C03 Machine Learning Single-Cell Cancer Immunotherapy Competition

Offered under: 6.S099
Term(s): IAP only
Level: Undergraduate
Units: 6 (P/D/F)
Prerequisite: Programming (e.g., Python) at the level of 6.1010. Recommended: 6.3720, 6.3900, or 6.3730[J]/IDS.012[J]. No background in biology required.
Instructors: Caroline Uhler (EECS), Paul Blainey (Biological Engineering), Jonathan Weissman (Biology)

The future of cancer care is immunotherapy — using our body’s immune system to eliminate tumors. While T cells, our immune system’s fighter cells, should recognize and kill growing tumors, cancer cells send signals to T cells that cause these fighter cells to malfunction. But what if we could modify T cells to make them better at killing cancer cells? In this class, students will participate in a global cancer immunotherapy data science challenge and apply their machine learning skills to help solve this key biological problem. The challenge is being run by the Eric and Wendy Schmidt Center at the Broad Institute of MIT and Harvard. Students will learn the basics of cancer biology, single-cell sequencing technology, and data analysis needed to succeed in the challenge. Top-scoring submissions will be validated in a lab at the Broad Institute, and winners will be eligible for monetary prizes totaling $50,000 and paper authorship.