Inference of S-system models for large-scale genetic networks

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

This study proposes an efficient evolutionary algorithm, Intelligent Genetic Algorithm (IGA), for Inference of S-system models of large-scale genetic networks from the observed time-series data of gene expression patterns. High performance of IGA mainly arises from an intelligent crossover operation which applies orthogonal experimental design to speed up the search by using a systematic reasoning method instead of the conventional generate-and-go method. The proposed intelligent crossover employs a divide-and-conquer technique to cope with the problem of a large number of S-system parameters. The effectiveness of IGA is evaluated using simulated expression patterns. The proposed IGA with an existing problem decomposition strategy can efficiently cope with the inference problem of S-system models with several dozen genes to significant accuracy using a singleCPU personal computer.

Original languageEnglish
Title of host publicationProceedings - International Workshop on Biomedical Data Engineering, BMDE2005
DOIs
StatePublished - 1 Dec 2005
Event21st International Conference on Data Engineering Workshops 2005 - Tokyo, Japan
Duration: 3 Apr 20054 Apr 2005

Publication series

NameProceedings - International Workshop on Biomedical Data Engineering, BMDE2005
Volume2005

Conference

Conference21st International Conference on Data Engineering Workshops 2005
CountryJapan
CityTokyo
Period3/04/054/04/05

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