An Information and Combinatorial Theories-based Supervised Learning Framework for Integrative Inference and Analysis of Genetic Regulatory Networks

摘要：

A supervised learning framework based on information and combinatorial theories is introduced for the inference and analysis of genetic regulatory networks. First, an associativity measure is proposed to quantify the regulatory strength. Next, a phase-shift metric is defined for detecting regulatory orientations among network components. Thus, this framework can solve undirected problems from most current linear/nonlinear relevance methods. For computational redundancy, the size of the classified pair candidates is constrained within a multiobjective combinatorial optimization problem. In comparison with previously reported methods, our flexible approach can be used to efficiently identify a directed biological network that is verified by both synthetiC and real-world microarray datasets having different statistical characteristics. Thus, the underlying network-designing mechanisms are deciphered by qualitative and quantitative means.

关键词： Information theory Signal processing Combinatorial optimization Genetic regulatory network

作者: Binhua Tang Xuechen Wu Ge Tan Su-Shing Chen Qing Jing Bairong Shen

作者单位: Department of Bioinformatics,Tongji University.Shanghai,China Institute of Protein Research,Tongji University,Shanghai,China CAS-MPG Partner Institute of Computational Biology,Shanghai,China Institute of Health Sciences,Shanghai Institutes for Biological Sciences,Chinese Academy of Sciences Center for Systems Biology,Soochow University,Suzhou,China

会议类型: 国际会议

会议名称: The 3rd International Symposium on Optimization and System Biology(第三届最优化与系统生物学国际会议 OSB09)

会议地点: 张家界

会议语种:英文

页码: 387-401

在线出版日期: 2009-09-20（万方平台首次上网日期，不代表论文的发表时间）