COMPUTATIONAL ANALYSIS OF GENE PRIORITIZATION APPROACHES FOR SCHIZOPHRENIA

Abstract

Biological analysis of the genes helps in predicting the chemical process, but handling of data is very difficult because of their huge size, assorted natureand access time overheads. Disorders like schizophrenia can be diagnosed effectively by finding the most relevant genes which are highly associated with the disorder from the number of candidate genes. Traditional method for gene analysis includes single nucleotide polymorphism (SNP) finding, gene mutation analysis and many in-vitro techniques having many limitations like labor intensive, long- lasting, high cost, and inadequate knowledge of genetic materials. To address such issues computational methods are used which have many advantages over traditional method such as cost effective, time saving, pertinent testing as well as validating tools, ample of primary data etc.The current research emphasizes on computational methods for gene prioritization. Gene prioritization was done with the help of text mining approaches for schizophrenia disorder. Fortext mining Pubtator tool was used for gene prioritization. Pubtator prioritized nine genes (DISC1, COMT, NRG1, DTNBP1, DRD3, DAOA, RGS4, GRM3, and PRODH) which are associated with schizophrenia. Further in-vitro studies on these genes may reveal the actual cause behind the disorder.