Journal
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
Volume 72, Issue 3, Pages 1030-1037Publisher
WILEY
DOI: 10.1002/prot.21989
Keywords
gene prioritization; gene-disease associations; protein-disease associations; protein function prediction
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Funding
- NIA NIH HHS [P01 AG018397, P01AG018397] Funding Source: Medline
- NLM NIH HHS [K22 LM009135, K22LM009135, K22 LM009135-01, K22 LM009135-03, K22 LM009135-02, R01 LM009722] Funding Source: Medline
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One of the most important tasks of modern bioinformatics is the development Of computational tools that can be used to understand and treat human disease. To date, a variety of methods have been explored and algorithms for candidate gene prioritization are gaining in their usefulness. Here, we propose an algorithm for detecting gene-disease associations based on the human protein-protein interaction network, known gene-disease associations, protein sequence, and protein functional information at the molecular level. Our method, PhenoPred, is supervised: first, we mapped each gene/protein onto the spaces of disease and functional terms based on distance to all annotated proteins in the protein interaction network. We also encoded sequence, function, physicochemical, and predicted structural properties, such as secondary structure and flexibility. We then trained support vector machines to detect gene-disease associations for a number of terms in Disease Ontology and provided evidence that, despite the noise/incompleteness of experimental data and unfinished ontology of diseases, identification of candidate genes can be successful even when a large number of candidate disease terms are predicted on simultaneously.
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