个人简历


李晋，博士，教授，海南省拔尖人才
海南医学院 生物医学信息与工程学院
lijin0202@gmail.com,13633603698



个人简介：

李晋，38岁，博士，长期从事生物信息学的教学与科研工作。本人具有统计学、信息学、计算机、药学和遗传学等学术背景。目前主要研究方向为药物基因组学和药物信息学（Pharmacogenomics and Pharmacoinformatics）和癌症系统生物学(Cancer Systems Biology)，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。主持完成国家自然科学基金青年项目一项，黑龙江省自然科学基金一项，黑龙江省教育厅、卫生厅各一项，在研海南省自然科学基金一项。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34篇，被引用700余次，其中第一作者10篇。长期承担概率论与数理统计、生物统计学、多元统计分析、统计遗传学等多门课程的教学工作。

工作经历
2020.11-至今 海南医学院 教授
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. 2021.1-2025.12 《基于多组学数据的个性化癌症药物组合筛选》 海南医学院科研启动经费 （50万） 主持
2. 2021.4-2023.12 《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

已完成科研课题

1. 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
2. 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
3. 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
4. 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

SCI学术论文

已发表第一作者或并列第一作者论文
1.Wang, L;…; Li, Jin*, DysPIA: A novel Dysregulated Pathway Identification Analysis method. Frontiers in Genetics, 2021.6, Accepted.
2.Li, Jin, et al., Essentiality and Transcriptome-Enriched Pathway Scores Predict Drug-Combination Synergy. Biology,2020.9.
3.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.
4.Li, Jin, et al., eSNPO: An eQTL-based SNP Ontology and SNP functional enrichment analysis platform. Scientific reports, 2016. 6: p. 30595.
5.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.
6.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.
7.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. （
8.Jiang, Y.; Li, Jin et al., HGPGD: the human gene population genetic difference database. PloS one, 2013. 8(5): p. e64150.
9.Li, Jin, et al., DBGSA: a novel method of distance-based gene set analysis. Journal of human genetics, 2012. 57(10): p. 642-653.
10.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.


已发表其他论文：
11.Yu,H, et al., Conditional transcriptional relationships may serve as cancer prognostic markers, BMC Medical Genomics, 2021, Accepted.
12.Zhang, X, et al., A pan-cancer study of class-3 semaphorins as therapeutic targets in cancer, BMC Genomics, 2020.4.
13.Lin X. et al.,Genome-wide analysis of aberrant enhancer DNA methylation in human osteoarthritis, BMC Medical Genomics, 2020, 1.
14.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).
15.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.
16.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）
17.Xu, J., et al., EWAS: epigenome-wide association study software 2.0, Bioinformatics, 2018.34(15): p. 2657-2658.
18.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.
19.Lv, W., et al.,The drug target genes show higher evolutionary conservation than non-target genes, Oncotarget,2017, 7(4): p. 4961.
20.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.
21.Zhang, M., et al., Genome-wide pathway-based association analysis identifies risk pathways associated with Parkinson’s disease. Neuroscience, 2017. 340: p. 398-410.
22.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.
23.Xuan, P., et al., Prediction of potential disease-associated microRNAs based on random walk. Bioinformatics, 2015. 31(11): p. 1805-1815.
24.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.
25.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.
26.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. citation:
27.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.
28.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.
29.Teng, Z., et al., Computational prediction of protein function based on weighted mapping of domains and GO terms. BioMed research international, 2014. 2014.
30.Zhang, R., et al., RADB: a database of rheumatoid arthritis-related polymorphisms. Database, 2014.
31.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.
32.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.
33.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.
34.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
35.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.
36.Pathway-based drug combinatory synergy prediction using gene expression and essentiality data, 2020 AACR Annual meeting, Cancer Research 80 (16 Supplement), 4397-4397