title: Learning protein multi-view features in complex space.
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/ .
authors: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
authors: Dong-Jun
authors: DJ
authors: Yu
authors: Jun
authors: J
authors: Hu
authors: Xiao-Wei
authors: XW
authors: Wu
authors: Hong-Bin
authors: HB
authors: Shen
authors: Jun
authors: J
authors: Chen
authors: Zhen-Min
authors: ZM
authors: Tang
authors: Jian
authors: J
authors: Yang
authors: Jing-Yu
authors: JY
authors: Yang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003198:Computer Simulation; D003553:Cystine / Q000737:chemistry; D008958:Models, Molecular; D025341:Principal Component Analysis; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D012984:Software
chemical_list: D011506:Proteins; D003553:Cystine