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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Xiao Wang Guo-Zheng Li Jia-Ming Liu Rui-Wei Zhao |
| Copyright Year | 2011 |
| Abstract | Protein sub cellular localization aims at predicting the location of a protein within a cell using computational methods. Knowledge of sub cellular localization of proteins indicates protein functions and helps in identifying drug targets. Prediction of protein sub cellular localization is an important but challenging problem, particularly when proteins may simultaneously exist at, or move between, two or more different sub cellular 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 multi-label learning problem. We present and compare two multi-label 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 multi-label learning methods, we find that methods exploiting label correlations performs better than those leveraging label-specific features. |
| Starting Page | 282 |
| Ending Page | 285 |
| File Size | 155686 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781457717994 |
| DOI | 10.1109/BIBM.2011.36 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-11-12 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Proteins Multiplexing Learning systems Protein subcellular localization Protein engineering Correlation Humans Machine learning Multi-label learning |
| Content Type | Text |
| Resource Type | Article |
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