Prioritizing disease biomarkers using functional module based network analysis: A multilayer consensus driven scheme

Many complex diseases occur due to genetic factors. A perturbation in the pathway of gene interactions leads to such disorders. Even though a group of genes is responsible, a few significant genes act as a biomarker for disease, perturbing the healthy network. Identifying such marker genes or a set of genes that play a pivotal role in diseases helps drug prioritization. We propose a scheme for finding potential bio-markers using a multi-layer consensus-driven approach. We reconstruct a functional module guided disease sub-network, followed by a multi-step consensus of network inference methods and shared ontological terms. We perform centrality analysis on the sub-networks under consideration and report hub genes as potentially key players in the target disease. To establish our scheme's effectiveness, we use Alzheimer's Disease (AD) and Breast Cancer as candidate diseases for experimentation. We evaluate the significance of prioritized genes based on reported evidence. We observe that BRCA1, BRCA2, and PTEN are the essential genes for Breast Cancer, whereas MAPK1, APP, and CASP7 are the essential genes playing an important role during AD.