Evolutionary Modeling and Inference of Genetic Network

Shin Ando, Erina Sakamoto, Hitoshi Iba

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

We have modeled relations of genetic regulatory network in a form of ordinary differential equations using the Evolutionary Modeling method. The model was derived by hybrid algorithm of Genetic Programming with statistical analysis. It models the gene expression profile as a general polynomial function of other gene's expression. The method of genetic programming and Least Mean Square method were combined to identify the regulatory pathways and its degree of influence from the given time series in a concise form. The results of multiple runs were statistically analyzed to indicate the term with robust and significant influence. Our approach was applied and evaluated to artificial data. The method has also been applied to a publicly available gene expression profile.

Original languageEnglish
Title of host publicationProceedings of the 6th Joint Conference on Information Sciences, JCIS 2002
EditorsJ.H. Caulfield, S.H. Chen, H.D. Cheng, R. Duro, J.H. Caufield, S.H. Chen, H.D. Cheng, R. Duro, V. Honavar
Pages1249-1256
Number of pages8
Publication statusPublished - 1 Dec 2002
EventProceedings of the 6th Joint Conference on Information Sciences, JCIS 2002 - Research Triange Park, NC, United States
Duration: 8 Mar 200213 Mar 2002

Publication series

NameProceedings of the Joint Conference on Information Sciences
Volume6

Conference

ConferenceProceedings of the 6th Joint Conference on Information Sciences, JCIS 2002
CountryUnited States
CityResearch Triange Park, NC
Period8/03/0213/03/02

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Ando, S., Sakamoto, E., & Iba, H. (2002). Evolutionary Modeling and Inference of Genetic Network. In J. H. Caulfield, S. H. Chen, H. D. Cheng, R. Duro, J. H. Caufield, S. H. Chen, H. D. Cheng, R. Duro, ... V. Honavar (Eds.), Proceedings of the 6th Joint Conference on Information Sciences, JCIS 2002 (pp. 1249-1256). (Proceedings of the Joint Conference on Information Sciences; Vol. 6).