Inference of Gene Regulatory Networks using S-System: A Unified Approach

被引：0
|
作者：
Wang, Haixin [1 ,2 ]
Qian, Lijun [1 ]
Dougherty, Edward [3 ,4 ]
机构：
[1] Prairie View A&M Univ, Dept Elect & Comp Engn, Prairie View, TX 77446 USA
[2] Fort Valley State Univ, Dept Math & Comp Sci, Fort Valley, GA 31030 USA
[3] Translat Genom Res Inst TGen, Comp Biol Div, Phoenix, AZ 85004 USA
[4] Texas A&M Univ, Dept Elect & Comp Engn, College Stn, TX 77843 USA
来源：
2007 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE IN BIOINFORMATICS AND COMPUTATIONAL BIOLOGY | 2007年
关键词：
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
In this paper, a unified approach to infer gene regulatory networks using the S-system model is proposed. In order to discover the structure of large-scale gene regulatory networks, a simplified S-system model is proposed that enables fast parameter estimation to determine the major gene interactions. If a detailed S-system model is desirable for a subset of genes, a two-step method is proposed where the range of the parameters will be determined first using Genetic Programming and Recursive Least Square estimation. Then the exact values of the parameters will be calculated using a multi-dimensional optimization algorithm. Both downhill simplex algorithm and modified Powell algorithm are tested for multi-dimensional optimization. Simulation results using both synthetic data and real microarray measurements demonstrate the effectiveness of the proposed methods.
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页码：82 / +
页数：2
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