A new Approach using Distance Matrix Image to Predict G-protein-coupled Receptor Functional Classes

摘要：

G-protein coupled receptors (GPCRs) play a key role in diverse physiological processes and are the targets of over half of the marketed drugs. Using the pseudo amino acid (PseAA) composition to represent the sample of a protein can incorporate a considerable amount of sequence pattern information so as to improve the prediction quality for its structural or functional classification. In this paper, the protein distance matrix image(DMI) is introduced. Based on the protein DM1, geometric moments derived from each of the protein sequences concerned are adopted for its PseAA. It was demonstrated thru the jackknife cross-validation test that the overall success rate by the new approach was very effective. The success rates thus obtained on a previously constructed benchmark dataset are quite promising.

关键词： G-protein coupled receptor Distance Matrix Fuzzy K-nearest neighbor Geometric Moment Jackknife cross-validation test

作者: Xuan Xiao Chun-cai Xiao Pu Wang

作者单位: Computer Department Jing-De-Zhen Ceramic Institute Jing-De-Zhen 333403 China

会议类型: 国际会议

会议名称: 2010 International Conference on Future Information Technology(2010年未来信息技术国际会议 ICFIT 2010)

会议地点: 长沙

会议语种:英文

页码: 255-258

在线出版日期: 2010-12-14（万方平台首次上网日期，不代表论文的发表时间）