Xiao Wang

DMcloud improves fitting of molecular models into cryo-EM maps by local structure fitting, achieving higher accuracy and coverage than existing methods.

HiCFoundation is a Hi-C-based model that integrates chromatin structure with epigenomic regulation across species, improving analysis of 3D genome architecture and single-cell data.

TwinC predicts trans-chromosomal DNA contacts using DNA sequence and provides insights into their functional mechanisms.

Puget predicts gene expression across cell types using sequence and Hi-C data, improving generalization to new biosamples and species.

NuFold predicts RNA tertiary structures using deep learning with a nucleobase center representation to bridge the gap between sequence data and structural insights.

CryoNeRF improves cryo-EM structure reconstruction by handling both homogeneous and heterogeneous data, achieving better performance than existing methods.

DiffModeler uses diffusion models to accurately model protein complex structures from low-resolution cryo-EM maps, achieving high TM-Scores across various resolutions.

The paper introduces DAQ-refine, a protocol that improves protein models from cryo-EM maps using a deep learning score and modified AlphaFold2 refinement, showing consistent improvements over existing methods.

OC_Finder is a deep learning tool that automatically segments, counts, and classifies osteoclasts in microscopic images with high accuracy comparable to manual methods.

The paper introduces GNN-DOVE, a deep learning model for evaluating protein docking models, showing improved performance over existing methods.