Association classification algorithm based on structure sequence in protein secondary structure prediction

作     者：Zhou, Zhun Yang, Bingru Hou, Wei 

作者机构：Tsinghua Univ Dept Environm Sci & Engn Beijing 100084 Peoples R China Univ Sci & Technol Beijing Dept Informat Engn Beijing 100083 Peoples R China 

出 版 物：《EXPERT SYSTEMS WITH APPLICATIONS》 (专家系统及其应用)

年 卷 期：2010年第37卷第9期

页      面：6381-6389页

核心收录：

学科分类：1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Association classification Protein secondary structure prediction KDD center dot (Knowledge Discovery(center dot)) Compound pyramid model 

摘      要：Objective: To propose a novel associate classification algorithm SAC (structural association classification) and develop a compound pyramid model for accurate and precise protein secondary structure prediction. Method: Based on the slide window theory, the protein sequence was treated as a window with length of 13, in which the target amino acid resided in the center, while the remaining area was targeted as secondary amino acid structures. To the head and tail of the sequence, the mirror method was employed to fill the space with an opposite- position structure in relation to the central position. In the mining process, the KDD center dot model not only focuses on the high support and confidence rules, but also pay attention to high confidence and low support rules, which is called knowledge in shortage . Towards the end of the mining process, sets H, E and C, consisted of rule sets whose consequents are alpha-helix, beta-sheet and C-coil, were created respectively to meet the basic requirements for the protein secondary structure prediction. The knowledge base of protein secondary structure was then established with these three newly-acquired rule sets. Through the CMAR (Classification based on Multiple Association rules) algorithm, a novel multi-classifier was developed to determine the best likelihood of a given window to the secondary structure through the adjacent information on amino acid sequential window and screening of three different rule sets. Result: The protein knowledge base consisted of 8049 rules corresponding to sets H, E and C with 2642, 1895 and 3512 rules, respectively, was obtained. Experiment shows, theoretically, accuracy ratio exceeded 85% when confidence threshold value was 70% and 90%. Through the classification process using the multi-classifier SAC developed in four experiments, the significantly high accuracy and recall ratios up to 83.06% (According to Q(3) criterion, followed by abbreviation) in RS126 (Chen & Chaudhari, 2007;Guo et