Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Towards Inferring Protein Interactions: Challenges and Solutions | Hindawi Publishing Corporation EURASIP Journal on Applied Signal Processing Volume 2006 Article ID 37349 Pages 1-14 DOI ASP 2006 37349 Towards Inferring Protein Interactions Challenges and Solutions Ya Zhang 1 2 Hongyuan Zha 3 Chao-Hsien Chu 4 and Xiang Ji5 1 Information and Telecommunication Technology Center The University of Kansas Lawrence KS 66045 USA 2 Department of Electrical Engineering and Computer Science The University of Kansas Lawrence KS 66045 USA 3 Department of Computer Science and Engineering School of Engineering Pennsylvania State University University Park PA 16802 USA 4 College of Information Sciences and Technology Pennsylvania State University University Park PA 16802-6823 USA 5NEC Laboratories America Inc. Cupertino CA 95014 USA Received 1 May 2005 Revised 13 October 2005 Accepted 15 December 2005 Discovering interacting proteins has been an essential part of functional genomics. However existing experimental techniques only uncover a small portion of any interactome. Furthermore these data often have a very high false rate. By conceptualizing the interactions at domain level we provide a more abstract representation of interactome which also facilitates the discovery of unobserved protein-protein interactions. Although several domain-based approaches have been proposed to predict protein-protein interactions they usually assume that domain interactions are independent on each other for the convenience of computational modeling. A new framework to predict protein interactions is proposed in this paper where no assumption is made about domain interactions. Protein interactions may be the result of multiple domain interactions which are dependent on each other. A conjunctive norm form representation is used to capture the relationships between protein interactions and domain interactions. The problem of interaction inference is then modeled as a constraint satisfiability problem and solved via linear programing. Experimental results on
