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Protein Subcellular Localization Prediction Based on Compartment-Specific Biological Features

Protein Subcellular Localization Prediction Based on Compartment-Specific Biological Features

This video was recorded at Machine Learning Summer School (MLSS), Taipei 2006. 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 inte-grating 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 bench-mark 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.

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