  Reconstruction of gene regulatory networks is the process of identifying gene
dependency from gene expression profile through some computation techniques. In
our human body, though all cells pose similar genetic material but the
activation state may vary. This variation in the activation of genes helps
researchers to understand more about the function of the cells. Researchers get
insight about diseases like mental illness, infectious disease, cancer disease
and heart disease from microarray technology, etc. In this study, a
cancer-specific gene regulatory network has been constructed using a simple and
novel machine learning approach. In First Step, linear regression algorithm
provided us the significant genes those expressed themselves differently. Next,
regulatory relationships between the identified genes has been computed using
Pearson correlation coefficient. Finally, the obtained results have been
validated with the available databases and literatures. We can identify the hub
genes and can be targeted for the cancer diagnosis.
