Nucleic Acids Research Advance Access originally published online on September 11, 2006
Nucleic Acids Research 2006 34(17):4655-4666; doi:10.1093/nar/gkl638
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Nucleic Acids Research, 2006, Vol. 34, No. 17 4655-4666
© 2006 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Computational Biology |
PLPD: reliable protein localization prediction from imbalanced and overlapped datasets
1 Department of BioSystems, KAIST Daejeon City, Republic of Korea 2 Advanced Information Technology Research Center, KAIST Daejeon City, Republic of Korea 3 School of Computer Science and Engineering, Chung-Ang University Seoul City, Republic of Korea
*To whom correspondence should be addressed. Tel: +82 42 869 4316; Fax: +82 42 869 8680; Email: dhlee{at}biosoft.kaist.ac.kr
Received May 5, 2006. Revised July 25, 2006. Accepted August 10, 2006.
Subcellular localization is one of the key functional characteristics of proteins. An automatic and efficient prediction method for the protein subcellular localization is highly required owing to the need for large-scale genome analysis. From a machine learning point of view, a dataset of protein localization has several characteristics: the dataset has too many classes (there are more than 10 localizations in a cell), it is a multi-label dataset (a protein may occur in several different subcellular locations), and it is too imbalanced (the number of proteins in each localization is remarkably different). Even though many previous works have been done for the prediction of protein subcellular localization, none of them tackles effectively these characteristics at the same time. Thus, a new computational method for protein localization is eventually needed for more reliable outcomes. To address the issue, we present a protein localization predictor based on D-SVDD (PLPD) for the prediction of protein localization, which can find the likelihood of a specific localization of a protein more easily and more correctly. Moreover, we introduce three measurements for the more precise evaluation of a protein localization predictor. As the results of various datasets which are made from the experiments of Huh et al. (2003), the proposed PLPD method represents a different approach that might play a complimentary role to the existing methods, such as Nearest Neighbor method and discriminate covariant method. Finally, after finding a good boundary for each localization using the 5184 classified proteins as training data, we predicted 138 proteins whose subcellular localizations could not be clearly observed by the experiments of Huh et al. (2003).
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