Kim, J.K. and Raghava, G.P.S. and Bang, S. and Choi, S. (2006) Prediction of subcellular localization of proteins using pairwise sequence alignment and support vector machin. Pattern Recognition Letters, 27. pp. 996-1001. ISSN 0167-8655
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Abstract
Predicting the destination of a protein in a cell is important for annotating the function of the protein. Recent advances have allowed us to develop more accurate methods for predicting the subcellular localization of proteins. One of the most important factors for improving the accuracy of these methods is related to the introduction of new useful features for protein sequences. In this paper we present a new method for extracting appropriate features from the sequence data by com- puting pairwise sequence alignment scores. As a classifier, support vector machine (SVM) is used. The overall prediction accuracy evaluated by the jackknife valida- tion technique reached 94.70% for the eukaryotic non-plant data set and 92.10% for the eukaryotic plant data set, which is the highest prediction accuracy among the methods reported so far with such data sets. Our experimental results confirm that our feature extraction method based on pairwise sequence alignment is useful for this classification problem.
| Item Type: | Article |
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| Additional Information: | Copyright of this article belongs to Elsevier Science. |
| Uncontrolled Keywords: | Pairwise sequence alignment, Subcellular localization, Support vector Machine |
| Subjects: | Q Science > QR Microbiology |
| Depositing User: | Dr. K.P.S.Sengar |
| Date Deposited: | 17 Feb 2012 16:44 |
| Last Modified: | 21 Feb 2012 11:37 |
| URI: | http://crdd.osdd.net/open/id/eprint/1023 |
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