期刊
IEEE TRANSACTIONS ON NANOBIOSCIENCE
卷 11, 期 3, 页码 237-243出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNB.2012.2212249
关键词
Binary relevance; classifier chain; multilabel learning; protein subcellular localization
资金
- Natural Science Foundation of China [61005006, 61105053]
- Fundamental Research Funds for the Central Universities
- Division of Computing and Communication Foundations
- Direct For Computer & Info Scie & Enginr [1049864] Funding Source: National Science Foundation
Protein subcellular localization aims at predicting the location of a protein within a cell using computational methods. Knowledge of subcellular localization of proteins indicates protein functions and helps in identifying drug targets. Prediction of protein subcellular localization is an important but challenging problem, particularly when proteins may simultaneously exist at, or move between, two or more different subcellular location sites. Most of the existing protein subcellular localization methods are only used to deal with the single-location proteins. To better reflect the characteristics of multiplex proteins, we formulate prediction of subcellular localization of multiplex proteins as a multilabel learning problem. We present and compare two multilabel learning approaches, which exploit correlations between labels and leverage label-specific features, respectively, to induce a high quality prediction model. Experimental results on six protein data sets under various organisms show that our described methods achieve significantly higher performance than any of the existing methods. Among the different multilabel learning methods, we find that methods exploiting label correlations performs better than those leveraging label-specific features.
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