个人简历
李晋，博士，教授，博士生导师，海南省拔尖人才
澳门新葡萄新京威尼斯123 生物医学信息与工程学院
lijin@muhn.edu.cn,13633603698
个人简介：
李晋，42岁，博士，卫生健康大数据海南省工程研究中心副主任、计算表观遗传学教研室主任，研究生管理办公室主任，留学美国印第安纳大学和俄亥俄州立大学。长期从事人工智能和生物信息学的教学与科研工作。具有统计学、计算机、药学和遗传学等学术背景。目前主要研究方向为机器学习、人工智能及在药物基因组学、药物信息学（Pharmacogenomics and Pharmacoinformatics）和癌症系统生物学(Cancer Systems Biology)中的应用，特别是药物-药物组合预测（Drug-drug combination prediction）、癌症风险通路挖掘(Cancer risk pathway mining)、表达数量性状挖掘(Expression QTL)、基因-基因互作(Gene-Gene Interaction)等。主持国家自然科学基金地区项目二项，省自然科学基金二项。在Science translational medicine、European journal of human genetics、Frontiers in Genetics、genes、Scientific reports、Bioinformatics等杂志发表SCI收录学术论文40余篇，被引用1500余次，其中第一作者13篇。长期承担概率论与数理统计、生物统计学、多元统计分析、统计遗传学、生物信息学等多门课程的教学工作。担任中国生物信息学学会（筹）重大疾病组学信息学专委会常委、中国人工智能学会生物信息学与人工生命专委会委员、中国计算机学会生物信息学专委会委员、中国细胞生物学会重大疾病组学信息学专委会委员、中国生物工程学会计算生物学与生物信息学专委会委员、海南省科技伦理委员会人工智能领域专家委员会委员、海南省学位委员会第一届学科评议组委员、海南省科技工作者协会委员等职务。
工作经历
2020.11-至今 澳门新葡萄新京威尼斯123 教授、博士生导师
2018.4-2020.10 美国俄亥俄州立大学 博士后
2016.12-2018.3 美国印第安纳大学 博士后
2014.9-2020.10 哈尔滨医科大学 副教授、硕士生导师
2009.9-2014.8 哈尔滨医科大学 讲师
2004.6-2009.8 哈尔滨医科大学 助教
学习经历
2012.3-2016.7 哈尔滨工业大学 生命科学与技术学院/计算机科学与技术学院 生物医学工程 博士 导师：郭茂祖教授
2006.8-2009.6 哈尔滨医科大学 生物信息科学与技术学院
生物物理学 硕士 导师：李霞教授
2000.8-2004.6 吉林大学数学学院 统计学 学士
在研科研课题
1. 2023.1-2026.12《基于生物通路和子通路分析的个性化乳腺癌药物组合筛选》 国家自然科学基金地区项目（32260155，33万） 主持
2. 2021.1-2025.12 《基于多组学数据的个性化癌症药物组合筛选》 澳门新葡萄新京威尼斯123科研启动经费 （50万） 主持
已完成科研课题
1. 2021.4-2024.6 《整合多组学数据的细胞系特异药物协同作用预测研究》 海南省自然科学基金面上项目 （5万） 主持 已结题
2. 2014.1-2016.12《面向人类复杂疾病的EQTL模块挖掘及其META分析方法研究》 国家自然科学基金青年项目（61300116，23万） 主持 已结题
3. 2014.1-2016.12 《全基因组meta-eQTL模型挖掘人类复杂疾病风险模块》 黑龙江省自然科学基金（5万） 主持 已结题
4. 2013.1-2015.12 《基于基因-基因共调控网络挖掘类风湿性关节炎风险基因》
黑龙江省教育厅面上项目（2万） 主持 已结题
5. 2012.1-2013.12 《复杂疾病相关基因功能类挖掘方法研究及平台建设》
黑龙江省卫生厅项目 主持 已结题
已发表学术论文
第一作者/通讯作者论文
1. Chen,J.;…; Li Jin*, Prediction of cancer cell line-specific synergistic drug combinations based on multi-omics data. PeerJ, 2025,2.
2. Chen,J.;…; Li Jin*, HCDT: An integrated Highly Confident Drug-Target Resource. Database, 2022,11.
3. Xie,W;…; Li Jin*, Extracting Drug-Drug Interactions from Biomedical Texts Using BioBERT with Improved Focal Loss, 2023 11th International Conference on Bioinformatics and Computational Biology, ICBCB 2023, Hangzhou, China, 2023-4-21-23. （EI）
4. Wang, L;…; Li, Jin*, DysPIA: A novel Dysregulated Pathway Identification Analysis method. Frontiers in Genetics, 2021,12: 647653.
5. Li, Jin, et al., Essentiality and Transcriptome-Enriched Pathway Scores Predict Drug-Combination Synergy. Biology,2020.9.
6. Wang, L.;Li, Jin, et al., Identification of Alternatively-Activated Pathways between Primary Breast Cancer and Liver Metastatic Cancer Using Microarray Data. Genes, 2019.10(10): p. 753.
7. Li, Jin, et al., eSNPO: An eQTL-based SNP Ontology and SNP functional enrichment analysis platform. Scientific reports, 2016. 6: p. 30595.
8. Li, Jin, et al., A gene-based information gain method for detecting gene-gene interactions in case-control studies. European journal of human genetics, 2015. 23(11): p. 1566-1572.
9. Li, Jin, et al., Mining disease genes using integrated protein–protein interaction and gene–gene co‐regulation information. FEBS open bio, 2015. 5(1): p. 251-256.
10. Li, Jin, et al., Relationship of common expression quantitative trait loci genes to the immune system. Genetics and Molecular Research, 2013. 12(4): p. 6546-6553.
11. Jiang, Y.; Li, Jin et al., HGPGD: the human gene population genetic difference database. PloS one, 2013. 8(5): p. e64150.
12. Li, Jin, et al., DBGSA: a novel method of distance-based gene set analysis. Journal of human genetics, 2012. 57(10): p. 642-653.
13. Wang, L.; Li, Jin et al., A novel stepwise support vector machine (SVM) method based on optimal feature combination for predicting miRNA precursors. African Journal of Biotechnology, 2011. 10(74): p. 16720-16731.
发表其他论文：
14. Xie, W. et al., Transformer-based Named Entity Recognition for Clinical Cancer Drug Toxicity by Positive-unlabeled Learning and KL Regularizers, Current Bioinformatics, 2024 19 (8), 738-751.
15. Liang, P. et al., Important nutrient sources and carbohydrate metabolism patterns in the growth and development of spargana, Parasites & Vectors, 2024 17 (1), 68.
16. Wang, Z. et al., Portraying the dark side of endogenous IFN-λ for promoting cancer progression and immunoevasion in pan-cancer, Journal of Translational Medicine, 2023, 21(1).
17. Zeng, Z. et al, Identifying novel therapeutic targets in gastric cancer using genome-wide CRISPR-Cas9 screening, Oncogene, 2022 41(14):2069-2078.
18. Wang, N. et al, Deep learning using bulk RNA-seq data expands cell landscape identification in tumor microenvironment, Oncoimmunology, 2022 11 (1), 2043662
19. Yu, H. et al., Conditional transcriptional relationships may serve as cancer prognostic markers, BMC Medical Genomics, 2021, 14,101.
20. Zhang, X. et al., A pan-cancer study of class-3 semaphorins as therapeutic targets in cancer, BMC Genomics, 2020.4.
21. Zeng, Z. et al., Expression, Location, Clinical Implication, and Bioinformatics Analysis of RNASET2 in Gastric Adenocarcinoma, Frontiers in Oncology, 2020,10:836.
22. Lin X. et al.,Genome-wide analysis of aberrant enhancer DNA methylation in human osteoarthritis, BMC Medical Genomics, 2020, 1.
23. Zhang, X. et al., Identification of a subtype of hepatocellular carcinoma with poor prognosis based on expression of genes within the glucose metabolic pathway, Cancers, 2019 14;11(12).
24. Liu, E. et al., A Fast and Furious Bayesian Network and Its Application of Identifying Colon Cancer to Liver Metastasis Gene Regulatory Networks. IEEE/ACM transactions on computational biology and bioinformatics, 2019.10.
25. Zhang, Y. et al., Multidimensional Integration Analysis of Autophagy-related Modules in Colorectal Cancer, Letters in Organic Chemistry, 2019.3.
26. Sun, X. et al., A PET imaging approach for determining EGFR mutation status for improved lung cancer patient management, Science translational medicine,2018. 10(431): p. eaan8840. （SCI影响因子：17.16）
27. Xu, J. et al., EWAS: epigenome-wide association study software 2.0, Bioinformatics, 2018.34(15): p. 2657-2658.
28. Zhang, T. et al., Core signaling pathways in ovarian cancer stem cell revealed by integrative analysis of multi-marker genomics data. PloS one, 2018. 13(5): p. e0196351.
29. Lv, W. et al.,The drug target genes show higher evolutionary conservation than non-target genes, Oncotarget,2017, 7(4): p. 4961.
30. Lv, H. et al., Genome-wide haplotype association study identify the FGFR2 gene as a risk gene for Acute Myeloid Leukemia. Oncotarget, 2017. 8(5): p. 7891.
31. Zhang, M. et al., Genome-wide pathway-based association analysis identifies risk pathways associated with Parkinson’s disease. Neuroscience, 2017. 340: p. 398-410.
32. Zhang, M. et al., Integrative analysis of genome-wide association studies and gene expression analysis identifies pathways associated with rheumatoid arthritis. Oncotarget, 2016. 7(8): p. 8580.
33. Xuan, P. et al., Prediction of potential disease-associated microRNAs based on random walk. Bioinformatics, 2015. 31(11): p. 1805-1815.
34. Shang, Z. et al., Genome-wide haplotype association study identify TNFRSF1A, CASP7, LRP1B, CDH1 and TG genes associated with Alzheimer's disease in Caribbean Hispanic individuals. Oncotarget, 2015. 6(40): p. 42504.
35. Zhang, R. et al., Genes with stable DNA methylation levels show higher evolutionary conservation than genes with fluctuant DNA methylation levels. Oncotarget, 2015. 6(37): p. 40235.
36. Jiang, Y. et al., MCPerm: a Monte Carlo permutation method for accurately correcting the multiple testing in a meta-analysis of genetic association studies. PloS one, 2014. 9(2): p. e89212.
37. Lv, H. et al., Association between polymorphisms in the promoter region of interleukin-10 and susceptibility to inflammatory bowel disease. Molecular biology reports, 2014. 41(3): p. 1299-1310.
38. Zhang, M. et al., Pathway-based association analysis of two genome-wide screening data identifies rheumatoid arthritis-related pathways. Genes and immunity, 2014. 15(7): p. 487-494.
39. Teng, Z. et al., Computational prediction of protein function based on weighted mapping of domains and GO terms. BioMed research international, 2014.
40. Zhang, R., et al., RADB: a database of rheumatoid arthritis-related polymorphisms. Database, 2014.
41. Xuan, P. et al., Prediction of microRNAs associated with human diseases based on weighted k most similar neighbors. PloS one, 2013. 8(8): p. e70204.
42. Zhang, R. et al., Association between the IL7R T244I polymorphism and multiple sclerosis: a meta-analysis. Molecular biology reports, 2011. 38(8): p. 5079-5084.
43. Sun, P. et al., Assessing the patterns of linkage disequilibrium in genic regions of the human genome. The FEBS journal, 2011. 278(19): p. 3748-3755.
44. Chen, X. et al., A sub-pathway-based approach for identifying drug response principal network. Bioinformatics, 2010. 27(5): p. 649-654.
会议特邀学术报告
45. 基于高通量测序数据的肝细胞癌增强子RNA标志物挖掘与功能分析.中国细胞生物学学会2024年全国学术大会，福州，2024.4.8-12.
46. Identification of Alternatively-Activated Pathways between Primary Breast Cancer and Liver Metastatic Cancer Using Microarray Data, 2019 International Conference on Intelligent Biology and Medicine (ICIBM 2019), Columbus, Ohio, USA, 2019.6.9-11.
47. Pathway-based drug combinatory synergy prediction using gene expression and essentiality data, 2020 AACR Annual meeting, Cancer Research 80 (16 Supplement), 4397-4397
