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Bioinformatics

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https://www.readbyqxmd.com/read/28379466/mackinac-a-bridge-between-modelseed-and-cobrapy-to-generate-and-analyze-genome-scale-metabolic-models
#1
Michael Mundy, Helena Mendes-Soares, Nicholas Chia
Summary: Reconstructing and analyzing a large number of genome-scale metabolic models is a fundamental part of the integrated study of microbial communities; however, two of the most widely used frameworks for building and analyzing models use different metabolic network representations. Here we describe Mackinac, a Python package that combines ModelSEED's ability to automatically reconstruct metabolic models with COBRApy's advanced analysis capabilities to bridge the differences between the two frameworks and facilitate the study of the metabolic potential of microorganisms...
August 30, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28444139/multi-label-classifier-based-on-histogram-of-gradients-for-predicting-the-anatomical-therapeutic-chemical-class-classes-of-a-given-compound
#2
Loris Nanni, Sheryl Brahnam
Motivation: Given an unknown compound, is it possible to predict its ATC (Anatomical Therapeutic Chemical) class/classes? This is a challenging yet important problem since such a prediction could be used to deduce not only a compound's possible active ingredients but also its therapeutic, pharmacological, and chemical properties, thereby substantially expediting the pace of drug development. The problem is challenging because some drugs and compounds belong to two or more ATC classes, making machine learning extremely difficult...
April 24, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28444127/hla-class-i-binding-prediction-via-convolutional-neural-networks
#3
Yeeleng S Vang, Xiaohui Xie
Motivation: Many biological processes are governed by protein-ligand interactions. One such example is the recognition of self and nonself cells by the immune system. This immune response process is regulated by the major histocompatibility complex (MHC) protein which is encoded by the human leukocyte antigen (HLA) complex. Understanding the binding potential between MHC and peptides can lead to the design of more potent, peptide-based vaccines and immunotherapies for infectious autoimmune diseases...
April 21, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28444126/cimbinator-a-web-based-tool-for-drug-synergy-analysis-in-small-and-large-scale-datasets
#4
Åsmund Flobak, Miguel Vazquez, Astrid Lægreid, Alfonso Valencia
Motivation: Drug synergies are sought to identify combinations of drugs particularly beneficial. User-friendly software solutions that can assist analysis of large-scale datasets are required. Results: CImbinator is a web-service that can aid in batch-wise and in-depth analyzes of data from small-scale and large-scale drug combination screens. CImbinator offers to quantify drug combination effects, using both the commonly employed median effect equation, as well as advanced experimental mathematical models describing dose response relationships...
April 19, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28430858/mapping-genes-for-calcium-signaling-and-their-associated-human-genetic-disorders
#5
Matthias Hörtenhuber, Enrique M Toledo, Erik Smedler, Ernest Arenas, Seth Malmersjö, Lauri Louhivuori, Per Uhlén
Motivation: Signal transduction via calcium ions (Ca 2+ ) represents a fundamental signaling pathway in all eukaryotic cells. A large portion of the human genome encodes proteins used to assemble signaling systems that can transduce signals with diverse spatial and temporal dynamics. Results: Here, we provide a map of all of the genes involved in Ca 2+ signaling and link these genes to human genetic disorders. Using Gene Ontology terms and genome databases, 1,805 genes were identified as regulators or targets of intracellular Ca 2+ signals...
April 19, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28431087/a-method-for-learning-a-sparse-classifier-in-the-presence-of-missing-data-for-high-dimensional-biological-datasets
#6
Kristen Severson, Brinda Monian, J Christopher Love, Richard D Braatz
Motivation: This work addresses two common issues in building classification models for biological or medical studies: learning a sparse model, where only a subset of a large number of possible predictors is used, and training in the presence of missing data. This work focuses on supervised generative binary classification models, specifically linear discriminant analysis (LDA). The parameters are determined using an expectation maximization algorithm to both address missing data and introduce priors to promote sparsity...
April 18, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28430977/deep-mining-heterogeneous-networks-of-biomedical-linked-data-to-predict-novel-drug-target-associations
#7
Nansu Zong, Hyeoneui Kim, Victoria Ngo, Olivier Harismendy
Motivation: A heterogeneous network topology possessing abundant interactions between biomedical entities has yet to be utilized in similarity-based methods for predicting drug-target associations based on the array of varying features of drugs and their targets. Deep learning reveals features of vertices of a large network that can be adapted in accommodating the similarity-based solutions to provide a flexible method of drug-target prediction. Results: We propose a similarity-based drug-target prediction method that enhances existing association discovery methods by using a topology-based similarity measure...
April 18, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28430949/capturing-non-local-interactions-by-long-short-term-memory-bidirectional-recurrent-neural-networks-for-improving-prediction-of-protein-secondary-structure-backbone-angles-contact-numbers-and-solvent-accessibility
#8
Rhys Heffernan, Yuedong Yang, Kuldip Paliwal, Yaoqi Zhou
Motivation: The accuracy of predicting protein local and global structural properties such as secondary structure and solvent accessible surface area has been stagnant for many years because of the challenge of accounting for non-local interactions between amino acid residues that are close in three-dimensional structural space but far from each other in their sequence positions. All existing machine-learning techniques relied on a sliding window of 10-20 amino acid residues to capture some "short to intermediate" non-local interactions...
April 18, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28430871/cluspro-peptidock-efficient-global-docking-of-peptide-recognition-motifs-using-fft
#9
Kathryn A Porter, Bing Xia, Dmitri Beglov, Tanggis Bohnuud, Nawsad Alam, Ora Schueler-Furman, Dima Kozakov
Summary: We present an approach for the efficient docking of peptide motifs to their free receptor structures. Using a motif based search, we can retrieve structural fragments from the Protein Data Bank (PDB) that are very similar to the peptide's final, bound conformation. We use a Fast Fourier Transform (FFT) based docking method to quickly perform global rigid body docking of these fragments to the receptor. According to CAPRI peptide docking criteria, an acceptable conformation can often be found among the top-ranking predictions...
April 18, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28430868/most-visualization-software-for-producing-automated-textbook-style-maps-of-genome-scale-metabolic-networks
#10
James J Kelley, Shay Maor, Min Kyung Kim, Anatoliy Lane, Desmond S Lun
Summary: Visualization of metabolites, reactions and pathways in genome-scale metabolic networks (GEMs) can assist in understanding cellular metabolism. Three attributes are desirable in software used for visualizing GEMs: 1. automation, since GEMs can be quite large; 2. production of understandable maps that provide ease in identification of pathways, reactions, and metabolites; and 3. visualization of the entire network to show how pathways are interconnected. No software currently exists for visualizing GEMs that satisfies all three characteristics, but MOST-Visualization, an extension of the software package MOST (Metabolic Optimization and Simulation Tool), satisfies (1), and by using a pre-drawn overview map of metabolism based on the Roche map satisfies (2) and comes close to satisfying (3)...
April 18, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28419290/svmqa-support-vector-machine-based-protein-single-model-quality-assessment
#11
Balachandran Manavalan, Jooyoung Lee
Motivation: The accurate ranking of predicted structural models and selecting the best model from a given candidate pool remain as open problems in the field of structural bioinformatics. The quality assessment (QA) methods used to address these problems can be grouped into two categories: consensus methods and single-model methods. Consensus methods in general perform better and attain higher correlation between predicted and true quality measures. However, these methods frequently fail to generate proper quality scores for native-like structures which are distinct from the rest of the pool...
April 13, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28419258/eigenthreader-analogous-protein-fold-recognition-by-efficient-contact-map-threading
#12
Daniel Wa Buchan, David T Jones
Motivation: Protein fold recognition when appropriate, evolutionarily-related, structural templates can be identified is often trivial and may even be viewed as a solved problem. However in cases where no homologous structural templates can be detected, fold recognition is a notoriously difficult problem (Moult, Fidelis et al. 2014). Here we present EigenTHREADER, a novel fold recognition method capable of identifying folds where no homologous structures can be identified. EigenTHREADER takes a query amino acid sequence, generates a map of intra-residue contacts, and then searches a library of contact maps of known structures...
April 13, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28419235/a-bayesian-group-sparse-multi-task-regression-model-for-imaging-genetics
#13
Keelin Greenlaw, Elena Szefer, Jinko Graham, Mary Lesperance, Farouk S Nathoo
Motivation: Recent advances in technology for brain imaging and high-throughput genotyping have motivated studies examining the influence of genetic variation on brain structure. Wang et al. (Bioinformatics, 2012) have developed an approach for the analysis of imaging genomic studies using penalized multi-task regression with regularization based on a novel group l2,1-norm penalty which encourages structured sparsity at both the gene level and SNP level. While incorporating a number of useful features, the proposed method only furnishes a point estimate of the regression coefficients; techniques for conducting statistical inference are not provided...
April 13, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28419223/removal-of-batch-effects-using-distribution-matching-residual-networks
#14
Uri Shaham, Kelly P Stanton, Jun Zhao, Huamin Li, Khadir Raddassi, Ruth Montgomery, Yuval Kluger
Motivation: Sources of variability in experimentally derived data include measurement error in addition to the physical phenomena of interest. This measurement error is a combination of systematic components, originating from the measuring instrument, and random measurement errors. Several novel biological technologies, such as mass cytometry and single-cell RNA-seq, are plagued with systematic errors that may severely affect statistical analysis if the data is not properly calibrated...
April 13, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28419194/dminda-2-0-integrated-and-systematic-views-of-regulatory-dna-motif-identification-and-analyses
#15
Jinyu Yang, Xin Chen, Adam McDermaid, Qin Ma
Motivation: Motif identification and analyses are important and have been long-standing computational problems in bioinformatics. Substantial efforts have been made in this field during the past several decades. However, the lack of intuitive and integrative web servers impedes the progress of making effective use of emerging algorithms and tools. Results: Here we present an integrated web server, DMINDA 2.0, which contains: (i) five motif prediction and analyses algorithms, including a phylogenetic footprinting framework; (ii) 2,125 species with complete genomes to support the above five functions, covering animals, plants, and bacteria; and (iii) bacterial regulon prediction and visualization...
April 13, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28419190/chainy-an-universal-tool-for-standardized-relative-quantification-in-real-time-pcr
#16
Izaskun Mallona, Anna Díez-Villanueva, Berta Martín, Miguel A Peinado
No abstract text is available yet for this article.
April 12, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28407147/haplomerger2-rebuilding-both-haploid-sub-assemblies-from-high-heterozygosity-diploid-genome-assembly
#17
Shengfeng Huang, Mingjing Kang, Anlong Xu
Summary: De novo assembly is a difficult issue for heterozygous diploid genomes. The advent of high-throughput short-read and long-read sequencing technologies provides both new challenges and potential solutions to the issue. Here, we present HaploMerger2 (HM2), an automated pipeline for rebuilding both haploid sub-assemblies from the polymorphic diploid genome assembly. It is designed to work on pre-existing diploid assemblies, which are typically created by using de novo assemblers...
April 12, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28407137/domain-prediction-with-probabilistic-directional-context
#18
Alejandro Ochoa, Mona Singh
Motivation: Protein domain prediction is one of the most powerful approaches for sequence-based function prediction. While domain instances are typically predicted independently of each other, newer approaches have demonstrated improved performance by rewarding domain pairs that frequently co-occur within sequences. However, most of these approaches have ignored the order in which domains preferentially co-occur and have also not modeled domain co-occurrence probabilistically. Results: We introduce a probabilistic approach for domain prediction that models "directional" domain context...
April 12, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28407097/mircat2-accurate-prediction-of-plant-and-animal-micrornas-from-next-generation-sequencing-datasets
#19
Claudia Paicu, Irina Mohorianu, Matthew Stocks, Ping Xu, Aurore Coince, Martina Billmeier, Tamas Dalmay, Vincent Moulton, Simon Moxon
Motivation: MicroRNAs are a class of _21-22 nucleotide small RNAs which are excised from a stable hairpin-like secondary structure. They have important gene regulatory functions and are involved in many pathways including developmental timing, organogenesis and development in eukaryotes. There are several computational tools for miRNA detection from next-generation sequencing (NGS) datasets. However, many of these tools suffer from high false positive and false negative rates. Here we present a novel miRNA prediction algorithm, miRCat2...
April 12, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28407043/subcons-a-new-ensemble-method-for-improved-human-subcellular-localization-predictions
#20
M Salvatore, P Warholm, N Shu, W Basile, A Elofsson
Motivation: Knowledge of the correct protein subcellular localization is necessary for understanding the function of a protein. Unfortunately large-scale experimental studies are limited in their accuracy. Therefore, the development of prediction methods has been limited by the amount of accurate experimental data. However, recently large-scale experimental studies have provided new data that can be used to evaluate the accuracy of subcellular predictions in human cells. Using this data we examined the performance of state of the art methods and developed SubCons, an ensemble method that combines four predictors using a Random Forest classifier...
April 12, 2017: Bioinformatics
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