GRNInfer aims to derive the most consistent network structure with respect to Multiple Microarray Datasets, by exploiting available information from a variety of experiments. Specifically, inferring gene network is formulated as an optimization problem with minimization of L_1 norm for the objective function, which involves both forced matching and sparse terms. An efficient algorithm is developed to solve such a large-scale linear programming in an iterative manner. With such a procedure, a consistent and sparse structure that is also considered to be biologically plausible, can be expected to be derived.

Contact

If you have any comments or suggestion, please contact:

Dr. Luonan Chen
Department of Electrical Engineering and Electronics
Osaka Sangyo University
Email: chen@elec.osaka-sandai.ac.jp


Dr.Dong Xu
Digital Biology Laboratory
Computer Science Department and Christopher S. Bond Life Sciences Center
University of Missouri Columbia
Email: xudong@missouri.edu



Also available at http://zhangroup.aporc.org/bioinfo/grninfer