Recent advances in machine learning such as deep learning have led to powerful tools for modeling complex data with high predictive accuracy. However, the resulting models are typically black box, limiting their usefulness in scientific discovery. I will describe an “interpretable-by-design” machine learning model capturing a fundamental cellular process known as RNA splicing. Our model provides a systematic understanding of RNA splicing logic, recapitulating and extending existing domain knowledge.

The talk will not assume any prior knowledge, and should be accessible to a broad audience.