Evolutionary divide-and-conquer approach to inferring S-system models of genetic networks

Shinn-Ying Ho*, Chih Hung Hsieh, Fu Chieh Yu, Hui Ling Huang

*Corresponding author for this work

研究成果: Conference contribution同行評審

2 引文 斯高帕斯(Scopus)

摘要

This paper proposes an efficient evolutionary divide-and-conquer approach (EDACA) to inferring S-system models of genetic networks from time-series data of gene expression. Inference of an S-system model has 2N(N+1) parameters to be optimized where N is the number of genes in a genetic network. To cope with higher dimensionality, the proposed approach consists of two stages where each uses a divide-and-conquer strategy. The optimization problem is first decomposed into N subproblems having 2(N+1) parameters each. At the first stage, each subproblem is solved using a novel intelligent genetic algorithm (IGA) with intelligent crossover based on orthogonal experimental design (OED). The intelligent crossover divides two parents into n pairs of parameter groups, economically identifies the potentially better one of two groups of each pair, and systematically obtains a potentially good approximation to the best one of all 2n combinations using at most 2n function evaluations. At the second stage, the obtained N solutions to the N subproblems are combined and refined using an OED-based simulated annealing algorithm (OSA) for handling noisy gene expression data. The effectiveness of EDACA is evaluated using simulated expression patterns with/without noise running on a single-CPU PC. It is shown that: 1) IGA is efficient enough to solve subproblems; 2) IGA is significantly superior to the existing method of using GA with simplex crossover; and 3) EDACA performs well in inferring S-system models of genetic networks from small-noise gene expression data.

原文English
主出版物標題2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings
頁面691-698
頁數8
DOIs
出版狀態Published - 31 十月 2005
事件2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005 - Edinburgh, Scotland, United Kingdom
持續時間: 2 九月 20055 九月 2005

出版系列

名字2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings
1

Conference

Conference2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005
國家United Kingdom
城市Edinburgh, Scotland
期間2/09/055/09/05

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