Yu Wang

This study evaluates 11 genomic language models and finds they offer limited advantages over a random baseline in regulatory decoding tasks.

MuCST integrates heterogeneous morphology images and spatial transcriptomics data using contrastive learning, identifying tissue subpopulations and tumor domains with high accuracy.

This paper introduces SVEN, a hybrid sequence-oriented model that predicts tissue-specific transcriptomic impacts of structural variants across 200+ tissues and cell lines.

REVA is a curated database of over 11.8 million noncoding variants with expression-modulating potential, providing functional annotations and benchmarking computational tools for variant analysis.