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Title: Hybrid SVM-ANFIS for protein subcellular location prediction

Authors: Bo Jin, Yuchun Tang, Yan-Qing Zhang

Addresses: Department of Computer Science, Georgia State University, Atlanta, GA 30302-3994, USA. ' Department of Computer Science, Georgia State University, Atlanta, GA 30302-3994, USA. ' Department of Computer Science, Georgia State University, Atlanta, GA 30302-3994, USA

Abstract: Predicting protein subcellular locations may help us understand protein functions and analyse protein interactions with other molecules. Many machine learning and computational techniques have been used to predict protein subcellular locations. In this paper, we propose a new hybrid classification system called SVM-ANFIS based on Support Vector Machines and Adaptive Neuro Fuzzy Inference System for protein subcellular location prediction. The experimental results show that the new system can not only achieve high total accuracies but also improve local accuracies in protein subcellular location prediction.

Keywords: SVMs; support vector machines; ANFIS; adaptive neuro fuzzy inference systems; protein subcellular locations; bioinformatics; location prediction; protein functions; protein interactions; fuzzy logic.

DOI: 10.1504/IJCIBSB.2009.024051

International Journal of Computational Intelligence in Bioinformatics and Systems Biology, 2009 Vol.1 No.1, pp.59 - 73

Available online: 24 Mar 2009 *

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