Cancer research today runs on computation as much as it runs on the bench. Identifying a disease-relevant gene, proving its clinical relevance, and finding a molecule that can act on it now depends on the same pipeline used in leading labs and published in leading journals — public expression databases, network biology, virtual screening, and molecular simulation.

This course walks participants through that entire pipeline, start to finish. It begins with the genetic and molecular basis of cancer, moves through gene expression and hub gene analysis, continues into multi-target computational drug discovery using natural products, and ends with a manuscript formatted and ready for journal submission.

The course is project-centric. From the early classes, every participant (or group) selects a cancer type and dataset, and applies each technique to that project in real time. By Class 20, every participant has been through the full translational research workflow once, hands-on, with their own results to show for it.