NDLI: Protein function prediction using weak-label learning

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Protein function prediction using weak-label learning

Content Provider	ACM Digital Library
Author	Domeniconi, Carlotta Zhang, Guoji Rangwala, Huzefa Yu, Guoxian Yu, Zhiwen
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.
Starting Page	202
Ending Page	209
Page Count	8
File Format	PDF
ISBN	9781450316705
DOI	10.1145/2382936.2382962
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-10-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Weak-label learning Protein function prediction Multi-label learning
Content Type	Text
Resource Type	Article

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