Chaitanya K. Joshi

This paper demonstrates that deep learning can accurately design RNA pseudoknots, achieving results comparable to human experts in a competition.

The paper describes a Kaggle competition for RNA 3D structure prediction, showing that template-based methods can outperform deep learning approaches.

This paper introduces gRNAde, an RNA language model for inverse design of RNA structure and function, achieving success rates comparable to human experts in designing pseudoknotted RNAs.

gRNAde designs RNA sequences based on 3D backbone structures using geometric deep learning, achieving higher native sequence recovery than Rosetta on benchmark tasks.

HYFA improves multi-tissue gene expression imputation by leveraging hypergraphs and transfer learning, outperforming existing methods and enabling cell-type signature resolution from bulk data.