Prediction of a gene regulatory network from gene expression Profiles with Linear Regression and Pearson Correlation Coefficient

Abstract

Reconstruction of gene regulatory networks is the process of identifying gene dependency from gene expression profile through some computation techniques. In our human body, all cells contain same genetic material but the same genes may or may not be active. This variation in the activation of genes assists researchers to understand more about the function of the cells. Microarray technology helps researchers to get insight about many different diseases such as various cancer disease, heart disease, mental illness, and infectious disease, etc. In this study, a cancer-specific gene regulatory network has been constructed using a simple and novel machine learning approach. First, significant genes differentially expressing them self in the disease condition has been identified using linear regression algorithm. Next, regulatory relationships between the identified genes has been computed using Pearson correlation coefficient. Finally The obtained results has been validated with the available databases and literatures. We can identify the hub genes and can be targeted for the cancer diagnosis.