Analysis of Gene Expression Dataset of Genetic Disease Analysis of Autism Spectrum Disorder using Machine Learning and GEO2R tool - Sanjay Kumar, Saroj Ghadle, Rakesh Tripathi (International Journal of Advanced Science and Technology, 2020)
The task of analyzing the gene expression dataset of complex genetic diseases like Autism Spectrum Disorder, cancer, cardiovascular, respiratory diseases, human disease resistant genes(R-Genes)at early stage is an important task in the today’s research field. This paper focused on analysis of two gene expression datasets GSE109905 and GSE26415 related to a complex genetic disease called as Autism Spectrum Disorder. The datasets are taken from an authorized Government website, National Centre for Biotechnology Information (NCBI). The purpose of analysis is to understand significance of gene expression values and identify the differentially expressed genes from the dataset. The analysis is done using machine learning approach and GEO2R tool. The analysis using machine learning approach and GEO2R tool results into differentially expressed genes. The analysis of GEO dataset GSE109905 of genetic disease, Autism Spectrum Disorder ASD and control group cases is carried out using GEO2R tool and the differentially expressed genes were observed. Also, the analysis of GEO dataset GSE26415 using the machine learning approach, linear supervised leaning model is performed and found 84.96 percent accuracy. The result of both approaches can helps us to understand the health status of an organism by identifying the differentially expressed genes.