GENOME WIDE INTEGRATION ANALYSIS - A NEW APPROACH TO PREDICT SUSCEPTIBLE GENE(S) FOR LUNG CANCER
Muthusamy Chinnasamy and Thirunalasundari Thiyagarajan*
Genome wide integration study (GWIS) is the new branch of science that deals with complete genome of the cell type with a specific condition. It is a comprehensive approach to test the hypothesis that, multiple genes work together and account for disease. Diagnosis of the disease at early stage is essential to cure the cancer. Methods available for cancer diagnosis as of now are all lab based and they are time bound and economically ineffective. Hence, a comprehensive analytical method is an urgent need of the hour to diagnose cancer. This study was aimed to find out a new approach for the detection of susceptible gene(s) for cancer using tensor algorithm. Three datasets were downloaded from GEO database. They are i) GSE18454- normal lung cell (NLC), ii) GDS1204 - lung cancer cell (LCC) and iii) GDS1204 -lung cancer cell treated with Mgd (LCCTD). Pre-processing of the data leads to 7,000 valid genes that are similar in all these 3 datasets. Tensor algorithm was implemented to analyze the data. Results revealed that 93% of genes were classified in to different categories, and the rest 7% genes were insignificant. Of the 93% genes classifiable56% of input genes are down regulated after treatment, 4% of genes are up regulated. Mgd treatment restores the genes expression successfully in 4%. 29% of genes were found to be further up-regulated after treatment. As an outcome a tool, will be provided to medical personale and using that they can analyse genomic data and predict lung cancer gene target(s).
Keywords: Lung cancer, Tensor, Genome Wide Analysis (GWA), PHP, MySQL, Mgd.
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