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    8453 results match your criteria BMC Bioinformatics [Journal]

    1 OF 170

    Taxonomy-aware feature engineering for microbiome classification.
    BMC Bioinformatics 2018 Jun 15;19(1):227. Epub 2018 Jun 15.
    Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.
    Background: What is a healthy microbiome? The pursuit of this and many related questions, especially in light of the recently recognized microbial component in a wide range of diseases has sparked a surge in metagenomic studies. They are often not simply attributable to a single pathogen but rather are the result of complex ecological processes. Relatedly, the increasing DNA sequencing depth and number of samples in metagenomic case-control studies enabled the applicability of powerful statistical methods, e. Read More

    A short note on dynamic programming in a band.
    BMC Bioinformatics 2018 Jun 15;19(1):226. Epub 2018 Jun 15.
    MaIAGE, INRA, Université Paris-Saclay, Jouy-en-Josas, 78350, France.
    Background: Third generation sequencing technologies generate long reads that exhibit high error rates, in particular for insertions and deletions which are usually the most difficult errors to cope with. The only exact algorithm capable of aligning sequences with insertions and deletions is a dynamic programming algorithm.

    Results: In this note, for the sake of efficiency, we consider dynamic programming in a band. Read More

    Mutational Signatures in Cancer (MuSiCa): a web application to implement mutational signatures analysis in cancer samples.
    BMC Bioinformatics 2018 Jun 14;19(1):224. Epub 2018 Jun 14.
    Gastroenterology Department, Hospital Clínic, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD, University of Barcelona, Barcelona, Spain.
    Background: Mutational signatures have been proved as a valuable pattern in somatic genomics, mainly regarding cancer, with a potential application as a biomarker in clinical practice. Up to now, several bioinformatic packages to address this topic have been developed in different languages/platforms. MutationalPatterns has arisen as the most efficient tool for the comparison with the signatures currently reported in the Catalogue of Somatic Mutations in Cancer (COSMIC) database. Read More

    Identification of drug-target interaction by a random walk with restart method on an interactome network.
    BMC Bioinformatics 2018 Jun 13;19(Suppl 8):208. Epub 2018 Jun 13.
    School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Buk-gu, Gwangju, 500-712, Republic of Korea.
    Background: Identification of drug-target interactions acts as a key role in drug discovery. However, identifying drug-target interactions via in-vitro, in-vivo experiments are very laborious, time-consuming. Thus, predicting drug-target interactions by using computational approaches is a good alternative. Read More

    Relation extraction for biological pathway construction using node2vec.
    BMC Bioinformatics 2018 Jun 13;19(Suppl 8):206. Epub 2018 Jun 13.
    Department of Library and Information Science, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul, Republic of Korea.
    Background: Systems biology is an important field for understanding whole biological mechanisms composed of interactions between biological components. One approach for understanding complex and diverse mechanisms is to analyze biological pathways. However, because these pathways consist of important interactions and information on these interactions is disseminated in a large number of biomedical reports, text-mining techniques are essential for extracting these relationships automatically. Read More

    In silico prediction of potential chemical reactions mediated by human enzymes.
    BMC Bioinformatics 2018 Jun 13;19(Suppl 8):207. Epub 2018 Jun 13.
    School of Integrative Engineering, Chung-Ang University, Seoul, Republic of Korea.
    Background: Administered drugs are often converted into an ineffective or activated form by enzymes in our body. Conventional in silico prediction approaches focused on therapeutically important enzymes such as CYP450. However, there are more than thousands of different cellular enzymes that potentially convert administered drug into other forms. Read More

    Identifying tweets of personal health experience through word embedding and LSTM neural network.
    BMC Bioinformatics 2018 Jun 13;19(Suppl 8):210. Epub 2018 Jun 13.
    Department of Medicine, Vanderbilt University, Nashville, TN, USA.
    Background: As Twitter has become an active data source for health surveillance research, it is important that efficient and effective methods are developed to identify tweets related to personal health experience. Conventional classification algorithms rely on features engineered by human domain experts, and engineering such features is a challenging task and requires much human intelligence. The resultant features may not be optimal for the classification problem, and can make it challenging for conventional classifiers to correctly predict personal experience tweets (PETs) due to the various ways to express and/or describe personal experience in tweets. Read More

    A systematic approach to identify therapeutic effects of natural products based on human metabolite information.
    BMC Bioinformatics 2018 Jun 13;19(Suppl 8):205. Epub 2018 Jun 13.
    Bio-Synergy Research Center, Daejeon, 34141, South Korea.
    Background: Natural products have been widely investigated in the drug development field. Their traditional use cases as medicinal agents and their resemblance of our endogenous compounds show the possibility of new drug development. Many researchers have focused on identifying therapeutic effects of natural products, yet the resemblance of natural products and human metabolites has been rarely touched. Read More

    Semi-Supervised Recurrent Neural Network for Adverse Drug Reaction mention extraction.
    BMC Bioinformatics 2018 Jun 13;19(Suppl 8):212. Epub 2018 Jun 13.
    Information Retrieval and Extraction Laboratory, Kohli Center for Intelligent Systems, International Institute of Information Technology, Hyderabad, India.
    Background: Social media is a useful platform to share health-related information due to its vast reach. This makes it a good candidate for public-health monitoring tasks, specifically for pharmacovigilance. We study the problem of extraction of Adverse-Drug-Reaction (ADR) mentions from social media, particularly from Twitter. Read More

    Identification of common coexpression modules based on quantitative network comparison.
    BMC Bioinformatics 2018 Jun 13;19(Suppl 8):213. Epub 2018 Jun 13.
    Bio-Synergy Research Center, Daejeon, 34141, South Korea.
    Background: Finding common molecular interactions from different samples is essential work to understanding diseases and other biological processes. Coexpression networks and their modules directly reflect sample-specific interactions among genes. Therefore, identification of common coexpression network or modules may reveal the molecular mechanism of complex disease or the relationship between biological processes. Read More

    Evaluation of pooling operations in convolutional architectures for drug-drug interaction extraction.
    BMC Bioinformatics 2018 Jun 13;19(Suppl 8):209. Epub 2018 Jun 13.
    Computer Science Department, Carlos III University of Madrid, 28911, Leganés, Spain.
    Background: Deep Neural Networks (DNN), in particular, Convolutional Neural Networks (CNN), has recently achieved state-of-art results for the task of Drug-Drug Interaction (DDI) extraction. Most CNN architectures incorporate a pooling layer to reduce the dimensionality of the convolution layer output, preserving relevant features and removing irrelevant details. All the previous CNN based systems for DDI extraction used max-pooling layers. Read More

    Blazing Signature Filter: a library for fast pairwise similarity comparisons.
    BMC Bioinformatics 2018 Jun 11;19(1):221. Epub 2018 Jun 11.
    Integrative Omics, Pacific Northwest National Laboratory, Richland, 99352, WA, USA.
    Background: Identifying similarities between datasets is a fundamental task in data mining and has become an integral part of modern scientific investigation. Whether the task is to identify co-expressed genes in large-scale expression surveys or to predict combinations of gene knockouts which would elicit a similar phenotype, the underlying computational task is often a multi-dimensional similarity test. As datasets continue to grow, improvements to the efficiency, sensitivity or specificity of such computation will have broad impacts as it allows scientists to more completely explore the wealth of scientific data. Read More

    DiNAMO: highly sensitive DNA motif discovery in high-throughput sequencing data.
    BMC Bioinformatics 2018 Jun 11;19(1):223. Epub 2018 Jun 11.
    Univ. Lille. Plateau de génomique fonctionnelle et structurale, Lille, F-59000, France.
    Background: Discovering over-represented approximate motifs in DNA sequences is an essential part of bioinformatics. This topic has been studied extensively because of the increasing number of potential applications. However, it remains a difficult challenge, especially with the huge quantity of data generated by high throughput sequencing technologies. Read More

    Variant site strain typer (VaST): efficient strain typing using a minimal number of variant genomic sites.
    BMC Bioinformatics 2018 Jun 11;19(1):222. Epub 2018 Jun 11.
    The School of Informatics, Computing, and Cyber Systems, Northern Arizona University, 1295 S Knoles Dr., Flagstaff, Arizona, 86001, USA.
    Background: Targeted PCR amplicon sequencing (TAS) techniques provide a sensitive, scalable, and cost-effective way to query and identify closely related bacterial species and strains. Typically, this is accomplished by targeting housekeeping genes that provide resolution down to the family, genera, and sometimes species level. Unfortunately, this level of resolution is not sufficient in many applications where strain-level identification of bacteria is required (biodefense, forensics, clinical diagnostics, and outbreak investigations). Read More

    MERIT reveals the impact of genomic context on sequencing error rate in ultra-deep applications.
    BMC Bioinformatics 2018 Jun 8;19(1):219. Epub 2018 Jun 8.
    Center for Systems and Computational Biology, Rutgers Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ, USA.
    Background: Rapid progress in high-throughput sequencing (HTS) and the development of novel library preparation methods have improved the sensitivity of detecting mutations in heterogeneous samples, specifically in high-depth (> 500×) clinical applications. However, HTS methods are bounded by their technical and theoretical limitations and sequencing errors cannot be completely eliminated. Comprehensive quantification of the background noise can highlight both the efficiency and the limitations of any HTS methodology, and help differentiate true mutations at low abundance from artifacts. Read More

    DrImpute: imputing dropout events in single cell RNA sequencing data.
    BMC Bioinformatics 2018 Jun 8;19(1):220. Epub 2018 Jun 8.
    Lillehei Heart Institute, University of Minnesota, 2231 6th St S.E, 4-165 CCRB, Minneapolis, MN, 55114, USA.
    Background: The single cell RNA sequencing (scRNA-seq) technique begin a new era by allowing the observation of gene expression at the single cell level. However, there is also a large amount of technical and biological noise. Because of the low number of RNA transcriptomes and the stochastic nature of the gene expression pattern, there is a high chance of missing nonzero entries as zero, which are called dropout events. Read More

    Discovering mutated driver genes through a robust and sparse co-regularized matrix factorization framework with prior information from mRNA expression patterns and interaction network.
    BMC Bioinformatics 2018 Jun 5;19(1):214. Epub 2018 Jun 5.
    School of Information Science and Technology, University of Science and Technology of China, Huangshan Road, Hefei, 230027, China.
    Background: Discovery of mutated driver genes is one of the primary objective for studying tumorigenesis. To discover some relatively low frequently mutated driver genes from somatic mutation data, many existing methods incorporate interaction network as prior information. However, the prior information of mRNA expression patterns are not exploited by these existing network-based methods, which is also proven to be highly informative of cancer progressions. Read More

    MGOGP: a gene module-based heuristic algorithm for cancer-related gene prioritization.
    BMC Bioinformatics 2018 Jun 5;19(1):215. Epub 2018 Jun 5.
    The First Clinical Hospital of Jilin University, Changchun, 130021, China.
    Background: Prioritizing genes according to their associations with a cancer allows researchers to explore genes in more informed ways. By far, Gene-centric or network-centric gene prioritization methods are predominated. Genes and their protein products carry out cellular processes in the context of functional modules. Read More

    mySyntenyPortal: an application package to construct websites for synteny block analysis.
    BMC Bioinformatics 2018 Jun 5;19(1):216. Epub 2018 Jun 5.
    Department of Biomedical Science and Engineering, Konkuk University, Seoul, 05029, South Korea.
    Background: Advances in sequencing technologies have facilitated large-scale comparative genomics based on whole genome sequencing. Constructing and investigating conserved genomic regions among multiple species (called synteny blocks) are essential in the comparative genomics. However, they require significant amounts of computational resources and time in addition to bioinformatics skills. Read More

    Sequence-based prediction of physicochemical interactions at protein functional sites using a function-and-interaction-annotated domain profile database.
    BMC Bioinformatics 2018 Jun 1;19(1):204. Epub 2018 Jun 1.
    College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Biotech Building Room B1-404, 30 South Puzhu Road, Jiangsu, 211816, Nanjing, People's Republic of China.
    Background: Identifying protein functional sites (PFSs) and, particularly, the physicochemical interactions at these sites is critical to understanding protein functions and the biochemical reactions involved. Several knowledge-based methods have been developed for the prediction of PFSs; however, accurate methods for predicting the physicochemical interactions associated with PFSs are still lacking.

    Results: In this paper, we present a sequence-based method for the prediction of physicochemical interactions at PFSs. Read More

    Genome-wide prediction of cis-regulatory regions using supervised deep learning methods.
    BMC Bioinformatics 2018 May 31;19(1):202. Epub 2018 May 31.
    Centre for Molecular Medicine and Therapeutics, BC Children's Hospital Research Institute, Department of Medical Genetics, University of British Columbia, Rm 3109, 950 West 28th Avenue, Vancouver, V5Z 4H4, Canada.
    Background: In the human genome, 98% of DNA sequences are non-protein-coding regions that were previously disregarded as junk DNA. In fact, non-coding regions host a variety of cis-regulatory regions which precisely control the expression of genes. Thus, Identifying active cis-regulatory regions in the human genome is critical for understanding gene regulation and assessing the impact of genetic variation on phenotype. Read More

    Core Hunter 3: flexible core subset selection.
    BMC Bioinformatics 2018 May 31;19(1):203. Epub 2018 May 31.
    Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 S9, Gent, 9000, Belgium.
    Background: Core collections provide genebank curators and plant breeders a way to reduce size of their collections and populations, while minimizing impact on genetic diversity and allele frequency. Many methods have been proposed to generate core collections, often using distance metrics to quantify the similarity of two accessions, based on genetic marker data or phenotypic traits. Core Hunter is a multi-purpose core subset selection tool that uses local search algorithms to generate subsets relying on one or more metrics, including several distance metrics and allelic richness. Read More

    Rigorous optimisation of multilinear discriminant analysis with Tucker and PARAFAC structures.
    BMC Bioinformatics 2018 May 30;19(1):197. Epub 2018 May 30.
    Department of Applied Mathematics and Computer Science, Technical University of Denmark, Building 324, Kongens Lyngby, 2800, Denmark.
    Background: We propose rigorously optimised supervised feature extraction methods for multilinear data based on Multilinear Discriminant Analysis (MDA) and demonstrate their usage on Electroencephalography (EEG) and simulated data. While existing MDA methods use heuristic optimisation procedures based on an ambiguous Tucker structure, we propose a rigorous approach via optimisation on the cross-product of Stiefel manifolds. We also introduce MDA methods with the PARAFAC structure. Read More

    Improved accuracy assessment for 3D genome reconstructions.
    BMC Bioinformatics 2018 May 30;19(1):196. Epub 2018 May 30.
    Division of Bioinformatics, Department of Epidemiology and Biostatistics, UCSF, 16th Street, San Francisco, 94158, USA.
    Background: Three dimensional (3D) genome spatial organization is critical for numerous cellular functions, including transcription, while certain conformation-driven structural alterations are frequently oncogenic. Genome conformation had been difficult to elucidate but the advent chromatin conformation capture assays, notably Hi-C, has transformed understanding of chromatin architecture and yielded numerous biological insights. Although most of these findings have flowed from analysis of proximity data produced by these assays, added value in generating 3D reconstructions has been demonstrated, deriving, in part, from superposing genomic features on the reconstruction. Read More

    EqualTDRL: illustrating equivalent tandem duplication random loss rearrangements.
    BMC Bioinformatics 2018 May 30;19(1):192. Epub 2018 May 30.
    Swarm Intelligence and Complex Systems Group, Faculty of Mathematics and Computer Science, Leipzig University, Augustusplatz 10, Leipzig, D-04109, Germany.
    Background: To study the differences between two unichromosomal circular genomes, e.g., mitochondrial genomes, under the tandem duplication random loss (TDRL) rearrangement it is important to consider the whole set of potential TDRL rearrangement events that could have taken place. Read More

    Combining RNA-seq data and homology-based gene prediction for plants, animals and fungi.
    BMC Bioinformatics 2018 May 30;19(1):189. Epub 2018 May 30.
    Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle (Saale), D-06120, Germany.
    Background: Genome annotation is of key importance in many research questions. The identification of protein-coding genes is often based on transcriptome sequencing data, ab-initio or homology-based prediction. Recently, it was demonstrated that intron position conservation improves homology-based gene prediction, and that experimental data improves ab-initio gene prediction. Read More

    JCDSA: a joint covariate detection tool for survival analysis on tumor expression profiles.
    BMC Bioinformatics 2018 May 29;19(1):187. Epub 2018 May 29.
    College of Information and Computer Engineering, Northeast Forestry University, No.26 Hexing Road, Harbin, 150001, China.
    Background: Survival analysis on tumor expression profiles has always been a key issue for subsequent biological experimental validation. It is crucial how to select features which closely correspond to survival time. Furthermore, it is important how to select features which best discriminate between low-risk and high-risk group of patients. Read More

    SemaTyP: a knowledge graph based literature mining method for drug discovery.
    BMC Bioinformatics 2018 May 30;19(1):193. Epub 2018 May 30.
    College of Computer Science and Technology, Dalian University of Technology, Hongling Road, Dalian, 116023, China.
    Background: Drug discovery is the process through which potential new medicines are identified. High-throughput screening and computer-aided drug discovery/design are the two main drug discovery methods for now, which have successfully discovered a series of drugs. However, development of new drugs is still an extremely time-consuming and expensive process. Read More

    RnaSeqSampleSize: real data based sample size estimation for RNA sequencing.
    BMC Bioinformatics 2018 May 30;19(1):191. Epub 2018 May 30.
    Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
    Background: One of the most important and often neglected components of a successful RNA sequencing (RNA-Seq) experiment is sample size estimation. A few negative binomial model-based methods have been developed to estimate sample size based on the parameters of a single gene. However, thousands of genes are quantified and tested for differential expression simultaneously in RNA-Seq experiments. Read More

    Recommending plant taxa for supporting on-site species identification.
    BMC Bioinformatics 2018 May 30;19(1):190. Epub 2018 May 30.
    Institute for Computer and Systems Engineering, Technische Universität Ilmenau, Helmholtzplatz 5, Ilmenau, 98693, Germany.
    Background: Predicting a list of plant taxa most likely to be observed at a given geographical location and time is useful for many scenarios in biodiversity informatics. Since efficient plant species identification is impeded mainly by the large number of possible candidate species, providing a shortlist of likely candidates can help significantly expedite the task. Whereas species distribution models heavily rely on geo-referenced occurrence data, such information still remains largely unused for plant taxa identification tools. Read More

    QTLTableMiner: semantic mining of QTL tables in scientific articles.
    BMC Bioinformatics 2018 May 25;19(1):183. Epub 2018 May 25.
    Plant Breeding, Wageningen University and Research, Wageningen, The Netherlands.
    Background: A quantitative trait locus (QTL) is a genomic region that correlates with a phenotype. Most of the experimental information about QTL mapping studies is described in tables of scientific publications. Traditional text mining techniques aim to extract information from unstructured text rather than from tables. Read More

    polyClustR: defining communities of reconciled cancer subtypes with biological and prognostic significance.
    BMC Bioinformatics 2018 May 25;19(1):182. Epub 2018 May 25.
    Division of Molecular Pathology, The Institute of Cancer Research (ICR), London, UK.
    Background: To ensure cancer patients are stratified towards treatments that are optimally beneficial, it is a priority to define robust molecular subtypes using clustering methods applied to high-dimensional biological data. If each of these methods produces different numbers of clusters for the same data, it is difficult to achieve an optimal solution. Here, we introduce "polyClustR", a tool that reconciles clusters identified by different methods into subtype "communities" using a hypergeometric test or a measure of relative proportion of common samples. Read More

    Correction to: Identification and characterization of conserved lncRNAs in human and rat brain.
    BMC Bioinformatics 2018 May 24;19(1):181. Epub 2018 May 24.
    MidSouth Bioinformatics Center and Joint Bioinformatics Ph.D. Program, University of Arkansas at Little Rock and University of Arkansas for Medical Sciences, 2801 S. University Avenue, Little Rock, AR, 72204, USA.
    After publication of the original article [1], it was noticed that the Acknowledgement statement was incorrect. The original statement reads. Read More

    A website to identify shared genes in Saccharomyces cerevisiae homozygous deletion library screens.
    BMC Bioinformatics 2018 May 23;19(1):179. Epub 2018 May 23.
    School of Science and Health, Western Sydney University, Campbelltown Campus, Locked Bag 1797, Penrith South DC, NSW, 1797, Australia.
    Background: The homozygous yeast deletion library includes approximately 4800 diploid strains each containing one deleted non-essential gene. Hundreds of publications have arisen through experimentation using this genome-wide biological resource. As part of this work over 677 genesets have been collated from these experiments representing the phenotypic responses of the library to a diverse set of chemical and physical challenges. Read More

    NextSV: a meta-caller for structural variants from low-coverage long-read sequencing data.
    BMC Bioinformatics 2018 May 23;19(1):180. Epub 2018 May 23.
    Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
    Background: Structural variants (SVs) in human genomes are implicated in a variety of human diseases. Long-read sequencing delivers much longer read lengths than short-read sequencing and may greatly improve SV detection. However, due to the relatively high cost of long-read sequencing, it is unclear what coverage is needed and how to optimally use the aligners and SV callers. Read More

    NPBSS: a new PacBio sequencing simulator for generating the continuous long reads with an empirical model.
    BMC Bioinformatics 2018 May 22;19(1):177. Epub 2018 May 22.
    Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China.
    Background: PacBio sequencing platform offers longer read lengths than the second-generation sequencing technologies. It has revolutionized de novo genome assembly and enabled the automated reconstruction of reference-quality genomes. Due to its extremely wide range of application areas, fast sequencing simulation systems with high fidelity are in great demand to facilitate the development and comparison of subsequent analysis tools. Read More

    SAMSA2: a standalone metatranscriptome analysis pipeline.
    BMC Bioinformatics 2018 May 21;19(1):175. Epub 2018 May 21.
    Genome Center, University of California, Davis, California, USA.
    Background: Complex microbial communities are an area of growing interest in biology. Metatranscriptomics allows researchers to quantify microbial gene expression in an environmental sample via high-throughput sequencing. Metatranscriptomic experiments are computationally intensive because the experiments generate a large volume of sequence data and each sequence must be compared with reference sequences from thousands of organisms. Read More

    Neural networks for link prediction in realistic biomedical graphs: a multi-dimensional evaluation of graph embedding-based approaches.
    BMC Bioinformatics 2018 May 21;19(1):176. Epub 2018 May 21.
    Language Technology Laboratory, TAL, University of Cambridge, 9 West Road, Cambridge, CB39DB, UK.
    Background: Link prediction in biomedical graphs has several important applications including predicting Drug-Target Interactions (DTI), Protein-Protein Interaction (PPI) prediction and Literature-Based Discovery (LBD). It can be done using a classifier to output the probability of link formation between nodes. Recently several works have used neural networks to create node representations which allow rich inputs to neural classifiers. Read More

    Robust joint score tests in the application of DNA methylation data analysis.
    BMC Bioinformatics 2018 May 18;19(1):174. Epub 2018 May 18.
    Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, 181 Longwood Avenue, Boston, 02115, USA.
    Background: Recently differential variability has been showed to be valuable in evaluating the association of DNA methylation to the risks of complex human diseases. The statistical tests based on both differential methylation level and differential variability can be more powerful than those based only on differential methylation level. Anh and Wang (2013) proposed a joint score test (AW) to simultaneously detect for differential methylation and differential variability. Read More

    Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction.
    BMC Bioinformatics 2018 May 16;19(1):173. Epub 2018 May 16.
    Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, M5S 1A8, Canada.
    Background: There is growing interest in utilizing artificial intelligence, and particularly deep learning, for computer vision in histopathology. While accumulating studies highlight expert-level performance of convolutional neural networks (CNNs) on focused classification tasks, most studies rely on probability distribution scores with empirically defined cutoff values based on post-hoc analysis. More generalizable tools that allow humans to visualize histology-based deep learning inferences and decision making are scarce. Read More

    Live neighbor-joining.
    BMC Bioinformatics 2018 May 16;19(1):172. Epub 2018 May 16.
    Faculdade de Computação, Universidade Federal de Mato Grosso do Sul, Av. Costa e Silva, s/n, Campo Grande, 79070-900, Brazil.
    Background: In phylogenetic reconstruction the result is a tree where all taxa are leaves and internal nodes are hypothetical ancestors. In a live phylogeny, both ancestral and living taxa may coexist, leading to a tree where internal nodes may be living taxa. The well-known Neighbor-Joining heuristic is largely used for phylogenetic reconstruction. Read More

    GPU-accelerated iterative reconstruction for limited-data tomography in CBCT systems.
    BMC Bioinformatics 2018 May 15;19(1):171. Epub 2018 May 15.
    Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain.
    Background: Standard cone-beam computed tomography (CBCT) involves the acquisition of at least 360 projections rotating through 360 degrees. Nevertheless, there are cases in which only a few projections can be taken in a limited angular span, such as during surgery, where rotation of the source-detector pair is limited to less than 180 degrees. Reconstruction of limited data with the conventional method proposed by Feldkamp, Davis and Kress (FDK) results in severe artifacts. Read More

    Correction to: HiComet: a high-throughput comet analysis tool for large-scale DNA damage assessment.
    BMC Bioinformatics 2018 May 11;19(1):170. Epub 2018 May 11.
    Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Korea.
    After publication of the original article [1], it has been found that the author affiliations have been accidentally left out in the PDF. The full affiliations can be found in this correction. Read More

    ASTRAL-III: polynomial time species tree reconstruction from partially resolved gene trees.
    BMC Bioinformatics 2018 May 8;19(Suppl 6):153. Epub 2018 May 8.
    Department of Electrical and Computer Engineering, University of California at San Diego, 9500 Gilman Drive, La Jolla, 92093-0021, CA, USA.
    Background: Evolutionary histories can be discordant across the genome, and such discordances need to be considered in reconstructing the species phylogeny. ASTRAL is one of the leading methods for inferring species trees from gene trees while accounting for gene tree discordance. ASTRAL uses dynamic programming to search for the tree that shares the maximum number of quartet topologies with input gene trees, restricting itself to a predefined set of bipartitions. Read More

    On the rank-distance median of 3 permutations.
    BMC Bioinformatics 2018 May 8;19(Suppl 6):142. Epub 2018 May 8.
    University of Campinas, Campinas, Brazil.
    Background: Recently, Pereira Zanetti, Biller and Meidanis have proposed a new definition of a rearrangement distance between genomes. In this formulation, each genome is represented as a matrix, and the distance d is the rank distance between these matrices. Although defined in terms of matrices, the rank distance is equal to the minimum total weight of a series of weighted operations that leads from one genome to the other, including inversions, translocations, transpositions, and others. Read More

    Generation and comparative genomics of synthetic dengue viruses.
    BMC Bioinformatics 2018 May 8;19(Suppl 6):140. Epub 2018 May 8.
    Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel.
    Background: Synthetic virology is an important multidisciplinary scientific field, with emerging applications in biotechnology and medicine, aiming at developing methods to generate and engineer synthetic viruses. In particular, many of the RNA viruses, including among others the Dengue and Zika, are widespread pathogens of significant importance to human health. The ability to design and synthesize such viruses may contribute to exploring novel approaches for developing vaccines and virus based therapies. Read More

    Computing the family-free DCJ similarity.
    BMC Bioinformatics 2018 May 8;19(Suppl 6):152. Epub 2018 May 8.
    Faculdade de Computação, Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, Brazil.
    Background: The genomic similarity is a large-scale measure for comparing two given genomes. In this work we study the (NP-hard) problem of computing the genomic similarity under the DCJ model in a setting that does not assume that the genes of the compared genomes are grouped into gene families. This problem is called family-free DCJ similarity. Read More

    A new method to measure the semantic similarity from query phenotypic abnormalities to diseases based on the human phenotype ontology.
    BMC Bioinformatics 2018 May 8;19(Suppl 4):162. Epub 2018 May 8.
    Department of Bioinformatics and Biostatistics, SJTU-Yale Joint Center for Biostatistics, Shanghai Jiao Tong University, Shanghai, China.
    Background: Although rapid developed sequencing technologies make it possible for genotype data to be used in clinical diagnosis, it is still challenging for clinicians to understand the results of sequencing and make correct judgement based on them. Before this, diagnosis based on clinical features held a leading position. With the establishment of the Human Phenotype Ontology (HPO) and the enrichment of phenotype-disease annotations, there throws much more attention to the improvement of phenotype-based diagnosis. Read More

    Pathway-based approach using hierarchical components of rare variants to analyze multiple phenotypes.
    BMC Bioinformatics 2018 May 8;19(Suppl 4):79. Epub 2018 May 8.
    Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea.
    Background: As one possible solution to the "missing heritability" problem, many methods have been proposed that apply pathway-based analyses, using rare variants that are detected by next generation sequencing technology. However, while a number of methods for pathway-based rare-variant analysis of multiple phenotypes have been proposed, no method considers a unified model that incorporate multiple pathways.

    Results: Simulation studies successfully demonstrated advantages of multivariate analysis, compared to univariate analysis, and comparison studies showed the proposed approach to outperform existing methods. Read More

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