Correlation networks are ideal to describe the relationship between the expression profiles of genes. Gene expression is a characteristic exhibited by a particular gene. Our body has thousands of genes; each of them expresses differently, and each one of them has a particular function associated with them. When genes corresponding to a particular part of the body becomes non-functional, i.e., not expressed, then the function corresponding to that part of the body does not happen, thereby causing impairment or mutations. Co-regulation is a method involved in clustering analysis to find genes that perform similar functions. We want to identify genes that are co-regulated or expressed in concert to be able to identify defective cellular programs.
By understanding this co-regulation, different ways for the healthy development of a cell can be identified and even changes leading to disease can be detected. However, this concept is not yet fully applied due to reasons such as a lack of benchmarking studies that support the global acceptance of these networks, the volume of data available, and the presence of coincidental noise or extra inconsequential relationships.
In my project, I propose to explore the robustness of the gene expressions by comparing structural similarities of commonly developed networks using big data infrastructures. Further, I will work on forming a theory about the structure of correlation networks which supports their conceptual usability in biomedical big data. The proposed research will also provide an ideal software pipeline which can supply valid, reproducible and reliable correlation networks
