Protein function prediction based on patterns in biological networks
RECOMB'08 Proceedings of the 12th annual international conference on Research in computational molecular biology
Predicting Protein Function by Multi-Label Correlated Semi-Supervised Learning
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Motivation: In general, most accurate gene/protein annotations are provided by curators. Despite having lesser evidence strengths, it is inevitable to use computational methods for fast and a priori discovery of protein function annotations. This paper considers the problem of assigning Gene Ontology (GO) annotations to partially annotated or newly discovered proteins. Results: We present a data mining technique that computes the probabilistic relationships between GO annotations of proteins on protein-protein interaction data, and assigns highly correlated GO terms of annotated proteins to non-annotated proteins in the target set. In comparison with other techniques, probabilistic suffix tree and correlation mining techniques produce the highest prediction accuracy of 81% precision with the recall at 45%. Availability: Code is available upon request. Results and used materials are available online at Contact: kirac@case.edu