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Learning protein multi-view features in complex space
Authors:Dong-Jun Yu  Jun Hu  Xiao-Wei Wu  Hong-Bin Shen  Jun Chen  Zhen-Min Tang  Jian Yang  Jing-Yu Yang
Institution:1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
3. Changshu Institute, Nanjing University of Science and Technology, Changshu, 215500, China
2. Department of Automation, Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai Jiao Tong University, Shanghai, 200240, China
Abstract:Protein attribute prediction from primary sequences is an important task and how to extract discriminative features is one of the most crucial aspects. Because single-view feature cannot reflect all the information of a protein, fusing multi-view features is considered as a promising route to improve prediction accuracy. In this paper, we propose a novel framework for protein multi-view feature fusion: first, features from different views are parallely combined to form complex feature vectors; Then, we extend the classic principal component analysis to the generalized principle component analysis for further feature extraction from the parallely combined complex features, which lie in a complex space. Finally, the extracted features are used for prediction. Experimental results on different benchmark datasets and machine learning algorithms demonstrate that parallel strategy outperforms the traditional serial approach and is particularly helpful for extracting the core information buried among multi-view feature sets. A web server for protein structural class prediction based on the proposed method (COMSPA) is freely available for academic use at: http://www.csbio.sjtu.edu.cn/bioinf/COMSPA/.
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