Protein-RNA binding, mediated through both RNA sequence and structure, plays a vital role in many cellular processes, including neuro-degenerative diseases. Modeling the sequence and structure binding preferences of an RNA-binding protein is a key computational challenge. Accurate models will enable prediction of new interactions and a better understanding of the binding mechanism.
In this talk, I will describe a new deep learning based approach to learn RNA sequence and structure binding preferences from large biological datasets. I will present the results of our algorithm outperforming the state of the art, both in vitro and in vivo. I will give examples of the biological insights we can gain by applying our neural networks to larger datasets of protein-RNA interactions. I will conclude with open questions and a discussion on the success of deep learning in computational biology.
No biological background is assumed or required for the purpose of the talk.