Yu Xu

TRAFICA: An Open Chromatin Language Model to Improve Transcription Factor Binding Affinity Prediction

Sat, 01 Nov 2025 00:00:00 +0000

Supplementary: source code, model weights and training/evaluation data.

Causal Transformer for Learning Embeddings from Structured Medical History Records and Multi-Source Data Integration for Complex Disease Risk Prediction

Wed, 17 Sep 2025 00:00:00 +0000

Supplementary notes can be added here, including code and data when available.

Mitigation of Multi-scale Biases in Cell-type Deconvolution for Spatially Resolved Transcriptomics Using HarmoDecon

Mon, 01 Sep 2025 00:00:00 +0000

Supplementary notes can be added here, including code and data when available.

stDyer enables spatial domain clustering with dynamic graph embedding

Thu, 20 Feb 2025 00:00:00 +0000

Overview

stDyer is an innovative deep learning framework designed for spatial domain clustering in spatially resolved transcriptomics (SRT) data. The framework integrates:

Gaussian Mixture Variational AutoEncoder (GM-VAE) for learning meaningful embeddings
Graph Attention Networks (GAT) for capturing spatial relationships
Dynamic Graph Construction that adaptively links spatial units based on Gaussian Mixture assignments
Mini-batch Strategy and Multi-GPU Support for handling large-scale datasets

Key Features

1. Dynamic Graph Embedding

Adaptively constructs graphs based on clustering assignments
Produces smoother spatial domain boundaries
Captures both transcriptomic similarity and spatial proximity

2. Scalability

Mini-batch processing strategy
Multi-GPU support for large datasets
Efficient handling of high-resolution SRT data

3. Superior Performance

Outperforms state-of-the-art tools in spatial domain clustering
Excellent performance in multi-slice analysis
Robust handling of large-scale datasets

Citation

If you use stDyer in your research, please cite:

@article{xu2025stdyer,
 title={stDyer enables spatial domain clustering with dynamic graph embedding},
 author={Xu, Ke and Xu, Yu and Wang, Zirui and Zhou, Xin Maizie and Zhang, Lu},
 journal={Genome Biology},
 volume={26},
 number={1},
 pages={34},
 year={2025},
 publisher={BioMed Central},
 doi={10.1186/s13059-025-03503-y}
}

Med-PRSIMD: Enhanced Complex Disease Risk Prediction through Integrative Analysis of Multi-Type Data and Medical History Records

Fri, 22 Nov 2024 00:00:00 +0000

Supplementary notes can be added here, including code and data when available.

A machine learning model for disease risk prediction by integrating genetic and non-genetic factors

Thu, 08 Dec 2022 00:00:00 +0000

Supplementary notes can be added here, including code and data.

dynDeepDRIM: a dynamic deep learning model to infer direct regulatory interactions using time-course single-cell gene expression data

Thu, 01 Sep 2022 00:00:00 +0000

Supplementary materials, including code and data.