Predicting protein subcellular locations for Gram-negative bacteria using neural networks ensemble

Authors:
Junwei Ma;Wenqi Liu;Hong Gu
Affiliations:
School of Electronic and Information Engineering, Dalian University of Technology, Dalian, China;School of Electronic and Information Engineering, Dalian University of Technology, Dalian, China;School of Electronic and Information Engineering, Dalian University of Technology, Dalian, China
Venue:
CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
Year:
2009

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Abstract

Many species of Gram-negative bacteria are pathogenic bacteria that can cause disease in a host organism. This pathogenic capability is usually associated with certain components in Gram-negative cells, so it is highly desirable to develop an effective method to predict the Gram-negative bacterial protein subcellular locations. Reflecting the wide applications of neural networks in this field, we design seven different training functions based on Elman networks, and use a genetic algorithm to select the proper networks for an ensemble. Experimental results show that the neural networks ensemble has a dominant advantage in performance.