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An integrated approach to inferring gene–disease associations in humans
Author(s) -
Radivojac Predrag,
Peng Kang,
Clark Wyatt T.,
Peters Brandon J.,
Mohan Amrita,
Boyle Sean M.,
Mooney Sean D.
Publication year - 2008
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.21989
Subject(s) - computational biology , disease , identification (biology) , candidate gene , computer science , gene , gene ontology , support vector machine , protein sequencing , sequence (biology) , biology , machine learning , bioinformatics , genetics , peptide sequence , gene expression , medicine , botany , pathology
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. Availability: www.phenopred.org . Proteins 2008. © 2008 Wiley‐Liss, Inc.