Protein function prediction using weak-label learning

Authors:
Guoxian Yu;Guoji Zhang;Huzefa Rangwala;Carlotta Domeniconi;Zhiwen Yu
Affiliations:
South China Univ. of Tech., Guangzhou, China;South China Univ. of Tech., Guangzhou, China;George Mason University, Fairfax, VA;George Mason University, Fairfax, VA;South China Univ. of Tech., Guangzhou, China
Venue:
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Year:
2012

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Abstract

Protein function prediction is one of the fundamental issues in the post-genomic era. Multi-label learning is widely used for predicting functions of proteins. Most multi-label learning methods assume that the proteins with annotation do not have any missing functions. However, in practice, we may have a subset of the ground-truth functions for a protein, and whether the protein has other functions is unknown. To complete the partial annotation of proteins, we propose a Protein Function Prediction method with Weak-label Learning (ProWL), and a variant of ProWL (ProWL-IF). Both ProWL and ProWL-IF replenish the functions of proteins under the assumption that proteins are partially annotated. In addition, ProWL-IF takes advantage of the knowledge that a protein cannot have certain functions (called irrelevant functions), which can further boost the performance of protein function prediction. Our experimental results on protein-protein interaction and gene microarray expression benchmarks validate the effectiveness of ProWL and ProWL-IF.