Prediction of subcellular localization of proteins is important for genome annotation, protein function prediction, and drug discovery. We present a prediction method for Gram-negative bacteria that uses ten one-versus-one support vector machine (SVM) classifiers, where compartment-specific biological features are selected as input to each SVM classifier. The final prediction of localization sites is determined by integrating the results from ten binary classifiers using a combination of majority votes and a probabilistic method. The overall accuracy reaches 91.4%, which is 1.6% better than the state-of-the-art system, in a ten-fold cross-validation evaluation on a benchmark data set. We demonstrate that feature selection guided by biological knowledge and insights in one-versus-one SVM classifiers can lead to a significant improvement in the prediction performance. Our model is also used to produce highly accurate prediction of 92.8% overall accuracy for proteins of dual localizations.
|頁（從 - 到）||325-330|
|期刊||Computational systems bioinformatics / Life Sciences Society. Computational Systems Bioinformatics Conference|
|出版狀態||已發佈 - 2006|
ASJC Scopus subject areas
Su, C. Y., Lo, A., Chiu, H. S., Sung, T. Y., & Hsu, W. L. (2006). Protein subcellular localization prediction based on compartment-specific biological features. Computational systems bioinformatics / Life Sciences Society. Computational Systems Bioinformatics Conference, 325-330.