Integrative Data Analytic Framework to Enhance Cancer Precision Medicine

With the advancement of high-throughput biotechnologies, we increasingly accumulate biomedical data about diseases, especially cancer. There is a need for computational models and methods to sift through, integrate, extract new knowledge from diverse available data, improve mechanistic understanding diseases patient care. To uncover molecular mechanisms drug indications specific cancer types, develop an integrative framework able harness wide range pan-cancer data. We show that our approach outperforms competing can identify associations. Furthermore, it captures underlying biology predictive response. Through joint integration sources, also links between types entities which no prior available. Our flexible be easily reformulated study any problem.