Please use this identifier to cite or link to this item:
Full metadata record
|dc.contributor.author||Chandra, Sekaran, K.||-|
|dc.identifier.citation||Communications in Computer and Information Science, 2010, Vol.101, , pp.83-89||en_US|
|dc.description.abstract||The reconstruction of gene networks is crucial to the understanding of cellular processes which are studied in Systems Biology. The success of computational methods of drug discovery and disease diagnosis is dependent upon our understanding of the biological basis of the interaction networks between the genes. Better modelling of biological processes and powerful evolutionary methods are proving to be a key factor in the solution of such problems. However, most of these methods are based on processing of genotypic information. We present an evolutionary algorithm for inferring gene networks from expression data using phenotypic interactions. The benefit of this is that we avoid the need for an explicit objective function in the optimization process. In order to realize this, we have implemented a method called as the Phenomic algorithm and validated it for stability and accuracy in the reconstruction of gene networks. � Springer-Verlag Berlin Heidelberg 2010.||en_US|
|dc.title||Inference of Gene Networks from Microarray Data through a Phenomic Approach||en_US|
|Appears in Collections:||2. Conference Papers|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.