Predicting subcellular localization of proteins using support vector machine with n-terminal amino composition

Authors:
Yan-fu Li;Juan Liu
Affiliations:
International School of Software, Wuhan University, Wuhan, China;International School of Software, Wuhan University, Wuhan, China
Venue:
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Year:
2005

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Abstract

Prediction of protein subcellular localization is one of the hot research topics in bioinformatics. In this paper, several support vector machines (SVM) with a new presented coding scheme method based on N-terminal amino compositions are first trained to discriminate between proteins destined for the mitochondrion, the chloroplast, the secretory pathway, and ‘other' localizations. Then a decision unit is used to make the final prediction based on several SVMs' outputs. Tested on redundancy-reduced sets, the proposed method reached 89.6 % (plant) and 91.9% (non-plant) total accuracies, which, to the best of our knowledge, are the highest ever reported using the same data sets.