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An Evolutionary Data Mining Algorithm to Find the Candidate Gene for Disease Diagnostics 
Friday, 12 September 2014,  3:00 -  5:00
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Speaker: Narayan Behera

Computational biophysics program

Haramaya University, Ethiopia &

Institute of Bioinformatics and Applied

Biotechnology, Bangalore

Date: Friday, Sep 12, 2014

Time: 3 p.m.

Venue: Conference Hall, C-MMACS New Bldg.

 

Abstract

Identification of genes responsible for a cancer is a frontier area of research in bioinformatics. Microarray samples contain information about the gene expression levels of thousands of genes. Clustering similar genes and selecting the top ranking genes help in identifying the key genes that play pivotal roles in specified biological conditions for diseased states. Today many algorithms exist for extracting this information but all have inherent limitations. In this research a novel algorithm has been proposed for gene clustering, feature selection and classification of test microarray samples with higher accuracy. The algorithm is a hybrid of clustering algortihm and evolutionary computation. The evolutionary computation uses an algorithm that utilizes the genetic principles of evolution to solve an optimization problem. The genetic distance measure employed here is based on mutual information which takes into account similarity of the gene expression levels as well as positive and negative correlations between the genes. A study on the gastric cancer, colon cancer and brain cancer microarray gene expression datasets and comparison with some existing algorithms show the present algorithm to be superior. It is used to find explicitly the top ranking genes that contain the most diagnostic information for gastric cancer. The present algorithm has potential applications in the computational drug discovery process.

Location Conference Hall, C-MMACS New Bldg.
Contact Dr. Krishnamohan

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