Yihang Bao

This paper introduces RLAgent, an LLM-based framework for RNA-ligand interaction modeling that enables interactive, code-free prediction at the RNA region level.

Memo-Patho predicts pathogenicity of transmembrane protein mutations using an alignment-free deep learning framework with contrastive pretraining, achieving high accuracy on benchmark datasets and manual curation.

This study benchmarks encoding strategies for non-coding SNPs across QTL prediction tasks, emphasizing interactions between encoding, models, and preprocessing.

This paper introduces Methven, a deep learning framework that predicts non-coding mutation effects on DNA methylation at single-cell resolution, showing superior performance in prioritizing functional mutations.

EMO predicts non-coding mutation-induced up- and down-regulation of gene expression using deep learning, achieving high performance across variant distances and cell types.

The paper uses a 3D deep learning model to find structural universality in TMPs interaction regions, achieving an MCC of 0.36.