Prediction of MHC II-binding peptides using rough set-based rule sets ensemble

Peptide binding to Major Histocompatibility Complex (MHC) is a prerequisite for any T cell-mediated immune response. Predicting which peptides can bind to a specific MHC molecule is indispensable to minimizing the number of peptides required to synthesize, to the development of vaccines and immunotherapy of cancer, and to aiding to understand the specificity of T-cell mediated immunity. At present, although predictions based on machine learning methods have good prediction performance, they cannot acquire understandable knowledge and prediction performance can be further improved. Thereupon, the Rule Sets ENsemble (RSEN) algorithm, which takes advantage of diverse attribute and attribute value reduction algorithms based on rough set (RS) theory, is proposed as the initial trial to acquire understandable rules along with enhancement of prediction performance. Finally, the RSEN is applied to predict the peptides that bind to HLA-DR4(B1^* 0401). Experimentation results show: (1) prepositional rules for predicting the peptides that bind to HLA-DR4 (B1^* 0401) are obtained; (2) compared with individual RS-based algorithms, the RSEN has a significant decrease (13%–38%) in prediction error rate; (3) compared with the Back-Propagation Neural Networks (BPNN), prediction error rate of the RSEN decreases by 4%–16%. The acquired rules have been applied to help experts make molecules modeling. An Zeng received the Ph.D. degree in computer applications technology from South China University of Technology in 2005. Nowadays she is a lecturer at the Faculty of Computer of Guangdong University of Technology. Her research interests are data mining, bioinformatics, neural networks, artificial intelligence, and computational immunology. In these areas she has published over 20 technical papers in various prestigious journals or conference proceedings. She is a member of the IEEE. Contact her at the Faculty of Computer, Guangdong Univ. of Technology, University Town, PanYu District, Guangzhou, 510006, P.R. China. Dan Pan received the Ph.D. degree in circuits and systems from South China University of Technology in 2001. He is a senior engineer in Guangdong Mobile Communication Co. Ltd at present. His research interests are data mining, machine learning, bioinformatics, and data warehousing, and applications of business modeling and software engineering to computer-aided business operations systems, especially in the telecom industry. In these areas he has published over 30 technical papers in refereed journals or conference proceedings. As a member of the International Association of Science and Technology for Development (IASTED) technical committee on artificial intelligence and expert systems, he served a number of conferences and publications. He is a member of the IEEE. Contact him at Guangdong Mobile Communication Co. Ltd., 208 Yuexiu South Rd., Guangzhou, 510100, P.R. China. Jian-bin He received the M.E. in computer science from South China University of Technology in 2002. He now is a data mining consultant at Teradata division of NCR (China), supporting telecom carriers to do data mining in data warehouses for market research. His research interests include statistical learning, semi-supervised learning, spectral clustering, multi-relational data mining and their application to social science. Contact him at NCR(China) Co. Ltd., Unit 2306, Tower B, Center Plaza, 161 Linhexi Road, Guangzhou, 510620, P.R. China.