Utkarsh Upadhyay

This paper introduces RNA-MDLM, a model for generating and inpainting non-coding RNA sequences, showing it produces sequences with natural composition and folding statistics.

The paper evaluates how contact map topology affects RNA structure prediction and introduces a Gaussian score to assess contact distribution quality.

NucleicBERT is a self-supervised masked-language model for RNA that learns contextual representations without alignments or evolutionary data, outperforming existing models in structural and functional tasks.

NucleoSeeker curates high-quality RNA structure datasets by filtering the PDB database, improving dataset quality for training and evaluating RNA structure prediction methods.