Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Inference of Gene Networks from Microarray Data through a Phenomic Approach(2010) D'Souza, R.G.L.; Chandra Sekaran, K.C.; Kandasamy, A.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.Item A multiobjective phenomic algorithm for inference of gene networks(2012) D'Souza, R.G.L.; Chandra Sekaran, K.C.; Kandasamy, A.Reconstruction of gene networks has become an important activity in Systems Biology. The potential for better methods of drug discovery and of disease diagnosis hinge upon our understanding of the interaction networks between the genes. Evolutionary methods are proving to be successful in such problems and a number of such methods have been proposed. However, all these methods are based on processing of genotypic information. We have presented an evolutionary algorithm for reconstructing gene networks from expression data using phenotypic interactions, thereby avoiding the need for an explicit objective function. Specifically, we have also extended the basic phenomic algorithm to perform multiobjective optimization for gene network reconstruction. We have applied this novel algorithm to the yeast sporulation dataset and validated it by comparing the results to the links found between genes of the yeast genome at the SGD database. © 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering.