 'RNA-seq is widely used for transcriptomic profiling, but the bioinformatics analysis of resultant data can be time-consuming and challenging, especially for biologists. We aim to streamline the bioinformatic analyses of gene-level data by developing a user-friendly, interactive web application for exploratory data analysis, differential expression, and pathway analysis.'),
(98689, 2018, 'bmc', 'Measures of co-expression for improved function prediction of long non-coding RNAs', 'Ehsani, Rezvan|Drabløs, Finn', 'Department of Mathematics, University of Zabol, Zabol, Iran. rezvanehsani74@gmail.com.|Department of Clinical and Molecular Medicine, NTNU - Norwegian University of Science and Technology, NO-7491, Trondheim, Norway. finn.drablos@ntnu.no.', '-|Norway', 0, 'Computational Biology|Gene Expression Profiling|Gene Expression Regulation|High-Throughput Nucleotide Sequencing|Humans|Molecular Sequence Annotation|RNA, Long Noncoding|Software', 'Journal Article', '10.1186/s12859-018-2546-y', '30567492', 'Almost 16,000 human long non-coding RNA (lncRNA) genes have been identified in the GENCODE project. However, the function of most of them remains to be discovered. The function of lncRNAs and other novel genes can be predicted by identifying significantly enriched annotation terms in already annotated genes that are co-expressed with the lncRNAs. However, such approaches are sensitive to the methods that are used to estimate the level of co-expression.'),
(98690, 2018, 'bmc', 'Towards practical privacy-preserving genome-wide association study', 'Bonte, Charlotte|Makri, Eleftheria|Ardeshirdavani, Amin|Simm, Jaak|Moreau, Yves|Vercauteren, Frederik', 'imec-COSIC, Department of Electrical Engineering, KU Leuven, Leuven, Belgium. cbonte@esat.kuleuven.be.|imec-COSIC, Department of Electrical Engineering, KU Leuven, Leuven, Belgium.|STADIUS KU Leuven, Leuven, Belgium.|STADIUS KU Leuven, Leuven, Belgium.|STADIUS KU Leuven, Leuven, Belgium.|imec-COSIC, Department of Electrical Engineering, KU Leuven, Leuven, Belgium.', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 7, 'Genome-Wide Association Study|Genomics|Humans', 'Journal Article', '10.1186/s12859-018-2541-3', '30572817', 'The deployment of Genome-wide association studies (GWASs) requires genomic information of a large population to produce reliable results. This raises significant privacy concerns, making people hesitate to contribute their genetic information to such studies.'),
(98691, 2018, 'bmc', 'Protein complexes identification based on go attributed network embedding', 'Xu, Bo|Li, Kun|Zheng, Wei|Liu, Xiaoxia|Zhang, Yijia|Zhao, Zhehuan|He, Zengyou', 'School of Software Technology, Dalian University of Technology, No.321 Tuqiang Road, Economic Development Zone, Dalian, 116024, China. boxu@dlut.edu.cn.|School of Software Technology, Dalian University of Technology, No.321 Tuqiang Road, Economic Development Zone, Dalian, 116024, China.|College of Computer Science and Technology, Dalian University of Technology, No.2 Linggong Road, Ganjingzi District, Dalian, 116024, China.|College of Computer Science and Technology, Dalian University of Technology, No.2 Linggong Road, Ganjingzi District, Dalian, 116024, China.|College of Computer Science and Technology, Dalian University of Technology, No.2 Linggong Road, Ganjingzi District, Dalian, 116024, China.|School of Software Technology, Dalian University of Technology, No.321 Tuqiang Road, Economic Development Zone, Dalian, 116024, China.|School of Software Technology, Dalian University of Technology, No.321 Tuqiang Road, Economic Development Zone, Dalian, 116024, China.', 'China|China|China|China|China|China|China', 0, 'Humans|Protein Interaction Mapping|Proteins|Proteomics', 'Journal Article', '10.1186/s12859-018-2555-x', '30572820', 'Identifying protein complexes from protein-protein interaction (PPI) network is one of the most important tasks in proteomics. Existing computational methods try to incorporate a variety of biological evidences to enhance the quality of predicted complexes. However, it is still a challenge to integrate different types of biological information into the complexes discovery process under a unified framework. Recently, attributed network embedding methods have be proved to be remarkably effective in generating vector representations for nodes in the network. In the transformed vector space, both the topological proximity and node attributed affinity between different nodes are preserved. Therefore, such attributed network embedding methods provide us a unified framework to integrate various biological evidences into the protein complexes identification process.'),
(98692, 2018, 'bmc', 'NGmerge: merging paired-end reads via novel empirically-derived models of sequencing errors', 'Gaspar, John M', 'Informatics Group, Division of Science, Harvard University Faculty of Arts and Sciences, Cambridge, MA, 02138, USA. jsh58@wildcats.unh.edu.', 'United States', 0, 'High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-018-2579-2', '30572828', 'Advances in Illumina DNA sequencing technology have produced longer paired-end reads that increasingly have sequence overlaps. These reads can be merged into a single read that spans the full length of the original DNA fragment, allowing for error correction and accurate determination of read coverage. Extant merging programs utilize simplistic or unverified models for the selection of bases and quality scores for the overlapping region of merged reads.'),
(98693, 2018, 'bmc', 'CANDLE/Supervisor: a workflow framework for machine learning applied to cancer research', 'Wozniak, Justin M|Jain, Rajeev|Balaprakash, Prasanna|Ozik, Jonathan|Collier, Nicholson T|Bauer, John|Xia, Fangfang|Brettin, Thomas|Stevens, Rick|Mohd-Yusof, Jamaludin|Cardona, Cristina Garcia|Essen, Brian Van|Baughman, Matthew', 'Argonne National Laboratory, Argonne, IL, USA. woz@anl.gov.|Argonne National Laboratory, Argonne, IL, USA.|Argonne National Laboratory, Argonne, IL, USA.|Argonne National Laboratory, Argonne, IL, USA.|Argonne National Laboratory, Argonne, IL, USA.|Argonne National Laboratory, Argonne, IL, USA.|Argonne National Laboratory, Argonne, IL, USA.|Argonne National Laboratory, Argonne, IL, USA.|Argonne National Laboratory, Argonne, IL, USA.|Los Alamos National Laboratory, Los Alamos, NM, USA.|Los Alamos National Laboratory, Los Alamos, NM, USA.|Lawrence Livermore National Laboratory, Livermore, CA, USA.|Minerva, San Francisco, CA, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 10, 'Early Detection of Cancer|Humans|Machine Learning|Neoplasms|Neural Networks (Computer)|Workflow', 'Journal Article', '10.1186/s12859-018-2508-4', '30577736', 'Current multi-petaflop supercomputers are powerful systems, but present challenges when faced with problems requiring large machine learning workflows. Complex algorithms running at system scale, often with different patterns that require disparate software packages and complex data flows cause difficulties in assembling and managing large experiments on these machines.'),
(98694, 2018, 'bmc', 'Nonlinear expression and visualization of nonmetric relationships in genetic diseases and microbiome data', 'Zhu, Xianchao|Shen, Xianjun|Jiang, Xingpeng|Wei, Kaiping|He, Tingting|Ma, Yuanyuan|Liu, Jiaqi|Hu, Xiaohua', 'School of Computer, Central China Normal University, Wuhan, China.|School of Computer, Central China Normal University, Wuhan, China. xjshen@mail.ccnu.edu.cn.|School of Computer, Central China Normal University, Wuhan, China.|School of Computer, Central China Normal University, Wuhan, China.|School of Computer, Central China Normal University, Wuhan, China.|School of Computer, Central China Normal University, Wuhan, China.|School of Computer, Central China Normal University, Wuhan, China.|School of Computer, Central China Normal University, Wuhan, China.', 'China|China|China|China|China|China|China|China', 0, 'Algorithms|Databases, Genetic|Gene Expression Regulation|Genetic Diseases, Inborn|Humans|Microbiota|Nonlinear Dynamics|Phenotype|Time Factors', 'Journal Article', '10.1186/s12859-018-2537-z', '30577738', 'The traditional methods of visualizing high-dimensional data objects in low-dimensional metric spaces are subject to the basic limitations of metric space. These limitations result in multidimensional scaling that fails to faithfully represent non-metric similarity data.'),
(98695, 2018, 'bmc', 'Computational approaches for Cancer 2017 workshop overview', 'Chandrasekaran, Sunita|Stahlberg, Eric', 'Department of Computer and Information Sciences, University of Delaware, Newark, DE, USA.|Biomedical Informatics and Data Science Directorate, Frederick National Laboratory, Frederick, MD, USA. eric.stahlberg@nih.gov.', 'United States|United States', 1, 'Computational Biology|Education|History, 21st Century|Humans|Neoplasms', 'Editorial', '10.1186/s12859-018-2502-x', '30577739', '-'),
(98696, 2018, 'bmc', 'An SVM-based method for assessment of transcription factor-DNA complex models', 'Corona, Rosario I|Sudarshan, Sanjana|Aluru, Srinivas|Guo, Jun-Tao', 'Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223, USA.|Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223, USA.|School of Computational Science and Engineering, Georgia Institute of Technology, 266 Ferst Drive, Atlanta, GA, 30332, USA.|Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223, USA. jguo4@uncc.edu.', 'United States|United States|Georgia|United States', 0, 'Algorithms|DNA|Models, Molecular|Protein Binding|Support Vector Machine|Transcription Factors', 'Journal Article', '10.1186/s12859-018-2538-y', '30577740', 'Atomic details of protein-DNA complexes can provide insightful information for better understanding of the function and binding specificity of DNA binding proteins. In addition to experimental methods for solving protein-DNA complex structures, protein-DNA docking can be used to predict native or near-native complex models. A docking program typically generates a large number of complex conformations and predicts the complex model(s) based on interaction energies between protein and DNA. However, the prediction accuracy is hampered by current approaches to model assessment, especially when docking simulations fail to produce any near-native models.'),
(98697, 2018, 'bmc', 'MicroRNA dysregulational synergistic network: discovering microRNA dysregulatory modules across subtypes in non-small cell lung cancers', 'Tran, Nhat|Abhyankar, Vinay|Nguyen, KyTai|Weidanz, Jon|Gao, Jean', 'Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX, 76019, USA.|UTARI Research Institute, The University of Texas at Arlington, 7300 Jack Newell Blvd S, Fort Worth, TX, 76118, USA.|Department of Bioengineering, The University of Texas at Arlington, Arlington, TX, 76019, USA.|Department of Biology, The University of Texas at Arlington, Arlington, TX, 76019, USA.|Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX, 76019, USA. gao@uta.edu.', 'United States|United States|United States|United States|United States', 0, 'Adenocarcinoma of Lung|Biomarkers, Tumor|Carcinoma, Non-Small-Cell Lung|Carcinoma, Squamous Cell|Cohort Studies|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Gene Regulatory Networks|Humans|Lung Neoplasms|MicroRNAs|Neoplasm Staging|ROC Curve|Sample Size', 'Journal Article', '10.1186/s12859-018-2536-0', '30577741', 'The majority of cancer-related deaths are due to lung cancer, and there is a need for reliable diagnostic biomarkers to predict stages in non-small cell lung cancer cases. Recently, microRNAs were found to have potential as both biomarkers and therapeutic targets for lung cancer. However, some of the microRNAs functions are unknown, and their roles in cancer stage progression have been mostly undiscovered in this clinically and genetically heterogeneous disease. As evidence suggests that microRNA dysregulations are implicated in many diseases, it is essential to consider the changes in microRNA-target regulation across different lung cancer subtypes.'),
(98698, 2018, 'bmc', 'High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow', 'Ozik, Jonathan|Collier, Nicholson|Wozniak, Justin M|Macal, Charles|Cockrell, Chase|Friedman, Samuel H|Ghaffarizadeh, Ahmadreza|Heiland, Randy|An, Gary|Macklin, Paul', 'Argonne National Laboratory, Argonne, IL, USA.|Argonne National Laboratory, Argonne, IL, USA.|Argonne National Laboratory, Argonne, IL, USA.|Argonne National Laboratory, Argonne, IL, USA.|Dept. of Surgery, University of Chicago, Chicago, IL, USA.|Opto-Knowledge Systems, Inc., Torrance, CA, USA.|Lawrence J. Ellison Center for Transformative Medicine, University of Southern California, Los Angeles, CA, USA.|Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA.|Dept. of Surgery, University of Chicago, Chicago, IL, USA.|Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA. macklinp@iu.edu.', 'United States|United States|United States|United States|United States|United States|United States|India|United States|India', 13, 'Humans|Models, Theoretical|Neoplasms|Workflow', 'Journal Article', '10.1186/s12859-018-2510-x', '30577742', 'Cancer is a complex, multiscale dynamical system, with interactions between tumor cells and non-cancerous host systems. Therapies act on this combined cancer-host system, sometimes with unexpected results. Systematic investigation of mechanistic computational models can augment traditional laboratory and clinical studies, helping identify the factors driving a treatments success or failure. However, given the uncertainties regarding the underlying biology, these multiscale computational models can take many potential forms, in addition to encompassing high-dimensional parameter spaces. Therefore, the exploration of these models is computationally challenging. We propose that integrating two existing technologies-one to aid the construction of multiscale agent-based models, the other developed to enhance model exploration and optimization-can provide a computational means for high-throughput hypothesis testing, and eventually, optimization.'),
(98699, 2018, 'bmc', 'Scalable deep text comprehension for Cancer surveillance on high-performance computing', 'Qiu, John X|Yoon, Hong-Jun|Srivastava, Kshitij|Watson, Thomas P|Blair Christian, J|Ramanathan, Arvind|Wu, Xiao C|Fearn, Paul A|Tourassi, Georgia D', 'Biomedical Sciences, Engineering, and Computing Group, Health Data Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA.|Biomedical Sciences, Engineering, and Computing Group, Health Data Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA. yoonh@ornl.gov.|Biomedical Sciences, Engineering, and Computing Group, Health Data Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA.|Herff College of Engineering, University of Memphis, Memphis, TN, USA.|Biomedical Sciences, Engineering, and Computing Group, Health Data Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA.|Biomedical Sciences, Engineering, and Computing Group, Health Data Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA.|Louisiana Tumor Registry, Louisiana State University Health Sciences Center, New Orleans, LA, USA.|Surveillance Research Program, National Cancer Institute, Bethesda, MD, USA.|Biomedical Sciences, Engineering, and Computing Group, Health Data Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 4, 'Comprehension|Computing Methodologies|Deep Learning|Humans|Neoplasms|Neural Networks (Computer)', 'Journal Article', '10.1186/s12859-018-2511-9', '30577743', 'Deep Learning (DL) has advanced the state-of-the-art capabilities in bioinformatics applications which has resulted in trends of increasingly sophisticated and computationally demanding models trained by larger and larger data sets. This vastly increased computational demand challenges the feasibility of conducting cutting-edge research. One solution is to distribute the vast computational workload across multiple computing cluster nodes with data parallelism algorithms. In this study, we used a High-Performance Computing environment and implemented the Downpour Stochastic Gradient Descent algorithm for data parallelism to train a Convolutional Neural Network (CNN) for the natural language processing task of information extraction from a massive dataset of cancer pathology reports. We evaluated the scalability improvements using data parallelism training and the Titan supercomputer at Oak Ridge Leadership Computing Facility. To evaluate scalability, we used different numbers of worker nodes and performed a set of experiments comparing the effects of different training batch sizes and optimizer functions.'),
(98700, 2018, 'bmc', 'Detection and visualization of complex structural variants from long reads', 'Stephens, Zachary|Wang, Chen|Iyer, Ravishankar K|Kocher, Jean-Pierre', 'Coordinated Science Lab, University of Illinois at Urbana-Champaign, Urbana, IL, USA.|Mayo Clinic, Rochester, MN, USA.|Coordinated Science Lab, University of Illinois at Urbana-Champaign, Urbana, IL, USA.|Mayo Clinic, Rochester, MN, USA. Kocher.JeanPierre@mayo.edu.', 'United States|United States|United States|United States', 0, 'Computer Simulation|Gene Duplication|Genome, Human|Genomic Structural Variation|Humans|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/s12859-018-2539-x', '30577744', 'With applications in cancer, drug metabolism, and disease etiology, understanding structural variation in the human genome is critical in advancing the thrusts of individualized medicine. However, structural variants (SVs) remain challenging to detect with high sensitivity using short read sequencing technologies. This problem is exacerbated when considering complex SVs comprised of multiple overlapping or nested rearrangements. Longer reads, such as those from Pacific Biosciences platforms, often span multiple breakpoints of such events, and thus provide a way to unravel small-scale complexities in SVs with higher confidence.'),
(98701, 2018, 'bmc', 'Biomedical semantic indexing by deep neural network with multi-task learning', 'Du, Yongping|Pan, Yunpeng|Wang, Chencheng|Ji, Junzhong', 'Faculty of Information Technology, Beijing University of Technology, Beijing, China.|Faculty of Information Technology, Beijing University of Technology, Beijing, China. pypmemorypool@gmail.com.|Faculty of Information Technology, Beijing University of Technology, Beijing, China.|Faculty of Information Technology, Beijing University of Technology, Beijing, China.', 'China|China|China|China', 0, 'Abstracting and Indexing as Topic|Algorithms|Deep Learning|Humans|Neural Networks (Computer)|Semantics', 'Journal Article', '10.1186/s12859-018-2534-2', '30577745', 'Biomedical semantic indexing is important for information retrieval and many other research fields in bioinformatics. It annotates biomedical citations with Medical Subject Headings. In face of unbalanced category distribution in the training data, sampling methods are difficult to apply for semantic indexing task.'),
(98702, 2018, 'bmc', 'Sparse coding of pathology slides compared to transfer learning with deep neural networks', 'Fischer, Will|Moudgalya, Sanketh S|Cohn, Judith D|Nguyen, Nga T T|Kenyon, Garrett T', 'Los Alamos National Laboratory, Los Alamos, NM, USA. wfischer@lanl.gov.|Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY, USA.|Los Alamos National Laboratory, Los Alamos, NM, USA.|Los Alamos National Laboratory, Los Alamos, NM, USA.|Los Alamos National Laboratory, Los Alamos, NM, USA.', 'United States|United States|United States|United States|United States', 3, 'Deep Learning|Humans|Neoplasms|Neural Networks (Computer)', 'Journal Article', '10.1186/s12859-018-2504-8', '30577746', 'Histopathology images of tumor biopsies present unique challenges for applying machine learning to the diagnosis and treatment of cancer. The pathology slides are high resolution, often exceeding 1GB, have non-uniform dimensions, and often contain multiple tissue slices of varying sizes surrounded by large empty regions. The locations of abnormal or cancerous cells, which may constitute a small portion of any given tissue sample, are not annotated. Cancer image datasets are also extremely imbalanced, with most slides being associated with relatively common cancers. Since deep representations trained on natural photographs are unlikely to be optimal for classifying pathology slide images, which have different spectral ranges and spatial structure, we here describe an approach for learning features and inferring representations of cancer pathology slides based on sparse coding.'),
(98703, 2018, 'bmc', 'Fast and scalable neural embedding models for biomedical sentence classification', 'Agibetov, Asan|Blagec, Kathrin|Xu, Hong|Samwald, Matthias', 'Section for Artificial Intelligence and Decision Support, Medical University of Vienna, Währinger Strasse 25A, OG1, Vienna, 1090, Austria.|Section for Artificial Intelligence and Decision Support, Medical University of Vienna, Währinger Strasse 25A, OG1, Vienna, 1090, Austria.|Section for Artificial Intelligence and Decision Support, Medical University of Vienna, Währinger Strasse 25A, OG1, Vienna, 1090, Austria.|Section for Artificial Intelligence and Decision Support, Medical University of Vienna, Währinger Strasse 25A, OG1, Vienna, 1090, Austria. matthias.samwald@meduniwien.ac.at.', 'Austria|Austria|Austria|Austria', 0, 'Biomedical Research|Humans|Language|Natural Language Processing|Neural Networks (Computer)|PubMed|Unified Medical Language System', 'Journal Article', '10.1186/s12859-018-2496-4', '30577747', 'Biomedical literature is expanding rapidly, and tools that help locate information of interest are needed. To this end, a multitude of different approaches for classifying sentences in biomedical publications according to their coarse semantic and rhetoric categories (e.g., Background, Methods, Results, Conclusions) have been devised, with recent state-of-the-art results reported for a complex deep learning model. Recent evidence showed that shallow and wide neural models such as fastText can provide results that are competitive or superior to complex deep learning models while requiring drastically lower training times and having better scalability. We analyze the efficacy of the fastText model in the classification of biomedical sentences in the PubMed 200k RCT benchmark, and introduce a simple pre-processing step that enables the application of fastText on sentence sequences. Furthermore, we explore the utility of two unsupervised pre-training approaches in scenarios where labeled training data are limited.'),
(98704, 2018, 'bmc', 'MeDEStrand: an improved method to infer genome-wide absolute methylation levels from DNA enrichment data', 'Xu, Jingting|Liu, Shimeng|Yin, Ping|Bulun, Serdar|Dai, Yang', 'Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA.|Division of Reproductive Science in Medicine, Department of Obstetrics and Gynecology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.|Division of Reproductive Science in Medicine, Department of Obstetrics and Gynecology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.|Division of Reproductive Science in Medicine, Department of Obstetrics and Gynecology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.|Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA. yangdai@uic.edu.', 'United States|United States|United States|United States|United States', 0, 'Cells, Cultured|CpG Islands|DNA|DNA Methylation|Epigenesis, Genetic|Fibroblasts|Genome, Human|Genomics|Humans|Mammary Glands, Human|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-018-2574-7', '30577750', 'DNA methylation of CpG dinucleotides is an essential epigenetic modification that plays a key role in transcription. Widely used DNA enrichment-based methods offer high coverage for measuring methylated CpG dinucleotides, with the lowest cost per CpG covered genome-wide. However, these methods measure the DNA enrichment of methyl-CpG binding, and thus do not provide information on absolute methylation levels. Further, the enrichment is influenced by various confounding factors in addition to methylation status, for example, CpG density. Computational models that can accurately derive absolute methylation levels from DNA enrichment data are needed.'),
(98705, 2018, 'bmc', 'Real-time data analysis for medical diagnosis using FPGA-accelerated neural networks', 'Sanaullah, Ahmed|Yang, Chen|Alexeev, Yuri|Yoshii, Kazutomo|Herbordt, Martin C', 'Computer Architecture and Automated Design Lab, Boston University, Boston, MA, USA.|Computer Architecture and Automated Design Lab, Boston University, Boston, MA, USA.|Argonne Leadership Computing Facility, Argonne National Laboratory, Lemont, IL, USA.|Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL, USA.|Computer Architecture and Automated Design Lab, Boston University, Boston, MA, USA. herbordt@bu.edu.', 'United States|United States|United States|United States|United States', 5, 'Data Analysis|Humans|Machine Learning|Neoplasms|Neural Networks (Computer)', 'Journal Article', '10.1186/s12859-018-2505-7', '30577751', 'Real-time analysis of patient data during medical procedures can provide vital diagnostic feedback that significantly improves chances of success. With sensors becoming increasingly fast, frameworks such as Deep Neural Networks are required to perform calculations within the strict timing constraints for real-time operation. However, traditional computing platforms responsible for running these algorithms incur a large overhead due to communication protocols, memory accesses, and static (often generic) architectures. In this work, we implement a low-latency Multi-Layer Perceptron (MLP) processor using Field Programmable Gate Arrays (FPGAs). Unlike CPUs and Graphics Processing Units (GPUs), our FPGA-based design can directly interface sensors, storage devices, display devices and even actuators, thus reducing the delays of data movement between ports and compute pipelines. Moreover, the compute pipelines themselves are tailored specifically to the application, improving resource utilization and reducing idle cycles. We demonstrate the effectiveness of our approach using mass-spectrometry data sets for real-time cancer detection.'),
(98706, 2018, 'bmc', 'High-throughput binding affinity calculations at extreme scales', 'Dakka, Jumana|Turilli, Matteo|Wright, David W|Zasada, Stefan J|Balasubramanian, Vivek|Wan, Shunzhou|Coveney, Peter V|Jha, Shantenu', 'Department Electrical and Computer Engineering, Rutgers University, 94 Brett Road, Piscataway, NJ, USA.|Department Electrical and Computer Engineering, Rutgers University, 94 Brett Road, Piscataway, NJ, USA.|Centre for Computational Sciences, UCL, 20 Gordon Street, London, UK.|Centre for Computational Sciences, UCL, 20 Gordon Street, London, UK.|Department Electrical and Computer Engineering, Rutgers University, 94 Brett Road, Piscataway, NJ, USA.|Centre for Computational Sciences, UCL, 20 Gordon Street, London, UK.|Centre for Computational Sciences, UCL, 20 Gordon Street, London, UK.|Department Electrical and Computer Engineering, Rutgers University, 94 Brett Road, Piscataway, NJ, USA. shantenu.jha@rutgers.edu.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 2, 'High-Throughput Screening Assays|Humans|Protein Binding', 'Journal Article', '10.1186/s12859-018-2506-6', '30577753', 'Resistance to chemotherapy and molecularly targeted therapies is a major factor in limiting the effectiveness of cancer treatment. In many cases, resistance can be linked to genetic changes in target proteins, either pre-existing or evolutionarily selected during treatment. Key to overcoming this challenge is an understanding of the molecular determinants of drug binding. Using multi-stage pipelines of molecular simulations we can gain insights into the binding free energy and the residence time of a ligand, which can inform both stratified and personal treatment regimes and drug development. To support the scalable, adaptive and automated calculation of the binding free energy on high-performance computing resources, we introduce the High-throughput Binding Affinity Calculator (HTBAC). HTBAC uses a building block approach in order to attain both workflow flexibility and performance.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(98707, 2018, 'bmc', 'Predicting tumor cell line response to drug pairs with deep learning', 'Xia, Fangfang|Shukla, Maulik|Brettin, Thomas|Garcia-Cardona, Cristina|Cohn, Judith|Allen, Jonathan E|Maslov, Sergei|Holbeck, Susan L|Doroshow, James H|Evrard, Yvonne A|Stahlberg, Eric A|Stevens, Rick L', 'Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL, USA. fangfang@anl.gov.|Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL, USA.|Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL, USA.|Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, USA.|Computer Science, Los Alamos National Laboratory, Los Alamos, NM, USA.|Computation Directorate, Lawrence Livermore National Laboratory, Livermore, CA, USA.|Department of Bioengineering and Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.|Developmental Therapeutics Branch, National Cancer Institute, Frederick, MD, USA.|Developmental Therapeutics Branch, National Cancer Institute, Frederick, MD, USA.|Developmental Therapeutics Branch, National Cancer Institute, Frederick, MD, USA.|Data Science and Information Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA.|Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 16, 'Cell Line, Tumor|Deep Learning|Drug Evaluation, Preclinical|Humans|National Cancer Institute (U.S.)|Neural Networks (Computer)|United States', 'Journal Article', '10.1186/s12859-018-2509-3', '30577754', 'The National Cancer Institute drug pair screening effort against 60 well-characterized human tumor cell lines (NCI-60) presents an unprecedented resource for modeling combinational drug activity.'),
(98708, 2018, 'bmc', 'CAT: computer aided triage improving upon the Bayes risk through xcexb5-refusal triage rules', 'Hengartner, Nicolas|Cuellar, Leticia|Wu, Xiao-Cheng|Tourassi, Georgia|Qiu, John|Christian, Blair|Bhattacharya, Tanmoy', 'Los Alamos National Laboratory, PO Box 1663, Los Alamos, 87545, NM, USA. nickh@lanl.gov.|Los Alamos National Laboratory, PO Box 1663, Los Alamos, 87545, NM, USA.|Louisiana State University, 2020 Gravier Street, 3rd Floor, New Orleans, 70112, LA, USA.|Oak Ridge National Laboratory, PO Box 2008, Oak Ridge, 37831, TN, USA.|Oak Ridge National Laboratory, PO Box 2008, Oak Ridge, 37831, TN, USA.|Oak Ridge National Laboratory, PO Box 2008, Oak Ridge, 37831, TN, USA.|Los Alamos National Laboratory, PO Box 1663, Los Alamos, 87545, NM, USA.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Bayes Theorem|Computers|Databases, Factual|Humans|Information Storage and Retrieval|Triage', 'Journal Article', '10.1186/s12859-018-2503-9', '30577756', 'Manual extraction of information from electronic pathology (epath) reports to populate the Surveillance, Epidemiology, and End Result (SEER) database is labor intensive. Systematizing the data extraction automatically using machine-learning (ML) and natural language processing (NLP) is desirable to reduce the human labor required to populate the SEER database and to improve the timeliness of the data. This enables scaling up registry efficiency and collection of new data elements. To ensure the integrity, quality, and continuity of the SEER data, the misclassification error of ML and NPL algorithms needs to be negligible. Current algorithms fail to achieve the precision of human experts who can bring additional information in their assessments. Differences in registry format and the desire to develop a common information extraction platform further complicate the ML/NLP tasks. The purpose of our study is to develop triage rules to partially automate registry workflow to improve the precision of the auto-extracted information.'),
(98709, 2018, 'bmc', 'Sequence-based bacterial small RNAs prediction using ensemble learning strategies', 'Tang, Guifeng|Shi, Jingwen|Wu, Wenjian|Yue, Xiang|Zhang, Wen', 'School of Computer Science, Wuhan University, Wuhan, 430072, China.|School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, China.|Electronic Information School, Wuhan University, Wuhan, 430072, China.|Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, 43210, USA.|College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China. zhangwen@whu.edu.cn.', 'China|China|China|United States|China', 0, 'Algorithms|Area Under Curve|Bacteria|Base Sequence|Benchmarking|Computational Biology|Databases, Nucleic Acid|Neural Networks (Computer)|RNA, Bacterial|RNA, Untranslated', 'Journal Article', '10.1186/s12859-018-2535-1', '30577759', 'Bacterial small non-coding RNAs (sRNAs) have emerged as important elements in diverse physiological processes, including growth, development, cell proliferation, differentiation, metabolic reactions and carbon metabolism, and attract great attention. Accurate prediction of sRNAs is important and challenging, and helps to explore functions and mechanism of sRNAs.'),
(98710, 2018, 'bmc', 'Capturing context-specific regulation in molecular interaction networks', 'Rush, Stephen T A|Repsilber, Dirk', 'School of Medical Sciences, Örebro University, Södra Grev Rosengatan, Örebro, Sweden.|School of Medical Sciences, Örebro University, Södra Grev Rosengatan, Örebro, Sweden. dirk.repsilber@oru.se.', 'Sweden|Sweden', 1, 'Air Pollutants|Algorithms|Computational Biology|Gene Expression Profiling|Gene Regulatory Networks|Humans|Pneumonia|Software', 'Journal Article', '10.1186/s12859-018-2513-7', '30577761', 'Molecular profiles change in response to perturbations. These changes are coordinated into functional modules via regulatory interactions. The genes and their products within a functional module are expected to be differentially expressed in a manner coherent with their regulatory network. This perspective presents a promising approach to increase precision in detecting differential signals as well as for describing differential regulatory signals within the framework of a priori knowledge about the underlying network, and so from a mechanistic point of view.'),
(98711, 2018, 'bmc', 'Deep clustering of protein folding simulations', 'Bhowmik, Debsindhu|Gao, Shang|Young, Michael T|Ramanathan, Arvind', 'Computational Science and Engineering Division, Oak Ridge National Laboratory, One Bethel Valley Road, MS6085, Oak Ridge, TN, USA.|Computational Science and Engineering Division, Oak Ridge National Laboratory, One Bethel Valley Road, MS6085, Oak Ridge, TN, USA.|Computational Science and Engineering Division, Oak Ridge National Laboratory, One Bethel Valley Road, MS6085, Oak Ridge, TN, USA.|Computational Science and Engineering Division, Oak Ridge National Laboratory, One Bethel Valley Road, MS6085, Oak Ridge, TN, USA. ramanathana@ornl.gov.', 'United States|United States|United States|United States', 7, 'Cluster Analysis|Molecular Dynamics Simulation|Protein Folding', 'Journal Article', '10.1186/s12859-018-2507-5', '30577777', 'We examine the problem of clustering biomolecular simulations using deep learning techniques. Since biomolecular simulation datasets are inherently high dimensional, it is often necessary to build low dimensional representations that can be used to extract quantitative insights into the atomistic mechanisms that underlie complex biological processes.'),
(98712, 2018, 'bmc', 'FELLA: an R package to enrich metabolomics data', 'Picart-Armada, Sergio|Fernández-Albert, Francesc|Vinaixa, Maria|Yanes, Oscar|Perera-Lluna, Alexandre', 'B2SLab, Departament dEnginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, Barcelona, 08028, Spain. sergi.picart@upc.edu.|B2SLab, Departament dEnginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, Barcelona, 08028, Spain.|Metabolomics Platform, IISPV, Department of Electronic Engineering (DEEEA), Universitat Rovira i Virgili, Tarragona, 43003, Spain.|Metabolomics Platform, IISPV, Department of Electronic Engineering (DEEEA), Universitat Rovira i Virgili, Tarragona, 43003, Spain.|B2SLab, Departament dEnginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, Barcelona, 08028, Spain.', 'Spain|Spain|Spain|Spain|Spain', 4, 'Animals|Computational Biology|Computer Graphics|Datasets as Topic|Female|Humans|Malaria|Metabolic Networks and Pathways|Metabolomics|Mice|Models, Biological|Non-alcoholic Fatty Liver Disease|Ovarian Neoplasms|Software|Zebrafish', 'Journal Article', '10.1186/s12859-018-2487-5', '30577788', 'Pathway enrichment techniques are useful for understanding experimental metabolomics data. Their purpose is to give context to the affected metabolites in terms of the prior knowledge contained in metabolic pathways. However, the interpretation of a prioritized pathway list is still challenging, as pathways show overlap and cross talk effects.'),
(98713, 2018, 'bmc', 'Rapid analysis of metagenomic data using signature-based clustering', 'Chappell, Timothy|Geva, Shlomo|Hogan, James M|Huygens, Flavia|Rathnayake, Irani U|Rudd, Stephen|Kelly, Wayne|Perrin, Dimitri', 'School of Electrical Engineering and Computer Science, Queensland University of Technology, 2 George Street, Brisbane, QLD 4001, Australia.|School of Electrical Engineering and Computer Science, Queensland University of Technology, 2 George Street, Brisbane, QLD 4001, Australia.|School of Electrical Engineering and Computer Science, Queensland University of Technology, 2 George Street, Brisbane, QLD 4001, Australia.|Institute of Health and Biomedical Innovation, Queensland University of Technology, 60 Musk Avenue, Kelvin Grove, QLD 4059, Australia.|Institute of Health and Biomedical Innovation, Queensland University of Technology, 60 Musk Avenue, Kelvin Grove, QLD 4059, Australia.|Queensland Facility for Advanced Bioinformatics (QFAB), Level 6 QBP (Bld 80), Chancellors place, The University of Queensland, St Lucia, Brisbane, QLD 4072, Australia.|School of Electrical Engineering and Computer Science, Queensland University of Technology, 2 George Street, Brisbane, QLD 4001, Australia.|School of Electrical Engineering and Computer Science, Queensland University of Technology, 2 George Street, Brisbane, QLD 4001, Australia. dimitri.perrin@qut.edu.au.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia', 0, 'Algorithms|Cluster Analysis|Data Analysis|Humans|Metagenomics|Microbiota', 'Journal Article', '10.1186/s12859-018-2540-4', '30577803', 'Sequencing highly-variable 16S regions is a common and often effective approach to the study of microbial communities, and next-generation sequencing (NGS) technologies provide abundant quantities of data for analysis. However, the speed of existing analysis pipelines may limit our ability to work with these quantities of data. Furthermore, the limited coverage of existing 16S databases may hamper our ability to characterise these communities, particularly in the context of complex or poorly studied environments.'),
(98714, 2018, 'bmc', 'Bidirectional long short-term memory with CRF for detecting biomedical event trigger in FastText semantic space', 'Wang, Yan|Wang, Jian|Lin, Hongfei|Tang, Xiwei|Zhang, Shaowu|Li, Lishuang', 'School of Computer Science and Technology, Dalian University of Technology, Dalian, China.|School of Computer Science and Technology, Dalian University of Technology, Dalian, China. wangjian@dlut.edu.cn.|School of Computer Science and Technology, Dalian University of Technology, Dalian, China.|School of Information Science and Engineering, Hunan First Normal University, Changsha, China. tangxiwei2010@gmail.com.|School of Computer Science and Technology, Dalian University of Technology, Dalian, China.|School of Computer Science and Technology, Dalian University of Technology, Dalian, China.', 'China|China|China|China|China|China', 6, 'Algorithms|Biomedical Research|Information Storage and Retrieval|Machine Learning|Semantics', 'Journal Article', '10.1186/s12859-018-2543-1', '30577839', 'In biomedical information extraction, event extraction plays a crucial role. Biological events are used to describe the dynamic effects or relationships between biological entities such as proteins and genes. Event extraction is generally divided into trigger detection and argument recognition. The performance of trigger detection directly affects the results of the event extraction. In general, the traditional method is used to address the trigger detection as a classification task, as well as the use of machine learning or rules method, which construct many features to improve the classification results. Moreover, the classification model only recognizes triggers composed of single words, whereas for multiple words, the result is unsatisfactory.'),
(98715, 2018, 'bmc', 'Web-based design and analysis tools for CRISPR base editing', 'Hwang, Gue-Ho|Park, Jeongbin|Lim, Kayeong|Kim, Sunghyun|Yu, Jihyeon|Yu, Eunchong|Kim, Sang-Tae|Eils, Roland|Kim, Jin-Soo|Bae, Sangsu', 'Department of Chemistry, Hanyang University, Seoul, South Korea.|Center for Digital Health, Berlin Institute of Health and Charitxc3xa9 Universitxc3xa4tsmedizin Berlin, Berlin, Germany.|Department of Chemistry, Seoul National University, Seoul, South Korea.|Department of Chemistry, Seoul National University, Seoul, South Korea.|Department of Chemistry, Hanyang University, Seoul, South Korea.|Department of Chemistry, Hanyang University, Seoul, South Korea.|Center for Genome Engineering, Institute for Basic Science, Daejeon, South Korea.|Center for Digital Health, Berlin Institute of Health and Charitxc3xa9 Universitxc3xa4tsmedizin Berlin, Berlin, Germany.|Department of Chemistry, Seoul National University, Seoul, South Korea.|Department of Chemistry, Hanyang University, Seoul, South Korea. sangsubae@hanyang.ac.kr.', '-|Germany|-|-|-|-|-|Germany|-|-', 0, 'CRISPR-Cas Systems|Clustered Regularly Interspaced Short Palindromic Repeats|Gene Editing|Humans|Internet', 'Journal Article', '10.1186/s12859-018-2585-4', '30587106', 'As a result of its simplicity and high efficiency, the CRISPR-Cas system has been widely used as a genome editing tool. Recently, CRISPR base editors, which consist of deactivated Cas9 (dCas9) or Cas9 nickase (nCas9) linked with a cytidine or a guanine deaminase, have been developed. Base editing tools will be very useful for gene correction because they can produce highly specific DNA substitutions without the introduction of any donor DNA, but dedicated web-based tools to facilitate the use of such tools have not yet been developed.'),
(98716, 2018, 'bmc', 'SMARTcleaner: identify and clean off-target signals in SMART ChIP-seq analysis', 'Zhao, Dejian|Zheng, Deyou', 'Department of Genetics, Albert Einstein College of Medicine, 1300 Morris Park Ave, Bronx, New York, USA.|Department of Genetics, Albert Einstein College of Medicine, 1300 Morris Park Ave, Bronx, New York, USA. deyou.zheng@einstein.yu.edu.', 'United States|United States', 0, 'Genomics|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA', 'Journal Article', '10.1186/s12859-018-2577-4', '30587107', 'Noises and artifacts may arise in several steps of the next-generation sequencing (NGS) process. Recently, an NGS library preparation method called SMART, or Switching Mechanism At the 5 end of the RNA Transcript, is introduced to prepare ChIP-seq (chromatin immunoprecipitation and deep sequencing) libraries from small amount of DNA material, using the DNA SMART ChIP-seq Kit. The protocol adds Ts to the 3 end of DNA templates, which is subsequently recognized and used by SMART poly(dA) primers for reverse transcription and then addition of PCR primers and sequencing adapters. The poly(dA) primers, however, can anneal to poly(T) sequences in a genome and amplify DNA fragments that are not enriched in the immunoprecipitated DNA templates. This off-target amplification results in false signals in the ChIP-seq data.'),
(98717, 2018, 'bmc', 'Degeneracy and genetic assimilation in RNA evolution', 'Rezazadegan, Reza|Reidys, Christian', 'University of Virginia Biocomplexity Institute, 995 Research Park Boulevard, Charlottesville, 22911, USA. rr7nz@virginia.edu.|University of Virginia Biocomplexity Institute, 995 Research Park Boulevard, Charlottesville, 22911, USA.', 'United States|United States', 0, 'Evolution, Molecular|Humans|Models, Genetic|RNA', 'Journal Article', '10.1186/s12859-018-2497-3', '30587112', 'The neutral theory of Motoo Kimura stipulates that evolution is mostly driven by neutral mutations. However adaptive pressure eventually leads to changes in phenotype that involve non-neutral mutations. The relation between neutrality and adaptation has been studied in the context of RNA before and here we further study transitional mutations in the context of degenerate (plastic) RNA sequences and genetic assimilation. We propose quasineutral mutations, i.e. mutations which preserve an element of the phenotype set, as minimal mutations and study their properties. We also propose a general probabilistic interpretation of genetic assimilation and specialize it to the Boltzmann ensemble of RNA sequences.'),
(98718, 2018, 'bmc', 'Reconstructing high-resolution chromosome three-dimensional structures by Hi-C complex networks', 'Liu, Tong|Wang, Zheng', 'Department of Computer Science, University of Miami, 1365 Memorial Drive, Coral Gables, FL, 33124, USA.|Department of Computer Science, University of Miami, 1365 Memorial Drive, Coral Gables, FL, 33124, USA. zheng.wang@miami.edu.', 'United States|United States', 0, 'Animals|Chromatin Immunoprecipitation|Chromosomes, Mammalian|Cluster Analysis|In Situ Hybridization, Fluorescence|Mice|Mouse Embryonic Stem Cells|RNA, Antisense|RNA, Long Noncoding', 'Journal Article', '10.1186/s12859-018-2464-z', '30591009', 'Hi-C data have been widely used to reconstruct chromosomal three-dimensional (3D) structures. One of the key limitations of Hi-C is the unclear relationship between spatial distance and the number of Hi-C contacts. Many methods used a fixed parameter when converting thexc2xa0number of Hi-C contacts to wish distances. However, a single parameter cannot properly explain the relationship between wish distances and genomic distances orxc2xa0thexc2xa0 locations of topologically associating domains (TADs).'),
(98719, 2018, 'bmc', 'DLAD4U: deriving and prioritizing disease lists from PubMed literature', 'Shen, Junhui|Vasaikar, Suhas|Zhang, Bing', 'Information Center, Beijing University of Chinese Medicine, Beijing, China.|Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, USA.|Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, USA. Bing.Zhang@bcm.edu.', 'China|United States|United States', 0, 'Disease|Genetic Association Studies|Humans|Information Storage and Retrieval|Internet|Nitric Oxide Synthase Type III|PubMed|Publications|Search Engine|Tumor Necrosis Factor-alpha', 'Journal Article', '10.1186/s12859-018-2463-0', '30591010', 'Due to recent technology advancements, disease related knowledge is growing rapidly. It becomes nontrivial to go through all published literature to identify associations between human diseases and genetic, environmental, and life style factors, disease symptoms, and treatment strategies. Here we report DLAD4U (Disease List Automatically Derived For You), an efficient, accurate and easy-to-use disease search engine based on PubMed literature.'),
(98720, 2018, 'bmc', 'A new insight into underlying disease mechanism through semi-parametric latent differential network model', 'He, Yong|Ji, Jiadong|Xie, Lei|Zhang, Xinsheng|Xue, Fuzhong', 'School of Statistics, Shandong University of Finance and Economics, Jinan, 250014, China.|School of Statistics, Shandong University of Finance and Economics, Jinan, 250014, China. jiadong@sdufe.edu.cn.|Department of Computer Science, Hunter College, The City University of New York, New York, 10065, USA.|School of Management, Fudan University, Shanghai, 200433, China.|School of Public Health, Shandong University, Jinan, 250012, China.', 'China|China|United States|China|China', 3, 'Computer Simulation|Disease|Gene Expression Regulation, Neoplastic|Gene Ontology|Gene Regulatory Networks|Humans|Lung Neoplasms|Models, Theoretical|Normal Distribution|ROC Curve', 'Journal Article', '10.1186/s12859-018-2461-2', '30591011', 'In genomic studies, to investigate how the structure of a genetic network differs between two experiment conditions is a very interesting but challenging problem, especially in high-dimensional setting. Existing literatures mostly focus on differential network modelling for continuous data. However, in real application, we may encounter discrete data or mixed data, which urges us to propose a unified differential network modelling for various data types.'),
(98721, 2018, 'bmc', 'The International Conference on Intelligent Biology and Medicine (ICIBM) 2018: bioinformatics towards translational applications', 'Liu, Xiaoming|Xie, Lei|Wu, Zhijin|Wang, Kai|Zhao, Zhongming|Ruan, Jianhua|Zhi, Degui', 'Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA. xiaoming.liu@uth.tmc.edu.|Department of Computer Science, Hunter College & The Graduate Center, The City University of New York, New York, NY, 10065, USA.|Department of Biostatistics, Brown University, Providence, RI, 02912, USA.|Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Childrens Hospital of Philadelphia, Philadelphia, PA, 19104, USA.|Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.|Department of Computer Science, The University of Texas at San Antonio, San Antonio, TX, 78249, USA.|Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA. degui.zhi@uth.tmc.edu.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Computational Biology|Electronic Health Records|Humans|Internationality|MCF-7 Cells|Medicine|Pharmacogenetics|Translational Medical Research', 'Editorial', '10.1186/s12859-018-2460-3', '30591012', 'The 2018 International Conference on Intelligent Biology and Medicine (ICIBM 2018) was held on June 10-12, 2018, in Los Angeles, California, USA. The conference consisted of a total of eleven scientific sessions, four tutorials, one poster session, four keynote talks and four eminent scholar talks, which covered a wild range of aspects of bioinformatics, medical informatics, systems biology and intelligent computing. Here, we summarize nine research articles selected for publishing in BMC Bioinformatics.'),
(98722, 2018, 'bmc', 'A multitask bi-directional RNN model for named entity recognition on Chinese electronic medical records', 'Chowdhury, Shanta|Dong, Xishuang|Qian, Lijun|Li, Xiangfang|Guan, Yi|Yang, Jinfeng|Yu, Qiubin', 'Center of Excellence in Research and Education for Big Military Data Intelligence (CREDIT), Department of Electrical and Computer Engineering, Prairie View A&M University, Texas A&M University System, Prairie View, Texas, 77446, USA.|Center of Excellence in Research and Education for Big Military Data Intelligence (CREDIT), Department of Electrical and Computer Engineering, Prairie View A&M University, Texas A&M University System, Prairie View, Texas, 77446, USA.|Center of Excellence in Research and Education for Big Military Data Intelligence (CREDIT), Department of Electrical and Computer Engineering, Prairie View A&M University, Texas A&M University System, Prairie View, Texas, 77446, USA.|Center of Excellence in Research and Education for Big Military Data Intelligence (CREDIT), Department of Electrical and Computer Engineering, Prairie View A&M University, Texas A&M University System, Prairie View, Texas, 77446, USA. xili@pvamu.edu.|Schools of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.|Schools of Software, Harbin University of Science and Technology, Harbin, China.|Second Affiliated Hospital of Harbin Medical University, Harbin, China.', 'United States|United States|United States|United States|China|China|China', 0, 'China|Electronic Health Records|Humans|Information Storage and Retrieval|Language|Models, Theoretical', 'Journal Article', '10.1186/s12859-018-2467-9', '30591015', 'Electronic Medical Record (EMR) comprises patients medical information gathered by medical stuff for providing better health care. Named Entity Recognition (NER) is a sub-field of information extraction aimed at identifying specific entity terms such as disease, test, symptom, genes etc. NER can be a relief for healthcare providers and medical specialists to extract useful information automatically and avoid unnecessary and unrelated information in EMR. However, limited resources of available EMR pose a great challenge for mining entity terms. Therefore, a multitask bi-directional RNN model is proposed here as a potential solution of data augmentation to enhance NER performance with limited data.'),
(98723, 2018, 'bmc', 'Application of transfer learning for cancer drug sensitivity prediction', 'Dhruba, Saugato Rahman|Rahman, Raziur|Matlock, Kevin|Ghosh, Souparno|Pal, Ranadip', 'Department of Electrical and Computer Engineering, Texas Tech University, 1012 Boston Ave, Lubbock, 79409, TX, USA.|Department of Electrical and Computer Engineering, Texas Tech University, 1012 Boston Ave, 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.|Department of Electrical and Computer Engineering, Texas Tech University, 1012 Boston Ave, Lubbock, 79409, TX, USA. ranadip.pal@ttu.edu.', 'United States|United States|United States|United States|United States', 4, 'Algorithms|Antineoplastic Agents|Area Under Curve|Databases, Factual|Gene Expression Regulation, Neoplastic|Humans|Neoplasms', 'Journal Article', '10.1186/s12859-018-2465-y', '30591023', 'In precision medicine, scarcity of suitable biological data often hinders the design of an appropriate predictive model. In this regard, large scale pharmacogenomics studies, like CCLE and GDSC hold the promise to mitigate the issue. However, one cannot directly employ data from multiple sources together due to the existing distribution shift in data. One way to solve this problem is to utilize the transfer learning methodologies tailored to fit in this specific context.'),
(98724, 2018, 'bmc', 'Large-scale mining disease comorbidity relationships from post-market drug adverse events surveillance data', 'Zheng, Chunlei|Xu, Rong', 'Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, 2103 Cornell Road, Cleveland, 44106, OH, USA.|Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, 2103 Cornell Road, Cleveland, 44106, OH, USA. rxx@case.edu.', 'United States|United States', 0, 'Algorithms|Comorbidity|Data Mining|Disease|Drug-Related Side Effects and Adverse Reactions|Gene Regulatory Networks|Humans|Product Surveillance, Postmarketing|Semantics|United States', 'Journal Article', '10.1186/s12859-018-2468-8', '30591027', 'Systems approaches in studying disease relationship have wide applications in biomedical discovery, such as disease mechanism understanding and drug discovery. The FDA Adverse Event Reporting System (FAERS) contains rich information about patient diseases, medications, drug adverse events and demographics of 17 million case reports. Here, we explored this data resource to mine disease comorbidity relationships using association rule mining algorithm and constructed a disease comorbidity network.'),
(98725, 2018, 'bmc', 'iMEGES: integrated mental-disorder GEnome score by deep neural network for prioritizing the susceptibility genes for mental disorders in personal genomes', 'Khan, Atlas|Liu, Qian|Wang, Kai', 'Division of Nephrology, Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, NY, 10032, USA.|Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Childrens Hospital of Philadelphia, Philadelphia, PA, 19104, USA.|Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Childrens Hospital of Philadelphia, Philadelphia, PA, 19104, USA. wangk@email.chop.edu.', 'United States|United States|United States', 4, 'Area Under Curve|Databases, Genetic|Deep Learning|Genetic Predisposition to Disease|Genome, Human|Humans|Mental Disorders|Neural Networks (Computer)|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1186/s12859-018-2469-7', '30591030', 'A range of rare and common genetic variants have been discovered to be potentially associated with mental diseases, but many more have not been uncovered. Powerful integrative methods are needed to systematically prioritize both variants and genes that confer susceptibility to mental diseases in personal genomes of individual patients and to facilitate the development of personalized treatment or therapeutic approaches.'),
(98726, 2018, 'bmc', 'Evaluation of top-down mass spectral identification with homologous protein sequences', 'Li, Ziwei|He, Bo|Kou, Qiang|Wang, Zhe|Wu, Si|Liu, Yunlong|Feng, Weixing|Liu, Xiaowen', 'College of Automation, Harbin Engineering University, 145, Nan Tong Street, Harbin, Heilongjiang, 150001, China.|College of Automation, Harbin Engineering University, 145, Nan Tong Street, Harbin, Heilongjiang, 150001, China.|Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, 719 Indiana Avenue, Indianapolis, IN, 46202, USA.|Department of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Parkway, Norman, OK, 73019, USA.|Department of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Parkway, Norman, OK, 73019, USA.|Department of Medical and Molecular Genetics, Indiana University School of Medicine, 410 West 10th Street, Indianapolis, IN, 46202, USA. yunliu@iu.edu.|College of Automation, Harbin Engineering University, 145, Nan Tong Street, Harbin, Heilongjiang, 150001, China. fengweixing@hrbeu.edu.cn.|Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, 719 Indiana Avenue, Indianapolis, IN, 46202, USA. xwliu@iupui.edu.', 'China|China|India|United States|United States|India|China|India', 0, 'Amino Acid Sequence|Databases, Protein|Escherichia coli|Humans|MCF-7 Cells|Proteins|Proteome|Sequence Homology, Amino Acid|Software|Tandem Mass Spectrometry', 'Journal Article', '10.1186/s12859-018-2462-1', '30591035', 'Top-down mass spectrometry has unique advantages in identifying proteoforms with multiple post-translational modifications and/or unknown alterations. Most software tools in this area search top-down mass spectra against a protein sequence database for proteoform identification. When the species studied in a mass spectrometry experiment lacks its proteome sequence database, a homologous protein sequence database can be used for proteoform identification. The accuracy of homologous protein sequences affects the sensitivity of proteoform identification and the accuracy of mass shift localization.'),
(98727, 2018, 'bmc', 'Predicting adverse drug reactions through interpretable deep learning framework', 'Dey, Sanjoy|Luo, Heng|Fokoue, Achille|Hu, Jianying|Zhang, Ping', 'Center for Computational Health, IBM T.J. Watson Research Center, 1101 Kitchawan Road, Yorktown Heights, NY, USA.|Center for Computational Health, IBM T.J. Watson Research Center, 1101 Kitchawan Road, Yorktown Heights, NY, USA.|Cognitive Computing, IBM T.J. Watson Research Center, 1101 Kitchawan Road, Yorktown Heights, NY, USA.|Center for Computational Health, IBM T.J. Watson Research Center, 1101 Kitchawan Road, Yorktown Heights, NY, USA.|Center for Computational Health, IBM T.J. Watson Research Center, 1101 Kitchawan Road, Yorktown Heights, NY, USA. mail.pingzhang@gmail.com.', 'United States|United States|United States|United States|United States', 0, 'Area Under Curve|Back Pain|Deep Learning|Drug-Related Side Effects and Adverse Reactions|Humans|Models, Theoretical|Necrosis', 'Journal Article', '10.1186/s12859-018-2544-0', '30591036', 'Adverse drug reactions (ADRs) are unintended and harmful reactions caused by normal uses of drugs. Predicting and preventing ADRs in the early stage of the drug development pipeline can help to enhance drug safety and reduce financial costs.'),
(98728, 2018, 'bmc', 'Using natural language processing and machine learning to identify breast cancer local recurrence', 'Zeng, Zexian|Espino, Sasa|Roy, Ankita|Li, Xiaoyu|Khan, Seema A|Clare, Susan E|Jiang, Xia|Neapolitan, Richard|Luo, Yuan', 'Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.|Department of Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.|Department of Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.|Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA.|Department of Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.|Department of Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.|Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.|Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.|Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA. yuan.luo@northwestern.edu.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 10, 'Breast Neoplasms|Cohort Studies|Electronic Health Records|Female|Humans|Machine Learning|Natural Language Processing|Neoplasm Recurrence, Local|Reproducibility of Results|Support Vector Machine|Unified Medical Language System', 'Journal Article', '10.1186/s12859-018-2466-x', '30591037', 'Identifying local recurrences in breast cancer from patient data sets is important for clinical research and practice. Developing a model using natural language processing and machine learning to identify local recurrences in breast cancer patients can reduce the time-consuming work of a manual chart review.'),
(98729, 2018, 'bmc', 'Time-lagged Ordered Lasso for network inference', 'Nguyen, Phan|Braun, Rosemary', 'Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, IL, USA.|Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, IL, USA. rbraun@northwestern.edu.', 'United States|United States', 2, 'Gene Expression|Gene Regulatory Networks|Humans|Regression Analysis', 'Journal Article', '10.1186/s12859-018-2558-7', '30594121', 'Accurate gene regulatory networks can be used to explain the emergence of different phenotypes, disease mechanisms, and other biological functions. Many methods have been proposed to infer networks from gene expression data but have been hampered by problems such as low sample size, inaccurate constraints, and incomplete characterizations of regulatory dynamics. Since expression regulation is dynamic, time-course data can be used to infer causality, but these datasets tend to be short or sparsely sampled. In addition, temporal methods typically assume that the expression of a gene at a time point depends on the expression of other genes at only the immediately preceding time point, while other methods include additional time points without any constraints to account for their temporal distance. These limitations can contribute to inaccurate networks with many missing and anomalous links.'),
(98730, 2018, 'bmc', 'hoDCA: higher order direct-coupling analysis', 'Schmidt, Michael|Hamacher, Kay', 'Department of Physics, TU Darmstadt, Karolinenpl. 5, Darmstadt, 64287, Germany. schmidt@cbs.tu-darmstadt.de.|Department of Physics, TU Darmstadt, Karolinenpl. 5, Darmstadt, 64287, Germany.', 'Germany|Germany', 0, 'Humans|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1186/s12859-018-2583-6', '30594145', 'Direct-coupling analysis (DCA) is a method for protein contact prediction from sequence information alone. Its underlying principle is parameter estimation for a Hamiltonian interaction function stemming from a maximum entropy model with one- and two-point interactions. Vastly growing sequence databases enable the construction of large multiple sequence alignments (MSA). Thus, enough data exists to include higher order terms, such as three-body correlations.'),
(98731, 2018, 'bmc', 'Predicting adverse drug reactions of combined medication from heterogeneous pharmacologic databases', 'Zheng, Yi|Peng, Hui|Zhang, Xiaocai|Zhao, Zhixun|Yin, Jie|Li, Jinyan', 'Advanced Analytics Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, 15 Broadway Ultimo, Sydney, 2007, Australia.|Advanced Analytics Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, 15 Broadway Ultimo, Sydney, 2007, Australia.|Advanced Analytics Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, 15 Broadway Ultimo, Sydney, 2007, Australia.|Advanced Analytics Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, 15 Broadway Ultimo, Sydney, 2007, Australia.|Discipline of Business Analytics, The University of Sydney, Darlington, Sydney, 2006, Australia.|Advanced Analytics Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, 15 Broadway Ultimo, Sydney, 2007, Australia. Jinyan.Li@uts.edu.au.', 'Australia|Australia|Australia|Australia|Australia|Australia', 0, 'Adverse Drug Reaction Reporting Systems|Algorithms|Databases, Pharmaceutical|Drug Interactions|Drug-Related Side Effects and Adverse Reactions|Gene Regulatory Networks|Humans|Models, Statistical|Pharmaceutical Preparations|Predictive Value of Tests', 'Journal Article', '10.1186/s12859-018-2520-8', '30598065', 'Early and accurate identification of potential adverse drug reactions (ADRs) for combined medication is vital for public health. Existing methods either rely on expensive wet-lab experiments or detecting existing associations from related records. Thus, they inevitably suffer under-reporting, delays in reporting, and inability to detect ADRs for new and rare drugs. The current application of machine learning methods is severely impeded by the lack of proper drug representation and credible negative samples. Therefore, a method to represent drugs properly and to select credible negative samples becomes vital in applying machine learning methods to this problem.'),
(98732, 2018, 'bmc', 'Identification of pre-microRNAs by characterizing their sequence order evolution information and secondary structure graphs', 'Ma, Yuanlin|Yu, Zuguo|Han, Guosheng|Li, Jinyan|Anh, Vo', 'Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Hunan, 411105, China.|Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Hunan, 411105, China. yuzuguo@aliyun.com.|Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Hunan, 411105, China.|Advanced Analytics Institute, Faculty of Engineering & IT, University of Technology Sydney, P.O Box 123, Broadway, NSW 2007, Australia.|Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Hunan, 411105, China.', 'China|China|China|Australia|China', 0, 'Algorithms|Computational Biology|Genome, Human|Humans|Machine Learning|MicroRNAs|Support Vector Machine', 'Journal Article', '10.1186/s12859-018-2518-2', '30598066', 'Distinction between pre-microRNAs (precursor microRNAs) and length-similar pseudo pre-microRNAs can reveal more about the regulatory mechanism of RNA biological processes. Machine learning techniques have been widely applied to deal with this challenging problem. However, most of them mainly focus on secondary structure information of pre-microRNAs, while ignoring sequence-order information and sequence evolution information.'),
(98733, 2018, 'bmc', 'Estimating heterogeneous treatment effect by balancing heterogeneity and fitness', 'Zhang, Weijia|Le, Thuc Duy|Liu, Lin|Li, Jiuyong', 'School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, Australia. weijia.zhang@mymail.unisa.edu.au.|School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, Australia.', 'Australia|Australia|Australia|Australia', 0, 'Biomarkers, Tumor|Breast Neoplasms|Data Interpretation, Statistical|Female|Gene Expression Profiling|Humans|Models, Theoretical|Radiotherapy|Survival Rate|Treatment Outcome', 'Journal Article', '10.1186/s12859-018-2521-7', '30598067', 'Estimating heterogeneous treatment effect is a fundamental problem in biological and medical applications. Recently, several recursive partitioning methods have been proposed to identify the subgroups that respond differently towards a treatment, and they rely on a fitness criterion to minimize the error between the estimated treatment effects and the unobservable ground truths.'),
(98734, 2018, 'bmc', 'DeepM6ASeq: prediction and characterization of m6A-containing sequences using deep learning', 'Zhang, Yiqian|Hamada, Michiaki', 'Department of Electrical Engineering and Bioscience, Faculty of Science and Engineering, Waseda University, 55N-06-10, 3-4-1 Okubo Shinjuku-ku, Tokyo, 169-8555, Japan.|Department of Electrical Engineering and Bioscience, Faculty of Science and Engineering, Waseda University, 55N-06-10, 3-4-1 Okubo Shinjuku-ku, Tokyo, 169-8555, Japan. mhamada@waseda.jp.', 'Japan|Japan', 0, 'Adenosine|Alternative Splicing|Animals|Brain|Carcinoma, Hepatocellular|Computational Biology|Deep Learning|Embryonic Stem Cells|Humans|Liver Neoplasms|Methylation|Mice|RNA|Sequence Analysis, RNA|Zebrafish', 'Journal Article', '10.1186/s12859-018-2516-4', '30598068', 'N6-methyladensine (m6A) is a common and abundant RNA methylation modification found in various species. As a type of post-transcriptional methylation, m6A plays an important role in diverse RNA activities such as alternative splicing, an interplay with microRNAs and translation efficiency. Although existing tools can predict m6A at single-base resolution, it is still challenging to extract the biological information surrounding m6A sites.'),
(98735, 2018, 'bmc', 'In silico design of MHC class I high binding affinity peptides through motifs activation map', 'Xiao, Zhoujian|Zhang, Yuwei|Yu, Runsheng|Chen, Yin|Jiang, Xiaosen|Wang, Ziwei|Li, Shuaicheng', 'South China Normal University, HEMC (Guangzhou Higher Education Mega Center) campus, GuangZhou, China.|City University of Hong Kong, 83 Tat Chee Ave, Kowloon Tong, Hong Kong, China.|South China Normal University, HEMC (Guangzhou Higher Education Mega Center) campus, GuangZhou, China.|Huazhong University of Science and Technology, Luoyu Road 1037, Wuhan, China.|Huazhong University of Science and Technology, Luoyu Road 1037, Wuhan, China.|Hunan Agricultural University, Nongda Road, ChangSha, China.|City University of Hong Kong, 83 Tat Chee Ave, Kowloon Tong, Hong Kong, China. shuaicli@cityu.edu.hk.', 'China|China|China|China|China|China|China', 1, 'Algorithms|Alleles|Amino Acid Motifs|Computer Simulation|Histocompatibility Antigens Class I|Humans|Oligopeptides|Peptide Fragments|Protein Binding|Sequence Analysis, Protein', 'Journal Article', '10.1186/s12859-018-2517-3', '30598069', 'Finding peptides with high binding affinity to Class I major histocompatibility complex (MHC-I) attracts intensive research, and it serves a crucial part of developing a better vaccine for precision medicine. Traditional methods cost highly for designing such peptides. The advancement of computational approaches reduces the cost of new drug discovery dramatically. Compared with flourishing computational drug discovery area, the immunology area lacks tools focused on in silico design for the peptides with high binding affinity. Attributed to the ever-expanding amount of MHC-peptides binding data, it enables the tremendous influx of deep learning techniques for modeling MHC-peptides binding. To leverage the availability of these data, it is of great significance to find MHC-peptides binding specificities. The binding motifs are one of the key components to decide the MHC-peptides combination, which generally refer to a combination of some certain amino acids at certain sites which highly contribute to the binding affinity.'),
(98736, 2018, 'bmc', 'A benchmark study of sequence alignment methods for protein clustering', 'Wang, Yingying|Wu, Hongyan|Cai, Yunpeng', 'Research Center for Biomedical Information Technology, Shenzhen Institutes of Advanced Technologies, Chinese Academy of Sciences, Shenzhen, China.|Research Center for Biomedical Information Technology, Shenzhen Institutes of Advanced Technologies, Chinese Academy of Sciences, Shenzhen, China. hy.wu@siat.ac.cn.|Research Center for Biomedical Information Technology, Shenzhen Institutes of Advanced Technologies, Chinese Academy of Sciences, Shenzhen, China. yp.cai@siat.ac.cn.', 'China|China|China', 1, 'Algorithms|Benchmarking|Cluster Analysis|Computational Biology|Humans|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/s12859-018-2524-4', '30598070', 'Protein sequence alignment analyses have become a crucial step for many bioinformatics studies during the past decades. Multiple sequence alignment (MSA) and pair-wise sequence alignment (PSA) are two major approaches in sequence alignment. Former benchmark studies revealed drawbacks of MSA methods on nucleotide sequence alignments. To test whether similar drawbacks also influence protein sequence alignment analyses, we propose a new benchmark framework for protein clustering based on cluster validity. This new framework directly reflects the biological ground truth of the application scenarios that adopt sequence alignments, and evaluates the alignment quality according to the achievement of the biological goal, rather than the comparison on sequence level only, which averts the biases introduced by alignment scores or manual alignment templates. Compared with former studies, we calculate the cluster validity score based on sequence distances instead of clustering results. This strategy could avoid the influence brought by different clustering methods thus make results more dependable.'),
(98737, 2018, 'bmc', 'Single-cell gene expression analysis reveals xcexb2-cell dysfunction and deficit mechanisms in type 2 diabetes', 'Ma, Lichun|Zheng, Jie', 'Biomedical Informatics Lab, School of Computer Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore.|School of Information Science and Technology, ShanghaiTech University, Shanghai, 201210, China. zhengjie@shanghaitech.edu.cn.', 'Singapore|China', 0, 'Biomarkers|Case-Control Studies|Diabetes Mellitus, Type 2|Gene Expression Profiling|Gene Expression Regulation|Genome, Human|Humans|Insulin-Secreting Cells|Signal Transduction|Single-Cell Analysis|Systems Biology', 'Journal Article', '10.1186/s12859-018-2519-1', '30598071', 'Type 2 diabetes (T2D) is one of the most common chronic diseases. Studies on T2D are mainly built upon bulk-cell data analysis, which measures the average gene expression levels for a population of cells and cannot capture the inter-cell heterogeneity. The single-cell RNA-sequencing technology can provide additional information about the molecular mechanisms of T2D at single-cell level.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(98738, 2018, 'bmc', 'Computational prediction of plasma protein binding of cyclic peptides from small molecule experimental data using sparse modeling techniques', 'Tajimi, Takashi|Wakui, Naoki|Yanagisawa, Keisuke|Yoshikawa, Yasushi|Ohue, Masahito|Akiyama, Yutaka', 'Department of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 W8-76 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan.|Department of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 W8-76 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan.|Department of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 W8-76 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan.|Department of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 W8-76 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan.|Department of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 W8-76 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan.|Department of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 W8-76 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan. akiyama@c.titech.ac.jp.', 'Japan|Japan|Japan|Japan|Japan|Japan', 0, 'Blood Proteins|Computer Simulation|Humans|Machine Learning|Models, Theoretical|Peptides, Cyclic|Pharmaceutical Preparations|Protein Binding|Small Molecule Libraries', 'Journal Article', '10.1186/s12859-018-2529-z', '30598072', 'Cyclic peptide-based drug discovery is attracting increasing interest owing to its potential to avoid target protein depletion. In drug discovery, it is important to maintain the biostability of a drug within the proper range. Plasma protein binding (PPB) is the most important index of biostability, and developing a computational method to predict PPB of drug candidate compounds contributes to the acceleration of drug discovery research. PPB prediction of small molecule drug compounds using machine learning has been conducted thus far, however, no study has investigated cyclic peptides because experimental information of cyclic peptides is scarce.'),
(98739, 2018, 'bmc', 'PDRLGB: precise DNA-binding residue prediction using a light gradient boosting machine', 'Deng, Lei|Pan, Juan|Xu, Xiaojie|Yang, Wenyi|Liu, Chuyao|Liu, Hui', 'School of Software, Central South University, Changsha, 410075, China.|School of Software, Central South University, Changsha, 410075, China.|School of Software, Central South University, Changsha, 410075, China.|School of Software, Central South University, Changsha, 410075, China.|School of Software, Central South University, Changsha, 410075, China.|Lab of Information Management, Changzhou University, Changzhou, 213164, China. hliu@cczu.edu.cn.', 'China|China|China|China|China|China', 7, 'Algorithms|Binding Sites|Computational Biology|DNA|DNA-Binding Proteins|Humans|Machine Learning|Protein Binding|Protein Conformation|Support Vector Machine', 'Journal Article', '10.1186/s12859-018-2527-1', '30598073', 'Identifying specific residues for protein-DNA interactions are of considerable importance to better recognize the binding mechanism of protein-DNA complexes. Despite the fact that many computational DNA-binding residue prediction approaches have been developed, there is still significant room for improvement concerning overall performance and availability.'),
(98740, 2018, 'bmc', 'Classifying early and late mild cognitive impairment stages of Alzheimers disease by fusing default mode networks extracted with multiple seeds', 'Pei, Shengbing|Guan, Jihong|Zhou, Shuigeng', 'Department of Computer Science and Technology, Tongji University, 4800 Cao An Road, Shanghai, 201800, China.|Department of Computer Science and Technology, Tongji University, 4800 Cao An Road, Shanghai, 201800, China. jhguan@tongji.edu.cn.|Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, Fudan University, 220 Handan Road, Shanghai, 200433, China.', 'China|China|China', 0, 'Aged|Alzheimer Disease|Brain Mapping|Cognitive Dysfunction|Female|Humans|Magnetic Resonance Imaging|Male|Nerve Net', 'Journal Article', '10.1186/s12859-018-2528-0', '30598074', 'The default mode network (DMN) in resting state has been increasingly used in disease diagnosis since it was found in 2001. Prior work has mainly focused on extracting a single DMN with various techniques. However, by using seeding-based analysis with more than one desirable seed, we can obtain multiple DMNs, which are likely to have complementary information, and thus are more promising for disease diagnosis. In the study, we used 18 early mild cognitive impairment (EMCI) participants and 18 late mild cognitive impairment (LMCI) participants of Alzheimers disease (AD). First, we used seeding-based analysis with four seeds to extract four DMNs for each subject. Then, we conducted fusion analysis for all different combinations of the four DMNs. Finally, we carried out nonlinear support vector machine classification based on the mixing coefficients from the fusion analysis.'),
(98741, 2018, 'bmc', 'Convolutional neural network based on SMILES representation of compounds for detecting chemical motif', 'Hirohara, Maya|Saito, Yutaka|Koda, Yuki|Sato, Kengo|Sakakibara, Yasubumi', 'Department of Biosciences and Informatics, Keio University, Yokohama, 223-8522, Japan.|Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, 135-0064, Japan.|Department of Biosciences and Informatics, Keio University, Yokohama, 223-8522, Japan.|Department of Biosciences and Informatics, Keio University, Yokohama, 223-8522, Japan.|Department of Biosciences and Informatics, Keio University, Yokohama, 223-8522, Japan. yasu@bio.keio.ac.jp.', 'Japan|Japan|Japan|Japan|Japan', 0, 'Binding Sites|DNA|Deep Learning|Humans|Models, Chemical|Neural Networks (Computer)|Pharmaceutical Preparations|Protein Binding|Proteins|Software', 'Journal Article', '10.1186/s12859-018-2523-5', '30598075', 'Previous studies have suggested deep learning to be a highly effective approach for screening lead compounds for new drugs. Several deep learning models have been developed by addressing the use of various kinds of fingerprints and graph convolution architectures. However, these methods are either advantageous or disadvantageous depending on whether they (1) can distinguish structural differences including chirality of compounds, and (2) can automatically discover effective features.'),
(98742, 2018, 'bmc', 'DWNN-RLS: regularized least squares method for predicting circRNA-disease associations', 'Yan, Cheng|Wang, Jianxin|Wu, Fang-Xiang', 'School of Information Science and Engineering, Central South University, 932 South Lushan Rd, ChangSha, 410083, China.|School of Information Science and Engineering, Central South University, 932 South Lushan Rd, ChangSha, 410083, China. jxwang@mail.csu.edu.cn.|Biomedical Engineering and Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SKS7N5A9, Canada.', 'China|China|Canada', 0, 'Breast Neoplasms|Cluster Analysis|Computer Simulation|Coronary Artery Disease|Female|Gene Expression Profiling|Humans|Least-Squares Analysis|RNA|Risk Factors', 'Journal Article', '10.1186/s12859-018-2522-6', '30598076', 'Many evidences have demonstrated that circRNAs (circular RNA) play important roles in controlling gene expression of human, mouse and nematode. More importantly, circRNAs are also involved in many diseases through fine tuning of post-transcriptional gene expression by sequestering the miRNAs which associate with diseases. Therefore, identifying the circRNA-disease associations is very appealing to comprehensively understand the mechanism, treatment and diagnose of diseases, yet challenging. As the complex mechanism between circRNAs and diseases, wet-lab experiments are expensive and time-consuming to discover novel circRNA-disease associations. Therefore, it is of dire need to employ the computational methods to discover novel circRNA-disease associations.'),
(98743, 2018, 'bmc', 'Constructing a database for the relations between CNV and human genetic diseases via systematic text mining', 'Yang, Xi|Song, Zhuo|Wu, Chengkun|Wang, Wei|Li, Gen|Zhang, Wei|Wu, Lingqian|Lu, Kai', 'School of Computer Science, National University of Defense Technology, Changsha, 410073, China.|Genetalks Biotech Inc., Beijing, 100176, China.|School of Computer Science, National University of Defense Technology, Changsha, 410073, China. chengkun_wu@nudt.edu.cn.|School of Computer Science, National University of Defense Technology, Changsha, 410073, China.|Genetalks Biotech Inc., Beijing, 100176, China.|Genetalks Biotech Inc., Beijing, 100176, China.|Center for Medical Genetics, Central South University, 110 Xiangya Road, Changsha, 410078, Hunan, China. wulingqian@sklmg.edu.cn.|School of Computer Science, National University of Defense Technology, Changsha, 410073, China. kailu@nudt.edu.cn.', 'China|China|China|China|China|China|China|China', 0, 'Computational Biology|DNA Copy Number Variations|Data Mining|Databases, Factual|Disease|Genetic Testing|Human Genetics|Humans', 'Journal Article', '10.1186/s12859-018-2526-2', '30598077', 'The detection and interpretation of CNVs are of clinical importance in genetic testing. Several databases and web services are already being used by clinical geneticists to interpret the medical relevance of identified CNVs in patients. However, geneticists or physicians would like to obtain the original literature context for more detailed information, especially for rare CNVs that were not included in databases.'),
(98744, 2018, 'bmc', 'Predicting protein-protein interactions using high-quality non-interacting pairs', 'Zhang, Long|Yu, Guoxian|Guo, Maozu|Wang, Jun', 'College of Computer and Information Sciences, Southwest University, Chongqing, China.|College of Computer and Information Sciences, Southwest University, Chongqing, China.|School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China.|College of Computer and Information Sciences, Southwest University, Chongqing, China. kingjun@swu.edu.cn.', 'China|China|China|China', 5, 'Animals|Computational Biology|Humans|Mice|Neural Networks (Computer)|Protein Interaction Mapping|Proteins|Saccharomyces cerevisiae|Sequence Analysis, Protein', 'Journal Article', '10.1186/s12859-018-2525-3', '30598096', 'Identifying protein-protein interactions (PPIs) is of paramount importance for understanding cellular processes. Machine learning-based approaches have been developed to predict PPIs, but the effectiveness of these approaches is unsatisfactory. One major reason is that they randomly choose non-interacting protein pairs (negative samples) or heuristically select non-interacting pairs with low quality.'),
(98745, 2018, 'bmc', 'A latent allocation model for the analysis of microbial composition and disease', 'Abe, Ko|Hirayama, Masaaki|Ohno, Kinji|Shimamura, Teppei', 'Division of Systems Biology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 4668550, Japan.|School of Health Sciences, Nagoya University Graduate School of Medicine, 1-1-20 Daiko-Minami, Higashi-ku, Nagoya, 61-8873, Japan.|Division of Neurogenetics, Center for Neurological Diseases and Cancer, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 4668550, Japan.|Division of Systems Biology, Nagoya university Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 4668550, Japan. shimamura@med.nagoya-u.ac.jp.', 'Japan|Japan|Japan|Japan', 1, 'Algorithms|Bacteria|Case-Control Studies|Colorectal Neoplasms|Computer Simulation|Humans|Microbiota|Models, Statistical|Parkinson Disease', 'Journal Article', '10.1186/s12859-018-2530-6', '30598099', 'Establishing the relationship between microbiota and specific diseases is important but requires appropriate statistical methodology. A specialized feature of microbiome count data is the presence of a large number of zeros, which makes it difficult to analyze in case-control studies. Most existing approaches either add a small number called a pseudo-count or use probability models such as the multinomial and Dirichlet-multinomial distributions to explain the excess zero counts, which may produce unnecessary biases and impose a correlation structure taht is unsuitable for microbiome data.'),
(98746, 2018, 'bmc', 'miRBaseConverter: an R/Bioconductor package for converting and retrieving miRNA name, accession, sequence and family information in different versions of miRBase', 'Xu, Taosheng|Su, Ning|Liu, Lin|Zhang, Junpeng|Wang, Hongqiang|Zhang, Weijia|Gui, Jie|Yu, Kui|Li, Jiuyong|Le, Thuc Duy', 'Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China.|Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095 Adelaide, Australia.|School of Engineering, Dali University, Dali, 671003, China.|Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095 Adelaide, Australia.|Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, 48109, United States.|School of Computer and Information, Hefei University of Technology, Hefei, 230009, China.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095 Adelaide, Australia.|School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095 Adelaide, Australia. Thuc.Le@unisa.edu.au.', 'China|China|Australia|China|China|Australia|United States|China|Australia|Australia', 6, 'Algorithms|Animals|Computational Biology|Databases, Nucleic Acid|Gene Ontology|Humans|MicroRNAs|Molecular Sequence Annotation|Software|Terminology as Topic', 'Journal Article', '10.1186/s12859-018-2531-5', '30598108', 'miRBase is the primary repository for published miRNA sequence and annotation data, and serves as the go-to place for miRNA research. However, the definition and annotation of miRNAs have been changed significantly across different versions of miRBase. The changes cause inconsistency in miRNA related data between different databases and articles published at different times. Several tools have been developed for different purposes of querying and converting the information of miRNAs between different miRBase versions, but none of them individually can provide the comprehensive information about miRNAs in miRBase and users will need to use a number of different tools in their analyses.'),
(98747, 2019, 'bmc', 'Image-based classification of plant genus and family for trained and untrained plant species', 'Seeland, Marco|Rzanny, Michael|Boho, David|Wäldchen, Jana|Mäder, Patrick', 'Institute for Computer and Systems Engineering, Technische Universität Ilmenau, Helmholtzplatz 5, Ilmenau, 98693, Germany. marco.seeland@tu-ilmenau.de.|Max-Planck-Institute for Biogeochemistry, Department Biogeochemical Integration, Hans-Knöll-Str. 10, Jena, 07745, Germany.|Institute for Computer and Systems Engineering, Technische Universität Ilmenau, Helmholtzplatz 5, Ilmenau, 98693, Germany.|Max-Planck-Institute for Biogeochemistry, Department Biogeochemical Integration, Hans-Knöll-Str. 10, Jena, 07745, Germany.|Institute for Computer and Systems Engineering, Technische Universität Ilmenau, Helmholtzplatz 5, Ilmenau, 98693, Germany.', 'Germany|Germany|Germany|Germany|Germany', 0, 'Phylogeny|Plants', 'Journal Article', '10.1186/s12859-018-2474-x', '30606100', 'Modern plant taxonomy reflects phylogenetic relationships among taxa based on proposed morphological and genetic similarities. However, taxonomical relation is not necessarily reflected by close overall resemblance, but rather by commonality of very specific morphological characters or similarity on the molecular level. It is an open research question to which extent phylogenetic relations within higher taxonomic levels such as genera and families are reflected by shared visual characters of the constituting species. As a consequence, it is even more questionable whether the taxonomy of plants at these levels can be identified from images using machine learning techniques.'),
(98748, 2019, 'bmc', 'NCLcomparator: systematically post-screening non-co-linear transcripts (circular, trans-spliced, or fusion RNAs) identified from various detectors', 'Chen, Chia-Ying|Chuang, Trees-Juen', 'Genomics Research Center, Academia Sinica, Taipei, 11529, Taiwan.|Genomics Research Center, Academia Sinica, Taipei, 11529, Taiwan. trees@gate.sinica.edu.tw.', '-|-', 3, 'Gene Fusion|Genome|RNA|RNA Splicing|Sequence Analysis, RNA', 'Journal Article', '10.1186/s12859-018-2589-0', '30606103', 'Non-co-linear (NCL) transcripts consist of exonic sequences that are topologically inconsistent with the reference genome in an intragenic fashion (circular or intragenic trans-spliced RNAs) or in an intergenic fashion (fusion or intergenic trans-spliced RNAs). On the basis of RNA-seq data, numerous NCL event detectors have been developed and detected thousands of NCL events in diverse species. However, there are great discrepancies in the identification results among detectors, indicating a considerable proportion of false positives in the detected NCL events. Although several helpful guidelines for evaluating the performance of NCL event detectors have been provided, a systematic guideline for measurement of NCL events identified by existing tools has not been available.'),
(98749, 2019, 'bmc', 'DBS: a fast and informative segmentation algorithm for DNA copy number analysis', 'Ruan, Jun|Liu, Zhen|Sun, Ming|Wang, Yue|Yue, Junqiu|Yu, Guoqiang', 'School of Information Engineering, Wuhan University of Technology, Wuhan, Hubei, 430070, China.|School of Information Engineering, Wuhan University of Technology, Wuhan, Hubei, 430070, China.|School of Information Engineering, Wuhan University of Technology, Wuhan, Hubei, 430070, China.|Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, 22203, USA.|Department of Pathology, Hubei Cancer Hospital, Wuhan, Hubei, 430079, China.|Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, 22203, USA. yug@vt.edu.', 'China|China|China|United States|China|United States', 0, 'Algorithms|DNA|DNA Copy Number Variations|High-Throughput Nucleotide Sequencing|Humans', 'Journal Article', '10.1186/s12859-018-2565-8', '30606105', 'Genome-wide DNA copy number changes are the hallmark events in the initiation and progression of cancers. Quantitative analysis of somatic copy number alterations (CNAs) has broad applications in cancer research. With the increasing capacity of high-throughput sequencing technologies, fast and efficient segmentation algorithms are required when characterizing high density CNAs data.'),
(98750, 2019, 'bmc', 'LimeSeg: a coarse-grained lipid membrane simulation for 3D image segmentation', 'Machado, Sarah|Mercier, Vincent|Chiaruttini, Nicolas', 'Marcos González Gaitán lab, University of Geneva, Department of Biochemistry, quai Ernest-Ansermet 30, Geneva, 1211, Switzerland.|Aurélien Roux lab, University of Geneva, Department of Biochemistry, quai Ernest-Ansermet 30, Geneva, 1211, Switzerland.|Aurélien Roux lab, University of Geneva, Department of Biochemistry, quai Ernest-Ansermet 30, Geneva, 1211, Switzerland. nicolas.chiaruttini@unige.ch.', 'Switzerland|Switzerland|Switzerland', 0, 'Cell Membrane|Humans|Imaging, Three-Dimensional|Lipids', 'Journal Article', '10.1186/s12859-018-2471-0', '30606118', '3D segmentation is often a prerequisite for 3D object display and quantitative measurements. Yet existing voxel-based methods do not directly give information on the object surface or topology. As for spatially continuous approaches such as level-set, active contours and meshes, although providing surfaces and concise shape description, they are generally not suitable for multiple object segmentation and/or for objects with an irregular shape, which can hamper their adoption by bioimage analysts.'),
(98751, 2019, 'bmc', 'SeqClone: sequential Monte Carlo based inference of tumor subclones', 'Ogundijo, Oyetunji E|Wang, Xiaodong', 'Department of Electrical Engineering, Columbia University, New York, NY 10027, USA.|Department of Electrical Engineering, Columbia University, New York, NY 10027, USA. wangx@ee.columbia.edu.', 'United States|United States', 0, 'Algorithms|Clone Cells|Computer Simulation|Databases as Topic|Genotype|Humans|Monte Carlo Method|Neoplasms|Phylogeny|Probability', 'Journal Article', '10.1186/s12859-018-2562-y', '30611189', 'Tumor samples are heterogeneous. They consist of varying cell populations or subclones and each subclone is characterized with a distinct single nucleotide variant (SNV) profile. This explains the source of genetic heterogeneity observed in tumor sequencing data. To make precise prognosis and design effective therapy for cancer, ascertaining the subclonal composition of a tumor is of great importance.'),
(98752, 2019, 'bmc', 'Predicting peptide presentation by major histocompatibility complex class I: an improved machine learning approach to the immunopeptidome', 'Boehm, Kevin Michael|Bhinder, Bhavneet|Raja, Vijay Joseph|Dephoure, Noah|Elemento, Olivier', 'Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD Program, 1300 York Avenue, New York, NY, USA. kmb2012@med.cornell.edu.|Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medical College, 413 East 69th Street, New York, NY, USA.|Department of Biochemistry, Weill Cornell Medical College, 1300 York Avenue, New York, NY, USA.|Department of Biochemistry, Weill Cornell Medical College, 1300 York Avenue, New York, NY, USA.|Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medical College, 413 East 69th Street, New York, NY, USA.', 'United States|Israel|United States|United States|Israel', 0, 'Algorithms|Cell Line, Tumor|Databases, Protein|Gene Expression Regulation|Histocompatibility Antigens Class I|Humans|Machine Learning|Peptides|Proteome|Reproducibility of Results', 'Journal Article', '10.1186/s12859-018-2561-z', '30611210', 'To further our understanding of immunopeptidomics, improved tools are needed to identify peptides presented by major histocompatibility complex class I (MHC-I). Many existing tools are limited by their reliance upon chemical affinity data, which is less biologically relevant than sampling by mass spectrometry, and other tools are limited by incomplete exploration of machine learning approaches. Herein, we assemble publicly available data describing human peptides discovered by sampling the MHC-I immunopeptidome with mass spectrometry and use this database to train random forest classifiers (ForestMHC) to predict presentation by MHC-I.'),
(98753, 2019, 'bmc', 'The computational prediction of drug-disease interactions using the dual-network L2,1-CMF method', 'Cui, Zhen|Gao, Ying-Lian|Liu, Jin-Xing|Wang, Juan|Shang, Junliang|Dai, Ling-Yun', 'School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, China.|Library of Qufu Normal University, Qufu Normal University, Rizhao, China.|School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, China. sdcavell@126.com.|School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, China.|School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, China.|School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, China.', 'China|China|China|China|China|China', 8, 'Algorithms|Area Under Curve|Computational Biology|Databases as Topic|Disease|Drug Interactions|Humans|Reproducibility of Results|Semantics', 'Journal Article', '10.1186/s12859-018-2575-6', '30611214', 'Predicting drug-disease interactions (DDIs) is time-consuming and expensive. Improving the accuracy of prediction results is necessary, and it is crucial to develop a novel computing technology to predict new DDIs. The existing methods mostly use the construction of heterogeneous networks to predict new DDIs. However, the number of known interacting drug-disease pairs is small, so there will be many errors in this heterogeneous network that will interfere with the final results.'),
(98754, 2019, 'bmc', 'OMeta: an ontology-based, data-driven metadata tracking system', 'Singh, Indresh|Kuscuoglu, Mehmet|Harkins, Derek M|Sutton, Granger|Fouts, Derrick E|Nelson, Karen E', 'J. Craig Venter Institute, 9605 Medical Center Drive, Suite 150, Rockville, MD, 20850, USA. isingh@jcvi.org.|J. Craig Venter Institute, 4120 Capricorn Ln, La Jolla, CA, 92037, USA.|J. Craig Venter Institute, 9605 Medical Center Drive, Suite 150, Rockville, MD, 20850, USA.|J. Craig Venter Institute, 9605 Medical Center Drive, Suite 150, Rockville, MD, 20850, USA.|J. Craig Venter Institute, 9605 Medical Center Drive, Suite 150, Rockville, MD, 20850, USA.|J. Craig Venter Institute, 9605 Medical Center Drive, Suite 150, Rockville, MD, 20850, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Biological Ontologies|Databases, Factual|Metadata|Metagenomics|Phylogeny|Software|User-Computer Interface|Whole Genome Sequencing', 'Journal Article', '10.1186/s12859-018-2580-9', '30612540', 'The development of high-throughput sequencing and analysis has accelerated multi-omics studies of thousands of microbial species, metagenomes, and infectious disease pathogens. Omics studies are enabling genotype-phenotype association studies which identify genetic determinants of pathogen virulence and drug resistance, as well as phylogenetic studies designed to track the origin and spread of disease outbreaks. These omics studies are complex and often employ multiple assay technologies including genomics, metagenomics, transcriptomics, proteomics, and metabolomics. To maximize the impact of omics studies, it is essential that data be accompanied by detailed contextual metadata (e.g., specimen, spatial-temporal, phenotypic characteristics) in clear, organized, and consistent formats. Over the years, many metadata standards developed by various metadata standards initiatives have arisen, the Genomic Standards Consortiums minimal information standards (MIxS), the GSCID/BRC Project and Sample Application Standard. Some tools exist for tracking metadata, but they do not provide event based capabilities to configure, collect, validate, and distribute metadata. To address this gap in the scientific community, an event based data-driven application, OMeta, was created that allows users to quickly configure, collect, validate, distribute, and integrate metadata.'),
(98755, 2019, 'bmc', 'GeNeCK: a web server for gene network construction and visualization', 'Zhang, Minzhe|Li, Qiwei|Yu, Donghyeon|Yao, Bo|Guo, Wei|Xie, Yang|Xiao, Guanghua', 'Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, 75390, TX, United States.|Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, 75390, TX, United States.|Department of Statistics, Inha University, Incheon, South Korea.|Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, 75390, TX, United States.|BioHPC team, Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, Texas, United States.|Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, 75390, TX, United States.|Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, 75390, TX, United States. Guanghua.Xiao@UTSouthwestern.edu.', 'United States|United States|-|United States|United States|United States|United States', 0, 'Algorithms|Bayes Theorem|Computational Biology|Computer Graphics|Gene Regulatory Networks|Humans|Internet|Protein Interaction Maps|Software|Transcriptome', 'Journal Article', '10.1186/s12859-018-2560-0', '30616521', 'Reverse engineering approaches to infer gene regulatory networks using computational methods are of great importance to annotate gene functionality and identify hub genes. Although various statistical algorithms have been proposed, development of computational tools to integrate results from different methods and user-friendly online tools is still lagging.'),
(98756, 2019, 'bmc', 'Rapid fine mapping of causative mutations from sets of unordered, contig-sized fragments of genome sequence', 'Rallapalli, Ghanasyam|Corredor-Moreno, Pilar|Chalstrey, Edward|Page, Martin|MacLean, Daniel', 'The Sainsbury Laboratory, Norwich Research Park, Colney Lane, Norwich, NR4 7UH, UK. g.rallapalli@uea.ac.uk.|The Sainsbury Laboratory, Norwich Research Park, Colney Lane, Norwich, NR4 7UH, UK.|The Sainsbury Laboratory, Norwich Research Park, Colney Lane, Norwich, NR4 7UH, UK.|The Sainsbury Laboratory, Norwich Research Park, Colney Lane, Norwich, NR4 7UH, UK.|The Sainsbury Laboratory, Norwich Research Park, Colney Lane, Norwich, NR4 7UH, UK. dan.maclean@tsl.ac.uk.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Arabidopsis|Chromosome Mapping|Chromosomes, Plant|Genes, Plant|Genome, Plant|High-Throughput Nucleotide Sequencing|Homozygote|Hordeum|Mutation|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software|Zea mays', 'Journal Article', '10.1186/s12859-018-2515-5', '30616525', 'Traditional Map based Cloning approaches, used for the identification of desirable alleles, are extremely labour intensive and years can elapse between the mutagenesis and the detection of the polymorphism. High throughput sequencing based Mapping-by-sequencing approach requires an ordered genome assembly and cannot be used with fragmented, un-scaffolded draft genomes, limiting its application to model species and precluding many important organisms.'),
(98757, 2019, 'bmc', 'BO-LSTM: classifying relations via long short-term memory networks along biomedical ontologies', 'Lamurias, Andre|Sousa, Diana|Clarke, Luka A|Couto, Francisco M', 'LASIGE, Faculdade de Ciências, Universidade de Lisboa, Lisboa, 1749 016, Portugal. alamurias@lasige.di.fc.ul.pt.|LASIGE, Faculdade de Ciências, Universidade de Lisboa, Lisboa, 1749 016, Portugal.|University of Lisboa, Faculty of Sciences, BioISI - Biosystems & Integrative Sciences Institute, Campo Grande, C8 bdg, Lisboa, 1749 016, Portugal.|LASIGE, Faculdade de Ciências, Universidade de Lisboa, Lisboa, 1749 016, Portugal.', 'Portugal|Portugal|Portugal|Portugal', 9, 'Biological Ontologies|Data Mining|Databases, Factual|Deep Learning|Drug Interactions|Gene Ontology|Humans|Memory, Short-Term|Natural Language Processing|Neural Networks (Computer)|Software', 'Journal Article', '10.1186/s12859-018-2584-5', '30616557', 'Recent studies have proposed deep learning techniques, namely recurrent neural networks, to improve biomedical text mining tasks. However, these techniques rarely take advantage of existing domain-specific resources, such as ontologies. In Life and Health Sciences there is a vast and valuable set of such resources publicly available, which are continuously being updated. Biomedical ontologies are nowadays a mainstream approach to formalize existing knowledge about entities, such as genes, chemicals, phenotypes, and disorders. These resources contain supplementary information that may not be yet encoded in training data, particularly in domains with limited labeled data.'),
(98758, 2019, 'bmc', 'A statistical measure for the skewness of X chromosome inactivation based on case-control design', 'Wang, Peng|Zhang, Yu|Wang, Bei-Qi|Li, Jian-Long|Wang, Yi-Xin|Pan, Dongdong|Wu, Xian-Bo|Fung, Wing Kam|Zhou, Ji-Yuan', 'State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, No. 1023, South Shatai Road, Baiyun District, Guangzhou, 510515, China.|State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, No. 1023, South Shatai Road, Baiyun District, Guangzhou, 510515, China.|State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, No. 1023, South Shatai Road, Baiyun District, Guangzhou, 510515, China.|State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, No. 1023, South Shatai Road, Baiyun District, Guangzhou, 510515, China.|Department of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.|Yunnan Key Laboratory of Statistical Modeling and Data Analysis, Yunnan University, Kunming, China.|Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China.|Department of Statistics and Actuarial Science, University of Hong Kong, Hong Kong, China.|State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, No. 1023, South Shatai Road, Baiyun District, Guangzhou, 510515, China. zhoujiyuan5460@hotmail.com.', 'China|China|China|China|China|China|China|China|China', 0, 'Alleles|Case-Control Studies|Chromosomes, Human, X|Female|Genes, X-Linked|Heterozygote|Humans|Models, Statistical|X Chromosome Inactivation', 'Journal Article', '10.1186/s12859-018-2587-2', '30616589', 'Skewed X chromosome inactivation (XCI), which is a non-random process, is frequently observed in both healthy and affected females. Furthermore, skewed XCI has been reported to be related to many X-linked diseases. However, no statistical method is available in the literature to measure the degree of the skewness of XCI for case-control design. Therefore, it is necessary to develop methods for such a task.'),
(98759, 2019, 'bmc', 'A model to predict the function of hypothetical proteins through a nine-point classification scoring schema', 'Ijaq, Johny|Malik, Girik|Kumar, Anuj|Das, Partha Sarathi|Meena, Narendra|Bethi, Neeraja|Sundararajan, Vijayaraghava Seshadri|Suravajhala, Prashanth', 'Department of Biotechnology, Osmania University, Hyderabad, 500007, India.|Department of Pediatrics, The Battelle Center for Mathematical Medicine, The Research Institute at Nationwide Childrens Hospital, The Ohio State University, Columbus, OH, USA.|Bioclues.org, Kukatpally, Hyderabad, 500072, India.|Bioclues.org, Kukatpally, Hyderabad, 500072, India.|Department of Biotechnology and Bioinformatics, Birla Institute of Scientific Research, Statue Circle, RJ, 302001, India.|Department of Biotechnology, Osmania University, Hyderabad, 500007, India.|Bioclues.org, Kukatpally, Hyderabad, 500072, India. chanusuba@gmail.com.|Bioclues.org, Kukatpally, Hyderabad, 500072, India. prash@bisr.res.in.', 'India|United States|India|India|India|India|India|India', 0, 'Bayes Theorem|Humans|Proteins', 'Journal Article', '10.1186/s12859-018-2554-y', '30621574', 'Hypothetical proteins [HP] are those that are predicted to be expressed in an organism, but no evidence of their existence is known. In the recent past, annotation and curation efforts have helped overcome the challenge in understanding their diverse functions. Techniques to decipher sequence-structure-function relationship, especially in terms of functional modelling of the HPs have been developed by researchers, but using the features as classifiers for HPs has not been attempted. With the rise in number of annotation strategies, next-generation sequencing methods have provided further understanding the functions of HPs.'),
(98760, 2019, 'bmc', 'Identifying genes with tri-modal association with survival and tumor grade in cancer patients', 'Zhang, Minzhe|Wang, Tao|Sirianni, Rosa|Shaul, Philip W|Xie, Yang', 'Department of Clinical Sciences, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390, USA.|Department of Clinical Sciences, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390, USA.|Department of Pediatrics, Division of Pulmonary and Vascular Biology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390, USA.|Department of Pediatrics, Division of Pulmonary and Vascular Biology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390, USA.|Department of Clinical Sciences, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390, USA. Yang.Xie@UTSouthwestern.edu.', 'United States|United States|United States|United States|United States', 0, 'Breast Neoplasms|Gene Expression Regulation, Neoplastic|Genes, Tumor Suppressor|Genomics|Humans|Neoplasm Grading|Oncogenes|Survival Analysis', 'Journal Article', '10.1186/s12859-018-2582-7', '30621577', 'Previous cancer genomics studies focused on searching for novel oncogenes and tumor suppressor genes whose abundance is positively or negatively correlated with end-point observation, such as survival or tumor grade. This approach may potentially miss some truly functional genes if both its low and high modes have associations with end-point observation. Such genes act as both oncogenes and tumor suppressor genes, a scenario that is unlikely but theoretically possible.'),
(98761, 2019, 'bmc', 'Classical and Bayesian random-effects meta-analysis models with sample quality weights in gene expression studies', 'Siangphoe, Uma|Archer, Kellie J|Mukhopadhyay, Nitai D', 'Office of Biostatistics, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA. siangphoeu@vcu.edu.|Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, Ohio, USA.|Department of Biostatistics, Virginia Commonwealth University, Richmond, Virginia, USA.', 'United States|United States|United States', 1, 'Bayes Theorem|Gene Expression|Gene Expression Profiling|Genome|Humans|Meta-Analysis as Topic|Research Design', 'Journal Article', '10.1186/s12859-018-2491-9', '30626315', 'Random-effects (RE) models are commonly applied to account for heterogeneity in effect sizes in gene expression meta-analysis. The degree of heterogeneity may differ due to inconsistencies in sample quality. High heterogeneity can arise in meta-analyses containing poor quality samples. We applied sample-quality weights to adjust the study heterogeneity in the DerSimonian and Laird (DSL) and two-step DSL (DSLR2) RE models and the Bayesian random-effects (BRE) models with unweighted and weighted data, Gibbs and Metropolis-Hasting (MH) sampling algorithms, weighted common effect, and weighted between-study variance. We evaluated the performance of the models through simulations and illustrated application of the methods using Alzheimers gene expression datasets.'),
(98762, 2019, 'bmc', 'CoExpresso: assess the quantitative behavior of protein complexes in human cells', 'Chalabi, Morteza H|Tsiamis, Vasileios|Käll, Lukas|Vandin, Fabio|Schwämmle, Veit', 'Department of Biochemistry and Molecular Biology and VILLUM Center for Bioanalytical Sciences, University of Southern Denmark, Campusvej 55, Odense M, 5230, Denmark.|Department of Biochemistry and Molecular Biology and VILLUM Center for Bioanalytical Sciences, University of Southern Denmark, Campusvej 55, Odense M, 5230, Denmark.|KTH - Science for Life Laboratory, School of Biotechnology, Royal Institute of Technology, Solna, Sweden.|Department of Information Engineering, University of Padua, Padua, Italy.|Department of Biochemistry and Molecular Biology and VILLUM Center for Bioanalytical Sciences, University of Southern Denmark, Campusvej 55, Odense M, 5230, Denmark. veits@bmb.sdu.dk.', 'Denmark|Denmark|Sweden|Italy|Denmark', 0, 'Algorithms|Humans|Proteins|Proteomics', 'Journal Article', '10.1186/s12859-018-2573-8', '30626316', 'Translational and post-translational control mechanisms in the cell result in widely observable differences between measured gene transcription and protein abundances. Herein, protein complexes are among the most tightly controlled entities by selective degradation of their individual proteins. They furthermore act as control hubs that regulate highly important processes in the cell and exhibit a high functional diversity due to their ability to change their composition and their structure. Better understanding and prediction of these functional states demands methods for the characterization of complex composition, behavior, and abundance across multiple cell states. Mass spectrometry provides an unbiased approach to directly determine protein abundances across different cell populations and thus to profile a comprehensive abundance map of proteins.'),
(98763, 2019, 'bmc', 'ACCBN: ant-Colony-clustering-based bipartite network method for predicting long non-coding RNA-protein interactions', 'Zhu, Rong|Li, Guangshun|Liu, Jin-Xing|Dai, Ling-Yun|Guo, Ying', 'School of Information Science and Engineering, Central South University, Changsha, 410083, China. zhurongsd@126.com.|School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, China.|School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, China.|School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, China.|School of Information Science and Engineering, Central South University, Changsha, 410083, China. yingguo@csu.edu.cn.', 'China|China|China|China|China', 3, 'Algorithms|Computational Biology|Humans|RNA, Long Noncoding', 'Journal Article', '10.1186/s12859-018-2586-3', '30626319', 'Long non-coding RNA (lncRNA) studies play an important role in the development, invasion, and metastasis of the tumor. The analysis and screening of the differential expression of lncRNAs in cancer and corresponding paracancerous tissues provides new clues for finding new cancer diagnostic indicators and improving the treatment. Predicting lncRNA-protein interactions is very important in the analysis of lncRNAs. This article proposes an Ant-Colony-Clustering-Based Bipartite Network (ACCBN) method and predicts lncRNA-protein interactions. The ACCBN method combines ant colony clustering and bipartite network inference to predict lncRNA-protein interactions.'),
(98764, 2019, 'bmc', 'A whitening approach to probabilistic canonical correlation analysis for omics data integration', 'Jendoubi, Takoua|Strimmer, Korbinian', 'Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK. t.jendoubi14@imperial.ac.uk.|School of Mathematics, University of Manchester, Alan Turing Building, Oxford Road, Manchester, M13 9PL, UK.', 'United Kingdom|United Kingdom', 0, 'Algorithms|Databases, Genetic|Humans|Multivariate Analysis|Principal Component Analysis', 'Journal Article', '10.1186/s12859-018-2572-9', '30626338', 'Canonical correlation analysis (CCA) is a classic statistical tool for investigating complex multivariate data. Correspondingly, it has found many diverse applications, ranging from molecular biology and medicine to social science and finance. Intriguingly, despite the importance and pervasiveness of CCA, only recently a probabilistic understanding of CCA is developing, moving from an algorithmic to a model-based perspective and enabling its application to large-scale settings.'),
(98765, 2019, 'bmc', 'CoMetGeNe: mining conserved neighborhood patterns in metabolic and genomic contexts', 'Zaharia, Alexandra|Labedan, Bernard|Froidevaux, Christine|Denise, Alain', 'Laboratoire de Recherche en Informatique (LRI), CNRS, Université Paris-Sud, Université Paris-Saclay, Orsay, 91405, France.|Laboratoire de Recherche en Informatique (LRI), CNRS, Université Paris-Sud, Université Paris-Saclay, Orsay, 91405, France.|Laboratoire de Recherche en Informatique (LRI), CNRS, Université Paris-Sud, Université Paris-Saclay, Orsay, 91405, France.|Laboratoire de Recherche en Informatique (LRI), CNRS, Université Paris-Sud, Université Paris-Saclay, Orsay, 91405, France. alain.denise@u-psud.fr.', 'France|France|France|France', 0, 'Bacteria|Bacterial Proteins|Computational Biology|Data Mining|Gene Order|Genome, Bacterial|Genomics|Metabolic Networks and Pathways|Software|Species Specificity|Systems Biology', 'Journal Article', '10.1186/s12859-018-2542-2', '30630411', 'In systems biology, there is an acute need for integrative approaches in heterogeneous network mining in order to exploit the continuous flux of genomic data. Simultaneous analysis of the metabolic pathways and genomic context of a given species leads to the identification of patterns consisting in reaction chains catalyzed by products of neighboring genes. Similar such patterns across several species can reveal their mode of conservation throughout the tree of life.'),
(98766, 2019, 'bmc', 'MS-Helios: a Circos wrapper to visualize multi-omic datasets', 'Marx, Harald|Coon, Joshua J', 'Department of Microbiology and Ecosystems Science, University of Vienna, 1090, Vienna, Austria. harald.marx@wisc.edu.|Department of Chemistry, University of Wisconsin-Madison, Madison, WI, 53706, USA. jcoon@chem.wisc.edu.', 'Austria|United States', 0, 'Animals|Computer Graphics|Databases, Factual|Datasets as Topic|Mass Spectrometry|Programming Languages|Proteome|Software|Swine|Transcriptome', 'Journal Article', '10.1186/s12859-018-2564-9', '30634897', 'Advances in high-resolution mass spectrometry facilitate the identification of hundreds of metabolites, thousands of proteins and their post-translational modifications. This remarkable progress poses a challenge to data analysis and visualization, requiring methods to reduce dimensionality and represent the data in a compact way. To provide a more holistic view, we recently introduced circular proteome maps (CPMs). However, the CPM construction requires prior data transformation and extensive knowledge of the Perl-based tool, Circos.'),
(98767, 2019, 'bmc', 'Manhattan Harvester and Cropper: a system for GWAS peak detection', 'Haller, Toomas|Tasa, Tõnis|Metspalu, Andres', 'Estonian Genome Center, Institute of Genomics, University of Tartu, 23b Riia Street, 51010, Tartu, Estonia. Toomas.Haller@ut.ee.|Institute of Computer Science, University of Tartu, Juhan Liivi 2, 50409, Tartu, Estonia.|Estonian Genome Center, Institute of Genomics, University of Tartu, 23b Riia Street, 51010, Tartu, Estonia.', 'Estonia|Estonia|Estonia', 0, 'Computer Graphics|Data Interpretation, Statistical|Data Mining|Genome-Wide Association Study|Genomics|Humans|Phenotype|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1186/s12859-019-2600-4', '30634901', 'Selection of interesting regions from genome wide association studies (GWAS) is typically performed by eyeballing of Manhattan Plots. This is no longer possible with thousands of different phenotypes. There is a need for tools that can automatically detect genomic regions that correspond to what the experienced researcher perceives as peaks worthwhile of further study.'),
(98768, 2019, 'bmc', 'GOTrapper: a tool to navigate through branches of gene ontology hierarchy', 'Hassan, Hezha|Shanak, Siba', 'Public Health Laboratory, Sulaimaniyah, Kurdistan Region, 46001, Iraq. hezha.hassan@hezhalab.com.|Faculty of Sciences, Arab American University-Palestine, P.O Box 240, Jenin, Palestine.', 'Iraq|-', 0, 'Computational Biology|Gene Expression Profiling|Gene Ontology|High-Throughput Nucleotide Sequencing|Humans|Information Storage and Retrieval|Software|User-Computer Interface', 'Journal Article', '10.1186/s12859-018-2581-8', '30634902', 'Gene Ontology (GO) is a useful resource of controlled vocabulary that provides information about annotated genes. Based on such resource, finding the biological function is useful for biologists to come up with different hypotheses and help further investigations of an experiment. The biological function for desired genes and gene associations is picked up from a randomly chosen list or through the analysis of differential gene expression. Many tools have been developed to utilize GO knowledge and cluster genes according to relevant biological functions. The retrieved GO terms include both specific and non-specific terms, which is not user-friendly in terms of data analysis. Thus one approach is still missing, which allows navigating through different levels of GO hierarchy manually.');
