Using Efficient RBF Networks to Classify Transport Proteins Based on PSSM Profiles and Biochemical Properties

Authors:
Yu-Yen Ou;Shu-An Chen
Affiliations:
Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, Taiwan;Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, Taiwan
Venue:
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Year:
2009

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Abstract

Transport proteins are difficult to understand by biological experiments due to the difficulty in obtaining crystals suitable for X-ray diffraction. Therefore, the use of computational techniques is a powerful approach to annotate the function of proteins. In this work, we propose a method based on PSSM profiles and other biochemical properties for classifying three major classes of transport proteins. Our method shows a 5-fold cross validation accuracy of 75.4% in a set of 1146 transport proteins with less than 20% mutual sequence identity.