Han Wen

MethylQUEEN is a DNA methylation foundation model that captures epigenetic information and enables tissue-of-origin tracing and disease detection through methylation patterns.

Uni-RNA, a deep learning model pre-trained on a large RNA sequence corpus, is reported to lead existing methods on structural and functional prediction tasks after fine-tuning.

The paper reports cryo-EM structures of NMDA receptors with GluN2C and GluN2D subunits, revealing structural and functional differences in their gating mechanisms.

UF-Symmetry improves prediction of large protein complexes by leveraging symmetry, achieving faster computation and better accuracy than existing methods.

RNANetMotif identifies sequence-structure RNA network motifs in RNA-protein binding sites using graph theory and validates them with molecular dynamics simulations.

PepNN introduces deep attention models for predicting peptide binding sites on proteins using sequence and structural data, achieving high performance on benchmark tests.