Ke Chen

This paper introduces BPfold, a deep learning method for RNA secondary structure prediction that improves accuracy and generalizability by incorporating base pair motif energy derived from tertiary structure modeling.

This paper introduces an RNA language model based on multiple sequence alignment, showing it can accurately predict RNA structure from sequence data.

This study evaluates deep learning models for RNA base-pairing prediction, showing that SPOT-RNA can accurately predict RNA-RNA interactions in unseen structures and without monomeric structures.

The paper describes an RNA tertiary structure modeling method used in CASP15, combining secondary structure information, motif prediction, and the BRiQ potential for energy optimization.

StressME integrates E. coli's metabolic and gene expression responses to thermal, oxidative, and acid stress, revealing cross-stress proteome allocation strategies and trade-offs.

The paper builds a large RNA sequence database (MARS) and integrates it with RNAcmap to improve homology search and RNA structure/function inference.