On the reconstruction of genetic network from partial microarray data

Authors:
Ahsan Raja Chowdhury;Madhu Chetty;Xuan Nguyen Vinh
Affiliations:
Gippsland School of Information Technology, Monash University, Australia;Gippsland School of Information Technology, Monash University, Australia;Gippsland School of Information Technology, Monash University, Australia
Venue:
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
Year:
2012

Citing 3
Cited 0

Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces

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Inference of S-system models of genetic networks using a cooperative coevolutionary algorithm

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Inferring Gene Regulatory Networks using Differential Evolution with Local Search Heuristics

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Abstract

Gene Regulatory Network (GRN) contains interactions occurring between transcription factors (TF) and target genes which are captured during the microarray creation. However, information about the interactions among microRNAs (miRNA) and target genes can not be captured by current microarray technology. To overcome this limitation, we propose a new technique to reverse engineer GRN from partial microarray data which represent target genes' interactions only. Using S-System model, the approach is modified to incorporate the unavailability of information about miRNA-target genes interactions. The most versatile Differential Evolutionary algorithm is used for optimization and parameter learning. Experimental studies on three newly created synthetic networks, and one real network of Saccharomycescerevisiae called IRMA network, show significant improvement compared to traditional S-System based approach.