Glioma is the most common adult brain cancer and is very difficult to treat. Despite surgery, radiation, and chemotherapy, less than 10% of patients survive beyond five years. There is an unmet clinical need for early prognosis and effective therapeutics for gliomas. In recent years, interdisciplinary research approaches have helped solve many biological problems in human health and disease. We have used statistical models and survival analysis to develop a prognostic signature for lower-grade gliomas. Using the regression models, we developed a long non-coding RNA (lncRNA) gene expression-based model to predict survival in glioma patients. We also showed the role of novel lncRNAs, LINC00152 and DRAIC, in tumor progression and cancer patients' survival. The overarching goal of my research group at the University of Hyderabad is to utilize network-based measures in machine learning to identify genetic interactions in cancers. Sex bias in cancer occurrence, progression, and survival has been known from epidemiological and population studies. We use a multivariate cox-regression proportional hazard model to identify sex-biased prognostic genes in glioblastoma. Although more genes are associated with poor survival of male glioblastoma patients, we found that the genes regulated by PPARg activation increase the risk in female glioblastoma patients. My research interest lies in biomarker discovery for cancer prognosis utilizing machine learning and bigdata approaches. The prognosis and cancer treatment worldwide are primarily based on the caucasian population making it difficult even to validate the developed model on an independent dataset. There is an urgent need to join hands and collaborate with nearby nations to bring forward studies on other under-represented populations to understand the diversity and disparity among cancer patients. I am looking for possible collaboration with SCO countries with researchers interested in using artificial intelligence to predict tumor progression in glioma patients and initiate consortiums for treating our patients more effectively.