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, and extract
new knowledge from the diverse available data to improve the mechanistic
understanding of diseases and patient care. To uncover molecular mechanisms and
drug indications for specific cancer types, we develop an integrative framework
able to harness a wide range of diverse molecular and pan-cancer data. We show
that our approach outperforms competing methods and can identify new
associations. Furthermore, through the joint integration of data sources, our
framework can also uncover links between cancer types and molecular entities
for which no prior knowledge is available. Our new framework is flexible and
can be easily reformulated to study any biomedical problems.Comment: 18 page
