Uncovering protein interaction in abstracts and text using a novel linear model and word proximity networks

We participated in three of the protein-protein interaction subtasks of the Second BioCreative Challenge: classification of abstracts relevant for protein-protein interaction (IAS), discovery of protein pairs (IPS) and text passages characterizing protein interaction (ISS) in full text documents. We approached the abstract classification task with a novel, lightweight linear model inspired by spam-detection techniques, as well as an uncertainty-based integration scheme. We also used a Support Vector Machine and the Singular Value Decomposition on the same features for comparison purposes. Our approach to the full text subtasks (protein pair and passage identification) includes a feature expansion method based on word-proximity networks. Our approach to the abstract classification task (IAS) was among the top submissions for this task in terms of the measures of performance used in the challenge evaluation (accuracy, F-score and AUC). We also report on a web-tool we produced using our approach: the Protein Interaction Abstract Relevance Evaluator (PIARE). Our approach to the full text tasks resulted in one of the highest recall rates as well as mean reciprocal rank of correct passages. Our approach to abstract classification shows that a simple linear model, using relatively few features, is capable of generalizing and uncovering the conceptual nature of protein-protein interaction from the bibliome. Since the novel approach is based on a very lightweight linear model, it can be easily ported and applied to similar problems. In full text problems, the expansion of word features with word-proximity networks is shown to be useful, though the need for some improvements is discussed

Extensible Terascale Facility (ETF): Indiana-Purdue Grid (IP-Grid)

NSF Award ID: ACI-0338618 Project Dates: 10/1/03-9/30/0

A genome-wide MeSH-based literature mining system predicts implicit gene-to-gene relationships and networks

Abstract Background The large amount of literature in the post-genomics era enables the study of gene interactions and networks using all available articles published for a specific organism. MeSH is a controlled vocabulary of medical and scientific terms that is used by biomedical scientists to manually index articles in the PubMed literature database. We hypothesized that genome-wide gene-MeSH term associations from the PubMed literature database could be used to predict implicit gene-to-gene relationships and networks. While the gene-MeSH associations have been used to detect gene-gene interactions in some studies, different methods have not been well compared, and such a strategy has not been evaluated for a genome-wide literature analysis. Genome-wide literature mining of gene-to-gene interactions allows ranking of the best gene interactions and investigation of comprehensive biological networks at a genome level. Results The genome-wide GenoMesh literature mining algorithm was developed by sequentially generating a gene-article matrix, a normalized gene-MeSH term matrix, and a gene-gene matrix. The gene-gene matrix relies on the calculation of pairwise gene dissimilarities based on gene-MeSH relationships. An optimized dissimilarity score was identified from six well-studied functions based on a receiver operating characteristic (ROC) analysis. Based on the studies with well-studied Escherichia coli and less-studied Brucella spp., GenoMesh was found to accurately identify gene functions using weighted MeSH terms, predict gene-gene interactions not reported in the literature, and cluster all the genes studied from an organism using the MeSH-based gene-gene matrix. A web-based GenoMesh literature mining program is also available at: http://genomesh.hegroup.org. GenoMesh also predicts gene interactions and networks among genes associated with specific MeSH terms or user-selected gene lists. Conclusions The GenoMesh algorithm and web program provide the first genome-wide, MeSH-based literature mining system that effectively predicts implicit gene-gene interaction relationships and networks in a genome-wide scope.http://deepblue.lib.umich.edu/bitstream/2027.42/112478/1/12918_2013_Article_1166.pd

Comparative genomics and transcriptomics elucidate virulence mechanisms and host responses in infectious diseases

The main thematic area of the present thesis is the development and application of bioinformatics pipelines, namely whole-genome sequence (WGS) analysis and transcriptome profile analysis. These pipelines were applied to study the fungal pathogen Aspergillus fumigatus (Manuscripts I, III, and IV) and the early human immune mechanisms activated in response to different types of pathogens (bacteria, fungi, and co-infections) in sepsis patients (Manuscript II). The comparative genomic and transcriptomic analyses applied in my thesis have significantly improved our understanding of fungal pathogenicity as well as the pathogen-specific immune response mechanisms of the human host. Next to a number of novel insights, my work included in this thesis has generated a large number of new hypotheses based on big-data analysis, offering the scientific community the possibility to design exciting new research to confirm them in future experimental studies and bring us closer to actual precision medicine for infectious diseases

The Gene Ontology Handbook

bioinformatics; biotechnolog

Computational Approaches To Improving The Reconstruction Of Metabolic Pathway

Metabolic pathway reconstruction is the essence of systems biology where in silico modeling and prediction of the cell's function is based on the interaction of the cell's components represented as a network of reactions. The reconstructed model and the associated database of information about the organism's genes and their functional roles facilitate a variety of analysis and simulation techniques that can enrich our understanding. However, there are unresolved issues for genome-scale metabolic network reconstruction, such as our incomplete knowledge of the cell's networks for metabolism, transport, and regulation; the completeness, accuracy, and specificity of the annotation of genomes; and our ability to fully utilise the available information from -omics (genomics, proteomics, metabolomics, etc) for the reconstruction of the networks. These issues result in incomplete metabolic models, which limit our ability to perform analysis of and to make predictions about the cell that are based on the network model. This dissertation discusses the state-of-the-art of metabolic pathway reconstruction and highlights the outstanding issues. In particular, we consider a number of case studies using genomes of fungi relevant to industrial applications, such as biofuels, to demonstrate the performance of existing techniques and illustrate the issues. Our case studies focus on the cell's central metabolism, and the utilisation and transport of sugars as a carbon source, since these are essential concerns for industrial applications. A significant deficiency in the existing state-of-the-art for the reconstruction of metabolic pathways is the ability to associate genes and proteins to the transport reactions that move specific compounds across the membranes of the cell. The dissertation reviews the state-of-the- art of prediction methods for transmembrane transport proteins by developing a scheme to describe and compare existing methods, and applying the existing techniques to the v fungal genome of A. niger CBS 513.88. This reveals the split between those methods that use the Transporter Classification (TC) as their target for prediction, and those that use the type of chemical substrates being transported as their target. Despite this difficulty in comparing approaches, it is clear that the state-of-the-art cannot predict specific substrates being transported, and hence cannot associate genes and proteins to the transport reactions. The dissertation presents TransATH, which stands for Transporters via ATH (Annotation Transfer by Homology), a system which automates Saier's protocol and includes the computation of subcellular localization and improves the computation of transmembrane segments. The choice of thresholds for the parameters of TransATH is investigated to determine optimal performance as defined by a gold standard set of transporters and non-transporters from S. cerevisiae. The dissertation demonstrates TransATH on the fungal genome of A. niger CBS 513.88 and evaluates the correctness of TransATH using the curated information in AspGD (the Aspergillus Database). A website for TransATH is available for use

Development of Virtual Screening and In Silico Biomarker Identification Model for Pharmaceutical Agents

Ph.DDOCTOR OF PHILOSOPH

Development of a Hepatitis C Virus knowledgebase with computational prediction of functional hypothesis of therapeutic relevance

Philosophiae Doctor - PhDTo ameliorate Hepatitis C Virus (HCV) therapeutic and diagnostic challenges requires robust intervention strategies, including approaches that leverage the plethora of rich data published in biomedical literature to gain greater understanding of HCV pathobiological mechanisms. The multitudes of metadata originating from HCV clinical trials as well as low and high-throughput experiments embedded in text corpora can be mined as data sources for the implementation of HCV-specific resources. HCV-customized resources may support the generation of worthy and testable hypothesis and reveal potential research clues to augment the pursuit of efficient diagnostic biomarkers and therapeutic targets. This research thesis report the development of two freely available HCV-specific web-based resources: (i) Dragon Exploratory System on Hepatitis C Virus (DESHCV) accessible via http://apps.sanbi.ac.za/DESHCV/ or http://cbrc.kaust.edu.sa/deshcv/ and (ii) Hepatitis C Virus Protein Interaction Database (HCVpro) accessible via http://apps.sanbi.ac.za/hcvpro/ or http://cbrc.kaust.edu.sa/hcvpro/. DESHCV is a text mining system implemented using named concept recognition and cooccurrence based approaches to computationally analyze about 32, 000 HCV related abstracts obtained from PubMed. As part of DESHCV development, the pre-constructed dictionaries of the Dragon Exploratory System (DES) were enriched with HCV biomedical concepts, including HCV proteins, name variants and symbols to enable HCV knowledge specific exploration. The DESHCV query inputs consist of user-defined keywords, phrases and concepts. DESHCV is therefore an information extraction tool that enables users to computationally generate association between concepts and support the prediction of potential hypothesis with diagnostic and therapeutic relevance. Additionally, users can retrieve a list of abstracts containing tagged concepts that can be used to overcome the herculean task of manual biocuration. DESHCV has been used to simulate previously reported thalidomide-chronic hepatitis C hypothesis and also to model a potentially novel thalidomide-amantadine hypothesis. HCVpro is a relational knowledgebase dedicated to housing experimentally detected HCV-HCV and HCV-human protein interaction information obtained from other databases and curated from biomedical journal articles. Additionally, the database contains consolidated biological information consisting of hepatocellular carcinoma (HCC) related genes, comprehensive reviews on HCV biology and drug development, functional genomics and molecular biology data, and cross-referenced links to canonical pathways and other essential biomedical databases. Users can retrieve enriched information including interaction metadata from HCVpro by using protein identifiers, gene chromosomal locations, experiment types used in detecting the interactions, PubMed IDs of journal articles reporting the interactions, annotated protein interaction IDs from external databases, and via “string searches”. The utility of HCVpro has been demonstrated by harnessing integrated data to suggest putative baseline clues that seem to support current diagnostic exploratory efforts directed towards vimentin. Furthermore, eight genes comprising of ACLY, AZGP1, DDX3X, FGG, H19, SIAH1, SERPING1 and THBS1 have been recommended for possible investigation to evaluate their diagnostic potential. The data archived in HCVpro can be utilized to support protein-protein interaction network-based candidate HCC gene prioritization for possible validation by experimental biologists.South Afric