title: Prediction and validation of gene-disease associations using methods inspired by social network analyses.
abstract: Correctly identifying associations of genes with diseases has long been a goal in biology. With the emergence of large-scale gene-phenotype association datasets in biology, we can leverage statistical and machine learning methods to help us achieve this goal. In this paper, we present two methods for predicting gene-disease associations based on functional gene associations and gene-phenotype associations in model organisms. The first method, the Katz measure, is motivated from its success in social network link prediction, and is very closely related to some of the recent methods proposed for gene-disease association inference. The second method, called Catapult (Combining dATa Across species using Positive-Unlabeled Learning Techniques), is a supervised machine learning method that uses a biased support vector machine where the features are derived from walks in a heterogeneous gene-trait network. We study the performance of the proposed methods and related state-of-the-art methods using two different evaluation strategies, on two distinct data sets, namely OMIM phenotypes and drug-target interactions. Finally, by measuring the performance of the methods using two different evaluation strategies, we show that even though both methods perform very well, the Katz measure is better at identifying associations between traits and poorly studied genes, whereas Catapult is better suited to correctly identifying gene-trait associations overall [corrected].
authors: Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, University of Texas, Austin, Texas, United States of America.
authors: U Martin
authors: UM
authors: Singh-Blom
authors: Nagarajan
authors: N
authors: Natarajan
authors: Ambuj
authors: A
authors: Tewari
authors: John O
authors: JO
authors: Woods
authors: Inderjit S
authors: IS
authors: Dhillon
authors: Edward M
authors: EM
authors: Marcotte
mesh_terms: D000465:Algorithms; D000818:Animals; D053263:Gene Regulatory Networks; D056726:Genetic Association Studies / Q000379:methods*; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D025941:Protein Interaction Mapping; D060756:Social Networking; D060388:Support Vector Machine
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