A Novel Method for Prediction of Protein Domain Using Distance-Based Maximal Entropy

作     者：Shu-xue Zou Yan-xin Huang Yan Wang Chun-guang Zhou 

作者机构：Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun 130012 P. R. China 

出 版 物：《Journal of Bionic Engineering》 (仿生工程学报（英文版）)

年 卷 期：2008年第5卷第3期

页      面：215-223页

核心收录：

学科分类：0710[理学-生物学] 07[理学] 09[农学] 

基　　金：National Natural Science Foundation of China (Grant No. 60433020, 60673099, 60673023) "985" project of Jilin University 

主　　题：protein domain boundary SVM imbalanced data learning distance-based maximal entropy 

摘      要：Detecting the boundaries of protein domains is an important and challenging task in both experimental and computational structural biology. In this paper, a promising method for detecting the domain structure of a protein from sequence information alone is presented. The method is based on analyzing multiple sequence alignments derived from a database search. Multiple measures are defined to quantify the domain information content of each position along the sequence. Then they are combined into a single predictor using support vector machine. What is more important, the domain detection is first taken as an imbal- anced data learning problem. A novel undersampling method is proposed on distance-based maximal entropy in the feature space of Support Vector Machine （SVM）. The overall precision is about 80%. Simulation results demonstrate that the method can help not only in predicting the complete 3D structure of a protein but also in the machine learning system on general im- balanced datasets.