校聘副教授 / 硕士生导师 / 计算机科学与技术系副主任
School of Computer Science and Engineering, Hunan University of Science and Technology
中文为主 | English below
我目前任职于 湖南科技大学计算机科学与工程学院，主要从事 生物信息学、AI4Science、单细胞组学、多组学融合与图表示学习 相关研究。
我的研究兴趣集中在：如何利用机器学习、深度学习和图神经网络，从单细胞和空间组学数据中建模细胞状态、细胞异质性、基因调控网络与复杂疾病机制。
I am an Associate Professor at Hunan University of Science and Technology. My research focuses on bioinformatics, AI for single-cell omics, multi-omics integration, graph learning, and computational modeling of cells.
- 单细胞组学分析：scRNA-seq 聚类、细胞类型注释、稀有细胞识别、扰动效应预测
- 空间转录组分析：空间域识别、组织结构建模、图对比学习
- 多组学与多模态融合：paired/unpaired single-cell multi-omics integration
- 基因调控网络推断：GRN reconstruction, gene regulatory relationship modeling
- AI4Science 工具开发：面向生物医学数据的可复现算法与分析平台
Research keywords: Bioinformatics, Single-cell Omics, Spatial Transcriptomics, Graph Neural Networks, Contrastive Learning, Multi-omics Integration, Gene Regulatory Networks.
| 项目 | 简介 | 技术/方向 |
|---|---|---|
| scGSI | Graph-guided self-supervised integration of paired single-cell multi-omics | Python, single-cell multi-omics |
| scCMA | Contrastive masked autoencoder for single-cell RNA-seq clustering / embedding | Python, scRNA-seq, contrastive learning |
| Loc-PCA-CMI | A method for gene regulatory network structure inference | MATLAB, GRN inference |
| DoRC | Rare cell discovery from ultra-large scRNA-seq data | Python, rare cell discovery |
| miRTMC | miRNA target prediction based on matrix completion | JavaScript / bioinformatics |
| BioDashboard | Research group dashboard and visualization platform | JavaScript, dashboard |
我们聚焦 AI4Science，将深度学习与单细胞、空间组学等测序数据结合，致力于对细胞进行数字化建模与分析，揭示细胞功能复杂性和疾病异常机制。
主要问题包括：
- 如何从单细胞中提取多组学、多模态生物信息？
- 如何构建可解释的细胞计算模型？
- 如何利用 AI 方法理解细胞发育、分化、癌症机制和药物响应？
Our group aims to build computational models of cells by integrating deep learning with single-cell and spatial omics data, supporting biological discovery and precision medicine.
- scMMGC: A generative multi-omics integration method based on dual-mask contrastive fusion of co-expression graph, ISBRA 2026.
- scCMA: A Contrastive Masked Autoencoder Framework for Robust Representation Learning of scRNA-seq Data, Interdisciplinary Sciences: Computational Life Sciences, 2026.
- spGCLF: A versatile deep graph contrastive learning framework for spatial transcriptomics analysis, IEEE BIBM 2024.
- A deep graph convolution network with attention for clustering scRNA-seq data, IEEE BIBM 2023.
- DoRC: Discovery of rare cells from ultra-large scRNA-seq data, IEEE BIBM 2019.
- D3GRN / BiXGBoost / Loc-PCA-CMI: Gene regulatory network inference and network reconstruction methods.
More publications: Research Homepage
我承担本科生课程：
- 高级 Web 技术
- 大数据存储技术
- 面向对象程序设计
Academic service:
- 国家自然科学基金项目通讯评审专家
- IEEE BIBM、ACM-BCB、ISBRA 等会议 PC Member
- Briefings in Bioinformatics、Bioinformatics、PLOS Computational Biology、Big Data Mining and Analytics 等期刊审稿人
欢迎对 生物信息学、单细胞组学、空间转录组、图神经网络、多组学融合、AI4Science 感兴趣的同学和合作者联系。
如果你希望加入课题组，请尽量在邮件中说明：
- 做过哪些完整的课程项目、比赛项目或科研项目？
- 主要负责哪部分工作？
- 是否有代码仓库、论文、报告或可展示的结果？
- 对未来研究方向是否已有初步想法？
Contact: chenxofhit@gmail.com
Homepage: https://chenxofhit.xyz