Taxonomy-superimposed graph mining
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
An Introduction to Metabolic Networks and Their Structural Analysis
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Automating diseases diagnosis in human: a time series analysis
Proceedings of the International Conference and Workshop on Emerging Trends in Technology
Functional similarities of reaction sets in metabolic pathways
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
Journal of Biomedical Informatics
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Motivation: Biological pathways provide significant insights on the interaction mechanisms of molecules. Presently, many essential pathways still remain unknown or incomplete for newly sequenced organisms. Moreover, experimental validation of enormous numbers of possible pathway candidates in a wet-lab environment is time-and effort-extensive. Thus, there is a need for comparative genomics tools that help scientists predict pathways in an organism's biological network. Results: In this article, we propose a technique to discover unknown pathways in organisms. Our approach makes in-depth use of Gene Ontology (GO)-based functionalities of enzymes involved in metabolic pathways as follows: Model each pathway as a biological functionality graph of enzyme GO functions, which we call pathway functionality template.Locate frequent pathway functionality patterns so as to infer previously unknown pathways through pattern matching in metabolic networks of organisms. We have experimentally evaluated the accuracy of the presented technique for 30 bacterial organisms to predict around 1500 organism-specific versions of 50 reference pathways. Using cross-validation strategy on known pathways, we have been able to infer pathways with 86% precision and 72% recall for enzymes (i.e. nodes). The accuracy of the predicted enzyme relationships has been measured at 85% precision with 64% recall. Availability: Code upon request. Contact: ali.cakmak@case.edu Supplementary information: Supplementary data are available at Bioinformatics online.