所属部门：智能医学研究中心   

   职称：青年研究员          

   学历：博士             

研究方向：转化生物信息学

电子邮箱：chenjunfang@ipm-gba.org.cn 

个人介绍

陈俊芳，德国海德堡大学博士，海德堡大学精神心理健康中心研究所Research Associate，现任粤港澳大湾区精准医学研究院（广州）青年研究员，复旦大学生命科学学院双聘研究员，课题组长。陈俊芳博士聚焦在转化生物信息学方面的研究，尤其是通过DNA甲基化组、基因组、转录组和神经影像以及临床信息等多模态数据研发系统性计算生物学方法和可解释性机器学习模型对精神心理疾病的分子机制、个性化遗传或表观遗传风险预测和新型生物标志物鉴定等有着深入的研究，科研成果包括JAMA Psychiatry, Schizophrenia Bulletin和Translational Psychiatry等国际期刊20余篇。 

课题组介绍

转化生物信息学与复杂疾病课题组致力于应用机器学习、统计学和高维组学大数据分析方法研究复杂神经系统性疾病和衰老相关的机制及其转化应用，全力助力个性化精准医学。课题组注重跨学科、基础研究与转化融通，研究方向包括但不限制于以下三个方面：

1）表观遗传可塑性解析：基于表观基因组学（例如甲基化组学）和机器学习等现代分子流行病学方法结合临床干预手段揭示复杂疾病[与或]衰老的表观遗传学机制及其可塑性；

2）衰老疾病共通机制探索：通过转化生物信息学和多模态跨尺度大数据鉴定与复杂疾病和衰老相关且具有生物学意义的新型分子途径与分子标志物；

3）大数据分析方法研发：应用机器学习、生物信息学和多组学包括单细胞组学以及神经影像学等多学科交叉前沿技术研究新型的数据分析策略，助力疾病风险诊断、疗效评估和靶点精准定位。

合作导师

金力，中国科学院院士，复旦大学校长，复旦大学上海医学院院长，粤港澳大湾区精准医学研究院（广州）院长，教授，博导，德国马普学会外籍会员，中国医学科学院学部委员环境与预防学部副主任，中国遗传学学会副理事长，中国人类学学会副会长，“十三五”国家精准医学专项专家组长。主要研究方向为医学遗传学及遗传流行病学、人类群体遗传学和计算生物学，在Cell、Nature、Science、Nat Rev Genet、JAMA、JCI等国际重要学术刊物发表论文1500多篇，被引6.1万余次。研究成果2次荣获国家自然科学二等奖（第一完成人）。

代表性论文专著

Chen, J., Schwarz, K., Zang, Z., Braun, U., Harneit, A., Kremer, T., ... & Schwarz, E. (2021). Hyper-coordinated DNA methylation is altered in schizophrenia and associated with brain function. Schizophrenia Bulletin Open.

Chen, J., Cao, H., Kaufmann, T., Westlye, L. T., Tost, H., Meyer-Lindenberg, A., & Schwarz, E. (2020). Identification of Reproducible BCL11A Alterations in Schizophrenia Through Individual-Level Prediction of Coexpression. Schizophrenia Bulletin. 

Chen, J., Zang, Z., Braun, U., Schwarz, K., Harneit, A., Kremer, T., ... & Schwarz, E. (2020). Association of a Reproducible Epigenetic Risk Profile for Schizophrenia With Brain Methylation and Function. JAMA psychiatry. 

Chen, J., Cao, H., Meyer-Lindenberg, A. and Schwarz, E., 2018. Male increase in brain gene expression variability is linked to genetic risk for schizophrenia. Translational psychiatry, 8(1), p.140. 

Chen, J., Lippold, D., Frank, J., Rayner, W., Meyer-Lindenberg, A. and Schwarz, E., 2018. Gimpute: an efficient genetic data imputation pipeline. Bioinformatics, 35(8), pp.1433-1435.

Chen, J. and Schwarz, E., 2017. BioMM: Biologically-informed Multi-stage Machine learning for identification of epigenetic fingerprints. arXiv preprint:1712.00336. (NeurIPS ML4H 2017)

Chen, J. and Schwarz, E., 2017. The role of blood-based biomarkers in advancing personalized therapy of schizophrenia. Expert Review of Precision Medicine and Drug Development, 2(6), pp.363-370. 

Chen, J., Lutsik, P., Akulenko, R., Walter, J. and Helms, V., 2014. AKSmooth: Enhancing low-coverage bisulfite sequencing data via kernel-based smoothing. Journal of bioinformatics and computational biology, 12(06), p.1442005. 

Bian, Yi, et al. Efficacy and Safety of Anticoagulation Treatment in COVID-19 Patient Subgroups Identified by Clinical-Based Stratification and Unsupervised Machine Learning: A Matched Cohort Study. Frontiers in medicine (2021).

Gass, Natalia, et al. Differential resting-state patterns across networks are spatially associated with Comt and Trmt2a gene expression patterns in a mouse model of 22q11. 2 deletion. NeuroImage (2021).

Schwarz, K., Moessnang, C., Schweiger, J. I., Harneit, A., Schneider, M., Chen, J., ... & Meyer-Lindenberg, A. (2021). Ventral striatal-hippocampus coupling during reward processing as a (stratification) biomarker for psychotic disorders. Biological Psychiatry.

Gass, N., Peterson, Z., Sartorius, A., Weber-Fahr, W., Reinwald, J. R., … Chen, J. ... & Nickl-Jockschat, T. (2021). Identifying Polygenic Contributions to Differential Resting-State Connectivity in a Mouse Model of 22q11. 2 Deletion. Biological Psychiatry. 

Braun, U., Harneit, A., Pergola, G., … Chen, J. ... et al. (2021). Brain network dynamics during working memory are modulated by dopamine and diminished in schizophrenia. Nat Commun 12, 3478.

Schwarz, E., Alnæs, D., Andreassen, O. A., Cao, H., Chen, J., Degenhardt, F., ... & Herrmann, C. (2020). Identifying multimodal signatures underlying the somatic comorbidity of psychosis: the COMMITMENT roadmap. Molecular Psychiatry, 1-3. 

Zhang, X., Braun, U., Harneit, A., Zang, Z., Geiger, L.S., Betzel, R.F., Chen, J., Schweiger, J.I., Schwarz, K., Reinwald, J.R. and Fritze, S., 2020. Generative network models of altered structural brain connectivity in schizophrenia. NeuroImage, p.117510.

Foo, J. C., Trautmann, N., Sticht, C., Treutlein, J., … Chen, J. …& Marcella Rietschel (2019). Longitudinal transcriptome-wide gene expression analysis of sleep deprivation treatment shows involvement of circadian genes and immune pathways. Translational psychiatry, 9(1), 1-10. 

Peterson, R.E., Kuchenbaecker, K., Walters, R.K., Chen, C.Y., Popejoy, A.B., Periyasamy, S., Lam, M., Iyegbe, C., … Chen, J. ... and Carey, C.E., 2019. Genome-wide Association Studies in Ancestrally Diverse Populations: Opportunities, Methods, Pitfalls, and Recommendations. Cell. 

She, J., Yuan, Z., ..., Chen, J., and Kroll, J. (2018). Targeting erythropoietin protects against proteinuria in type 2 diabetic patients and in zebrafish. Molecular metabolism, 8, 189-202. 

Cao, H., Chen, J., Meyer-Lindenberg, A. and Schwarz, E., 2017. A polygenic score for schizophrenia predicts glycemic control. Translational psychiatry, 7(12), pp.1-9.