NYU Researchers Unveil Machine Learning Model to Decode RNA Splicing Logic

In a recent groundbreaking research titled "Deciphering RNA splicing logic with interpretable machine learning," authors Susan E. Liao, Mukund Sudarshan, and Oded Regev from NYU's Department of Computer Science, Courant Institute of Mathematical Sciences, unveiled an innovative neural network model. This "interpretable-by-design" model offers invaluable insights into the complexities of RNA splicing.

Significantly bolstered by NYU Information Technology High Performance Computing resources, the model not only boasts exemplary predictive accuracy but also introduces a pioneering visualization technique. This methodology allows researchers to transparently trace the decision-making process from the input sequence straight through to the splicing prediction. 

Read the full research article: Deciphering RNA splicing logic with interpretable machine learning