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. Here we show that an "interpretable-by-design'' machine learning model captures a fundamental cellular process known as RNA splicing. Our model provides a systematic understanding of RNA splicing logic, recapitulating and extending on existing domain knowledge. It also allowed us to discover and experimentally validate novel splicing features.
This study highlights how interpretable machine learning can advance scientific discovery. The talk will not assume any prior biological knowledge.
Based on joint work with Susan E. Liao and Mukund Sudarshan.
Oded Regev is a Silver Professor in the Courant Institute of Mathematical Sciences of New York University. Prior to joining NYU, he was affiliated with Tel Aviv University and the École Normale Supérieure, Paris under the French National Center for Scientific Research (CNRS). He received his Ph.D. in computer science from Tel Aviv University in 2001 under the supervision of Yossi Azar. He is a recipient of the 2019 Simons Investigator award, the 2018 Gödel Prize, several best paper awards, and was a speaker at the 2022 International Congress of Mathematicians. His research areas include the geometry of numbers, RNA biology, machine learning, cryptography, and quantum computation.
- Program in Applied & Computational Mathematics
- Center for Statistics and Machine Learning