Hybrid SVM-ANFIS for protein subcellular location prediction

Authors:
Bo Jin;Yuchun Tang;Yan-Qing Zhang
Affiliations:
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
Venue:
International Journal of Computational Intelligence in Bioinformatics and Systems Biology
Year:
2009

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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.