招生工作
导师介绍
李文然
博士 副研究员 硕士生导师
人类表型组学研究组
研究方向：计算生物学、环境暴露与多组学整合分析
电子邮件(E-mail)：wrli@sinh.ac.cn
电话(Tel)：021-54920514
简历

2025-至今：中国科学院上海营养与健康研究所 副研究员
2021-2024年：中国科学院上海营养与健康研究所 博士后
2018-2019年：斯坦福大学 联合培养博士生
2015-2020年：清华大学 博士
2011-2015年：厦门大学 学士

研究内容

暴露组学研究个体一生中的环境暴露（饮食、生活方式、空气污染、社会因素等）对健康的影响。近年来，高通量组学技术和人工智能的发展，为暴露组学在健康研究中的应用提供了新机遇。我们的研究方向包括：
1.识别影响健康与疾病的主导暴露因素。采用因果推断模型，识别对健康具有保护作用的暴露因素以及疾病风险因素，并探索是否存在可优化健康的个性化暴露组合（如饮食、空气质量、社会环境等）。
2.整合多组学数据解析环境暴露导致个体健康差异的生物过程。结合基因组、表观基因组、蛋白组、代谢组、微生物组数据，建立精准的个体暴露风险评估模型，构建个体化暴露特征图谱，建立环境暴露的人群易感性分数，为相关疾病的早期预防提供指导。
3.大规模暴露数据的标准化与整合。如何在不同地区、人群中实现暴露数据的标准化，提高跨队列研究的可比性。整合环境监测、生物样本、可穿戴设备、电子健康记录等多源数据，构建全面的暴露组评估模型。

代表性论文（#共同第一作者, *共同通讯作者）
  1. Li W#, Cheng Y#, Cui A#, Huang M, Huang Q, Wang Q, Xia M, Qiu J, Peng Q, Li J, Li H, Wang Y, Zong G, Zheng Y, Wang J, Gao X, Ding C, Tang H, Jiang BH, Jin L, Li Y, Wang S. Multiomics Integration of Epigenetics, Proteomics, and Metabolomics Identifies Putative Drug Targets and Improves Early Prediction for Diabetes. Diabetes 2025 Sep 12:db250354.
  2. Yan Y#, Chai X#, Liu J, Wang S, Li W*, Huang T*. DeepMethyGene: a deep-learning model to predict gene expression using DNA methylations. BMC bioinformatics 2025 Dec;26(1):1-0.
  3. Fan X#, Qian Q#, Li W, Liu T, Zeng C, Jia P, Lin H, Gao X, Jin L, Xia M, Wang S, Liu F. Epigenetic drift score captures directional methylation variability and links aging to transcriptional, metabolic, and genetic alterations. Genome Research 2025 Oct 1;35(10):2173-2188.
  4. Li W#, Xia M#, Zeng H, Lin H, Teschendorff AE, Gao X*, Wang S*. Longitudinal analysis of epigenome-wide DNA methylation reveals novel loci associated with BMI change in East Asians. Clinical Epigenetics 2024 May 27;16(1):70.
  5. Peng Q#, Liu X#, Li W#, Jing H#, Li J, Gao X, Luo Q, Breeze CE, Pan S, Zheng Q, Li G, Qian J, Yuan L, Yuan N, You C, Du S, Zheng Y, Yuan Z, Tan J, Jia P, Wang J, Zhang Q, Lu X, Shi L, Guo S, Liu Y, Ni T, Wen B, Zeng C, Jin L, Teschendorff AE*, Liu F* & Wang S*. Analysis of blood methylation quantitative trait loci in East Asians reveals ancestry-specific impacts on complex traits. Nature Genetics 2024 May;56(5):846-860.
  6. Li W#, Zeng W#, Wong WH*. Modeling the causal mechanism between genotypes and phenotypes using large-scale biobank data and context-specific regulatory networks. Cybernetics and Intelligence 2024 Mar 5.
  7. Zeng H#, Li W#, Xia M, Ge J, Ma H, Chen L, Pan B, Lin H*, Wang S*, Gao X*. Longitudinal association of peripheral blood DNA methylation with liver fat content: distinguishing between predictors and biomarkers. Lipids in Health and Disease 2024 Sep 27;23(1):309.
  8. Xia M#, Li W#, Lin H, Zeng H, Ma S, Wu Q, Ma H, Li X, Pan B, Gao J, Hu Y, Liu Y, Wang S*, Gao X*. DNA methylation age acceleration contributes to the development and prediction of non-alcoholic fatty liver disease. GeroScience 2024 Aug;46(4):3525-3542.
  9. Cai X#, Li K#, Meng X#, Song Q#, Shi S, Li W, Niu Y, Jin L, Kan H*, Wang S*. Epigenome-wide association study on short-, intermediate-and long-term ozone exposure in Han Chinese, the NSPT study. Journal of Hazardous Materials 2024 Feb 5;463:132780.
  10. Yan Y, Li W, Wang S, Huang T*. Seq-rbppred: predicting rna-binding proteins from sequence. ACS omega 2024 Mar 4;9(11):12734-12742.
  11. Han D, Li Y, Wang L, Liang X, Miao Y, Li W, Wang S, Wang Z*. Comparative analysis of models in predicting the effects of SNPs on TF-DNA binding using large-scale in vitro and in vivo data. Briefings in Bioinformatics 2024 Mar 1;25(2):bbae110.
  12. Yang S, Liu Z, Li W, Hu Y, Liu S, Jing R, Hua W*. Validation of three European risk scores to predict long-term outcomes for patients receiving cardiac resynchronization therapy in an Asian population. Journal of Cardiovascular Translational Research 2021 Aug;14:754-760.
  13. Li W#, Duren Z#, Jiang R*, Wong WH*. A method for scoring the cell type-specific impacts of noncoding variants in personal genomes. Proceedings of the National Academy of Sciences 2020 Sep 1;117(35):21364-21372.
  14. Wang Y*, Chen S, Li W, Jiang R, Wang Y*. Associating divergent lncRNAs with target genes by integrating genome sequence, gene expression and chromatin accessibility data. NAR Genomics and Bioinformatics 2020 Jun;2(2):lqaa019.
  15. Li W, Wong WH*, Jiang R*. DeepTACT: predicting 3D chromatin contacts via bootstrapping deep learning. Nucleic Acids Research 2019 Jun 4;47(10):e60.
  16. Gan M#, Li W#, Jiang R*. EnContact: predicting enhancer-enhancer contacts using sequence-based deep learning model. PeerJ 2019 Sep 13;7:e7657.
  17. Li W, Wang M, Sun J, Wang Y*, Jiang R*. Gene co-opening network deciphers gene functional relationships. Molecular BioSystems 2017;13(11):2428-2439.
  18. Gan M, Li W, Zeng W, Wang X, Jiang R*. Mimvec: a deep learning approach for analyzing the human phenome. BMC systems biology 2017 Sep;11:3-16.