期刊
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
卷 11, 期 3, 页码 579-591出版社
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2013.142
关键词
Protein function prediction; multi-label learning; incomplete annotations
类别
资金
- US NSF [IIS-0905117, IIS-1252318]
- Natural Science Foundation of China [61003174, 61101234, 61372138]
- Fundamental Research Funds for the Central Universities of China [XDJK2014C044, XDJK2013C026, XDJK2013C123]
- Doctoral Fund of Southwest University [SWU110063, SWU113034]
- China Scholarship Council (CSC)
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1252318] Funding Source: National Science Foundation
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [0905117] Funding Source: National Science Foundation
- Division Of Computer and Network Systems
- Direct For Computer & Info Scie & Enginr [1205453] Funding Source: National Science Foundation
Automated protein function prediction is one of the grand challenges in computational biology. Multi-label learning is widely used to predict functions of proteins. Most of multi-label learning methods make prediction for unlabeled proteins under the assumption that the labeled proteins are completely annotated, i.e., without 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 predict protein functions with incomplete annotations, we propose a Protein Function Prediction method with Weak-label Learning (ProWL) and its variant ProWL-IF. Both ProWL and ProWL-IF can replenish the missing functions of proteins. In addition, ProWL-IF makes use of the knowledge that a protein cannot have certain functions, which can further boost the performance of protein function prediction. Our experimental results on protein-protein interaction networks and gene expression benchmarks validate the effectiveness of both ProWL and ProWL-IF.
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