AI药物发现、单细胞多组学和数字病理团队

本研究团队现有教师5人，其中教授2人，博士4人，博士研究生1人，硕士研究生11人，长期致力于人工智能在生物医学大数据领域的研究。

（1）AI药物发现。药物异质性（Pharmacological Heterogeneity）与药物互作（Drug-Drug Interaction, DDI）是临床药物治疗中普遍存在的两大挑战，直接关系到患者疗效、安全性及医疗资源的优化配置。本团队利用AI、统计模型，结合药物组学、转录组、蛋白质等组学数据，开展药物响应预测、多组学整合驱动的异质性机制、药物互作的系统建模与风险预警、人工智能辅助个体化预测、药物重定位等研究。

（2）单细胞多组学

基于单细胞多组学研究解析肿瘤免疫微环境，是深入理解肿瘤发生发展及免疫治疗机制的关键。肿瘤免疫微环境是一个高度异质性的动态生态系统，由肿瘤细胞、免疫细胞、基质细胞等多种成分构成，其在肿瘤进展、免疫逃逸和治疗耐受中起着决定性作用。传统的批量测序技术会掩盖细胞间的异质性，而单细胞多组学技术（如单细胞转录组学、免疫组库测序、染色质可及性测序等）能够在单个细胞分辨率下，同时分析细胞的基因表达、T细胞或B细胞受体多样性、表观遗传等多维度信息，从而解析肿瘤免疫微环境中不同细胞亚群的组成、功能状态、细胞间相互作用及空间分布规律。应用该技术，本团队绘制了多种癌症免疫微环境精细图谱、解析微环境异质性，为开发新的免疫治疗生物标志物和联合治疗策略提供了重要线索。

（3）数字病理。数字病理（Digital Pathology）是传统病理学与信息技术深度融合的新兴交叉学科，通过将传统玻璃切片转化为高分辨率全玻片数字图像（Whole Slide Image, WSI），结合人工智能（AI）、大数据、云计算等技术，实现病理图像的数字化存储、传输、分析与挖掘。本团队在AI驱动的病理图像智能分析、多模态数据融合与生物标志物发现、临床转化与智慧医疗应用等方向开展研究。

本团队主持国家自然科学基金2项，省自然科学基金6项，发表SCI论文60余篇。

本团队招收生物学（生物物理学）博士研究生，智能科学与技术（学硕）和电子信息（专硕）硕士研究生，欢迎报考（lijin@muhn.edu.cn）。

代表性论著：

1.Li T#, Han H#, Chen J, Feng D, Chen Z, Wang X, Liu X, Zhang R, Wang Q, Li X, Li B*, Wang L*, Li J*. MK-NMF: a novel multiple kernel-based non-negative matrix factorization model to mini synergistic drug combinations in cell lines. Current Bioinformatics. 2025.

2.Liu X#, Feng D#, Chen J, Li T, Wang X, Zhang R, Chen J, Cai X, Han H, Yu L, Li X, Li B, Wang L*, Li J*. HCDT 2.0: A Highly Confident Drug-Target Database for Experimentally Validated Genes, RNAs, and Pathways. Sci Data. 2025 Apr 25;12(1):695. doi: 10.1038/s41597-025-04981-2.

3.Chen J, Han H, Li L, Chen Z, Liu X, Li T, Wang X, Wang Q, Zhang R, Feng D, Yu L, Li X, Wang L, Li B*, Li J*. Prediction of cancer cell line-specific synergistic drug combinations based on multi-omics data. PeerJ. 2025 Feb 25;13:e19078. doi: 10.7717/peerj.19078.

4.Zhang R, Chen Z, Li T, Feng D, Liu X, Wang X, Han H, Yu L, Li X, Li B*, Wang L*, Li J*. Enhancer RNA in cancer: identification, expression, resources, relationship with immunity, drugs, and prognosis. Brief Funct Genomics. 2025 Jan 15;24:elaf007. doi: 10.1093/bfgp/elaf007.

5.Guo S, Mohan GS, Wang B, Li T, Daver N, Zhao Y, Reville PK, Hao D, Abbas HA. Paired single-B-cell transcriptomics and receptor sequencing reveal activation states and clonal signatures that characterize B cells in acute myeloid leukemia. J Immunother Cancer. 2024 Feb 28.

6.Li T#, Guo S#, Xu C#, Zhang M, Lyu C, Xu H, Hou Z, Zhang M, Li X, Ren J, Liu C, Kong D, Hao D, Wang G. Integrated single-cell transcriptome and TCR profiles reveal interferon signaling convergence associated with immunotherapy response in Hepatocellular Carcinoma. J Immunother Cancer. 2024 Nov 24.

7.Guo S#, Li T#, Xu D#, Xu J, Wang H, Li J, Bi X, Cao M, Xu Z, Xia Q, Cui Y, Li K. Prognostic Implications and Immune Infiltration Characteristics of Chromosomal Instability-Related Dysregulated CeRNA in Lung Adenocarcinoma. Front Mol Biosci. 2022 Mar 28.

8.Chen J, Chen Z, Chen R, Feng D, Li T, Han H, Bi X, Wang Z, Li K, Li Y, Li X, Wang L*, Li J*. HCDT: an integrated highly confident drug-target resource. Database (Oxford). 2022 Nov 24;2022:baac101. doi: 10.1093/database/baac101.

9.Wang L, Xie W, Li K, Wang Z, Li X, Feng W*, Li J*. DysPIA: A Novel Dysregulated Pathway Identification Analysis Method. Front Genet. 2021 Jul 5;12:647653. doi: 10.3389/fgene.2021.647653.

10.Li J, Huo Y, Wu X, Liu E, Zeng Z, Tian Z, Fan K, Stover D, Cheng L, Li L*. Essentiality and Transcriptome-Enriched Pathway Scores Predict Drug-Combination Synergy. Biology (Basel). 2020 Sep 7;9(9):278. doi: 10.3390/biology9090278.

11.Wang L#, Li J#, Liu E, Kinnebrew G, Zhang X, Stover D, Huo Y, Zeng Z, Jiang W, Cheng L, Feng W*, Li L*. Identification of Alternatively-Activated Pathways between Primary Breast Cancer and Liver Metastatic Cancer Using Microarray Data. Genes (Basel). 2019 Sep 25;10(10):753. doi: 10.3390/genes10100753.

12.Li J, Wang L, Jiang T, Wang J, Li X, Liu X, Wang C, Teng Z, Zhang R, Lv H, Guo M. eSNPO: An eQTL-based SNP Ontology and SNP functional enrichment analysis platform. Sci Rep. 2016 Jul 29;6:30595. doi: 10.1038/srep30595.

13.Li J, Huang D, Guo M, Liu X, Wang C, Teng Z, Zhang R, Jiang Y, Lv H, Wang L. A gene-based information gain method for detecting gene-gene interactions in case-control studies. Eur J Hum Genet. 2015 Nov;23(11):1566-72. doi: 10.1038/ejhg.2015.16.

14.Ge X, Chen W, Shi J, Zhang J, Tai H, Zhang Y, Wang B, Liu W, Chen S, Han H*. Prediction of Moderate-to-Severe Sepsis-Associated Acute Kidney Injury Using a Dual-Timepoint Machine Learning Model: Development, Multiregional Validation, and Clinical Deployment Study. J Med Internet Res. 2025 Sep 30;27:e73840. doi: 10.2196/73840. PMID: 41027023; PMCID: PMC12521856.

15.Han H , Talpur B A , Liu W ,et al.RNA-RBP interactions recognition using multi-label learning and feature attention allocation[J].Journal of Cloud Computing (2192-113X), 2024, 13(1).DOI:10.1186/s13677-024-00612-0.

16.Huang M , Han H , Li L ,et al.A Clinical Decision Support Framework for Heterogeneous Data Sources[J].IEEE Journal of Biomedical and Health Informatics, 2018, PP(99):1-1.DOI:10.1109/JBHI.2018.2846626.