Ye Yuan

DTMol: Pocket-based Molecular Docking using Diffusion Transformers

DTMol is a diffusion model for pocket-based molecular docking that outperforms existing methods on benchmark datasets and identifies new JAK2 inhibitors.

TSvelo: Comprehensive RNA velocity by modeling cascade of gene regulation, transcription and splicing

TSvelo improves RNA velocity analysis by modeling gene regulation, transcription, and splicing with neural ODEs, showing better performance on multi-lineage scRNA-seq datasets.

stFormer: a foundation model for spatial transcriptomics

stFormer is a transformer model for spatial transcriptomics data that improves performance on cell clustering and gene function prediction compared to existing models.

GexMolGen: Cross-modal Generation of Hit-like Molecules via Large Language Model Encoding of Gene Expression Signatures

GexMolGen generates hit-like molecules using gene expression signatures through a cross-modal approach, producing valid molecules from both in- and out-of-domain transcriptome data.

TFvelo: gene regulation inspired RNA velocity estimation

TFvelo improves RNA velocity estimation by incorporating gene regulatory information, achieving accurate dynamics modeling without relying on splicing data.

CINS: Cell Interaction Network inference from Single cell expression data

CINS infers differential cell-cell interactions between conditions using scRNA-Seq data and identifies the proteins involved in these interactions.

Deep learning of gene interactions from single cell time-course expression data

This paper develops deep learning methods to predict gene interactions from single-cell time-course RNA-Seq data, showing improved accuracy over existing methods.

Cell Type Assignments for Spatial Transcriptomics Data

The paper introduces FICT, a method that improves cell type assignment in spatial transcriptomics data by incorporating both gene expression and neighborhood information, showing better accuracy in identifying cell types and sub-types.

Reconstructed signaling and regulatory networks identify potential drugs for SARS-CoV-2 infection

The paper reconstructs signaling and regulatory networks for SARS-CoV-2 infection and identifies potential drug targets by integrating multi-omics data and experimental validation.

GCNG: Graph convolutional networks for inferring cell-cell interactions

GCNG uses graph convolutional networks to infer cell-cell gene interactions from spatial transcriptomics data, improving upon existing methods and enabling downstream functional analysis.

Deep learning for inferring gene relationships from single-cell expression data

The paper introduces a deep learning framework, CNNC, for inferring gene relationships from single-cell expression data, showing improvements over existing methods in various tasks.

Quantitative understanding of molecular competition as a hidden layer of gene regulatory network

This study introduces a competition motif model to analyze molecular competition's role in gene regulatory networks, revealing its impact on dose-response curves, response speed, noise, and target correlations.