Developing computer systems that can recognize and locate image features associated with disease is a challenge for developing fully-automated and high precision computer assisted diagnostics. Joint learning of language tasks in association with vision tasks (association of image features with text annotation) adds an additional level of challenge.  Furthermore, scaling-up approaches from small to large datasets presents additional issues, particularly related to medical images.

The proto-oncogene c-Myc is deregulated and overexpressed in ~70% of all cancers. Thus, c-Myc is an attractive therapeutic target since disrupting c-Myc activity could be used as pan-chemotherapy. Beyond cancer, Myc is also a positive effector of tissue inflammation, and its function has been implicated in the pathophysiology of heart failure. Because c-Myc is a transcription factor, a rationally designed small molecule targeting c-Myc would be required to exhibit significant specificity.