Prediction of protein subcellular multi-locations with a min-max modular support vector machine

Authors:
Yang Yang;Bao-Liang Lu
Affiliations:
Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

Citing 3
Cited 0

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Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Predicting protein localization in budding Yeast

Bioinformatics
Task decomposition and module combination based on class relations: a modular neural network for pattern classification

IEEE Transactions on Neural Networks

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Abstract

How to predict subcellular multi-locations of proteins with machine learning techniques is a challenging problem in computational biology community. Regarding the protein multi-location problem as a multi-label pattern classification problem, we propose a new predicting method for dealing with the protein subcellular localization problem in this paper. Two key points of the proposed method are to divide a seriously unbalanced multi-location problem into a number of more balanced two-class subproblems by using the part-versus-part task decomposition approach, and learn all of the subproblems by using the min-max modular support vector machine (M3-SVM). To evaluate the effectiveness of the proposed method, we perform experiments on yeast protein data set by using two kinds of task decomposition strategies and three kinds of feature extraction methods. The experimental results demonstrate that our method achieves the highest prediction accuracy, which is much better than that obtained by the existing approach based on the traditional support vector machine.