Drug Dosage Balancing Using Large Scale Multi-omics Datasets

Cancer is a disease of biological and cell cycle processes, driven by dosage of the limited set of drugs, resistance, mutations, and side effects. The identification of such limited set of drugs and their targets, pathways, and effects based on large scale multi-omics, multidimensional datasets is one of key challenging tasks in data-driven cancer genomics. This paper demonstrates the use of public databases associated with Drug-Target(Gene/Protein)-Disease to dissect the in-depth analysis of approved cancer drugs, their genetic associations, their pathways to establish a dosage balancing mechanism. This paper will also help to understand cancer as a disease associated pathways and effect of drug treatment on the cancer cells. We employ the Semantic Web approach to provide an integrated knowledge discovery process and the network of integrated datasets. The approach is employed to sustain the biological questions involving 1) Associated drugs and their omics signature, 2) Identification of gene association with integrated Drug-Target databases 3) Mutations, variants, and alterations from these targets 4) Their PPI Interactions and associated oncogenic pathways 5) Associated biological process aligned with these mutations and pathways to identify IC-50 level of each drug along-with adverse events and alternate indications. In principal this large semantically integrated database of around 30 databases will serve as Semantic Linked Association Prediction in drug discovery to explore and expand the dosage balancing and drug re-purposing.