Identification of gene interaction networks based on evolutionary computation

Authors:
Sung Hoon Jung;Kwang-Hyun Cho
Affiliations:
School of Information Engineering, Hansung University, Seoul, Korea;College of Medicine, Seoul National University, Seoul, Korea
Venue:
AIS'04 Proceedings of the 13th international conference on AI, Simulation, and Planning in High Autonomy Systems
Year:
2004

Citing 4
Cited 0

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Adaptation in natural and artificial systems
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Linear Modeling of Genetic Networks from Experimental Data

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Reconstructing gene networks from large scale gene expression data

Reconstructing gene networks from large scale gene expression data

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Abstract

This paper investigates applying a genetic algorithm and an evolutionary programming for identification of gene interaction networks from gene expression data. To this end, we employ recurrent neural networks to model gene interaction networks and make use of an artificial gene expression data set from literature to validate the proposed approach. We find that the proposed approach using the genetic algorithm and evolutionary programming can result in better parameter estimates compared with the other previous approach. We also find that any a priori knowledge such as zero relations between genes can further help the identification process whenever it is available.