Yuhao Xia

SpaceTracer detects somatic SNVs from spatial transcriptomics data to enable lineage tracing in human tissues, revealing tumor progression and immune cell migration.

This paper introduces DeepSCFold, a method for improving protein complex structure prediction by using sequence-based deep learning to model inter-chain interactions, achieving higher accuracy than existing methods.

STARNet infers gene regulatory networks from spatial multi-omics data, revealing domain-specific regulatory interactions in tissues.

DeepAssembly2 improves protein complex structure prediction using domain-domain interactions and integrates new features and models to enhance accuracy.

This paper introduces FoldPAthreader, a method predicting protein folding pathways using a novel force field derived from evolutionary data, achieving 70% accuracy on 30 targets.

E2EDA improves multi-domain protein structure prediction by accurately assembling domains using deep learning, achieving higher accuracy and faster performance than existing methods.

Pathfinder predicts protein folding pathways by analyzing conformational sampling data, showing accurate predictions for some proteins and potential pathways for others.

This paper introduces DeepAssembly, a method for predicting multi-domain protein and complex structures using inter-domain interactions, achieving higher accuracy than AlphaFold2 on multi-domain targets.

This paper introduces DeepIDDP, a deep learning method for inter-domain distance prediction, which improves domain assembly accuracy when integrated with SADA.

This paper introduces PAthreader, a method for identifying remote homologous structures using deep learning, which improves template accuracy and enhances protein folding pathway exploration.

This paper introduces SADA, a deep learning-assisted method for assembling multi-domain protein structures, achieving higher accuracy than existing methods.

The paper introduces MPDB, a multi-domain protein structure database, and a server with modules for filtering and detecting structural analogues to aid in multi-domain protein modeling.

This paper introduces GDDfold, a distance-guided protein folding algorithm that outperforms existing methods on benchmark datasets.

This paper introduces SNfold, a sequential niche multimodal conformation sampling algorithm for protein structure prediction, which improves sampling efficiency and accuracy compared to existing methods.