Abstract: Several “omics” studies that are going on currently work efficiently mostly for Mendelian diseases in identification of markers that can be used for diagnosis and treatment. In case of complex diseases, there are no strong associations for a single factor, since these diseases are multifactorial. To this end, we developed a new method to analyze the omics data in a network related context to identify pathways that are involved in disease development mechanism. We employed several computational methods ranging from machine learning algorithms for function identification to network search algorithms in identification of pathway markers. These markers can be used to understand individual disease development mechanism to determine the individual targets for treatments bridging the gap between omics data and personalized medicine.