Dissimilar Regulatory Actions Between Neurodegenerative Disease Pairs Through Probablistic Differential Correlation

Rigorous research on gene expression analysis comparing the expression samples over normal and diseased states has helped us in understanding the pattern of various diseases. To gain better insight some state of the art methodologies goes a step beyond through exploration of differential co-expression patterns, where the co-expression level between genes alters across different states. In our work we have utilized a recently published concept which frames up a probabilistic score for differential correlation to detect differentially co-expressed genes modules. Our contribution lies in implementing the probabilistic differential correlation score for the identification of significantly paired gene networks possessing extremely dissimilar regulatory actions across multiple diseases. This has been tested upon differential gene expression profiles of genes present in a set of neurodegenerative diseases. Our investigation gets more interesting testing the impact of not only common (among multiple disease sets) differentially expressed genes but the revealing role of mutually exclusive disease specific gene sets in the spreading of diseases. Hence, we can explore through the fruitful aspects of our work scrutinizing the disease specific problem.