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Erschienen in:
2005 | OriginalPaper | Buchkapitel
Predicting Subcellular Localization of Proteins Using Support Vector Machine with N-Terminal Amino Composition
verfasst von : Yan-fu Li, Juan Liu
Erschienen in: Advanced Data Mining and Applications
Verlag: Springer Berlin Heidelberg
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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.