史兴杰

•  国际统计学会推选会员（ISI Elected member）

•  中国现场统计研究会理事

•  中国优选法统筹法与经济数学研究会数据科学分会理事

•  现场统计研究会环境与资源统计学会理事

生物医学大数据融合和海洋大数据融合的统计机器学习方法：

•  在生物医学领域，随着高通量测序技术的应用，生物医学大数据的涌现为验证旧发现、发现新规律和产生新知识提供了前所未有的机遇。面对大规模、高通量、高噪声的数据挑战，我们采用统计机器学习方法结合生物信息学技术，融合组学与遗传学大数据，实现从细胞到组织的生物学功能在时空尺度上的解析。

博士生：马晓慧（24级，南京大学，吴兴新课题组）、杨莞硕（23级，重庆大学，周国立课题组）

硕士生：杨娅东（22级学硕）、隆伊凡（24级专硕）

本科生：柏敏希（21级）

•  同时，我们最近开始探索海洋大数据融合，通过研究数据融合和统计机器学习方法，将多源现场观测和海洋模式相结合，建立高时空分辨率的三维温盐流场数据集，服务于西太多圈层相互作用数据集成研究。

硕士生：李纯奕（23级专硕）、郑一诺（24级专硕）、朱洁珍（24级专硕）、夏一凡（24级专硕）

本科生：江叶鹏（22级）、洪宸烨（22级）

本科生

•  数理统计

•  统计软件

研究生

•  机器学习

1. 国家自然科学基金委重大研究计划集成项目“西太平洋多圈层相互作用数据集成研究”（92358303），2024.01-2026.12，主要参与者，在研。

2. 上海市“科技创新行动计划”计算生物学重点专项“精准医学大数据共性统计学习理论、算法与应用”（23JS1400500），2023.12-2026.11，项目高级研究人员，在研。

3. 国家自然科学基金面上项目“大规模遗传变异和生存表型关联分析中的统计方法研究”（12171229），2022.01-2025.12，项目负责人，在研。

4. 国家重点研发计划“油气管网安全运维的大数据分析理论、算法及应用”（2021YFA1000100），2021-2026，项目骨干成员，在研。

5. 上海市面上项目“空间转录组学数据的聚类方法研究”（22ZR1420500），2022.04-2025.03，项目负责人，在研。

6. 国家自然科学基金青年项目“高维单调转移模型的变量选择及其在违约风险评估中的应用” （71501089），2016.01-2018.12年，项目负责人，已结题。

开发的工具：https://github.com/XingjieShi

（#并列一作，*通讯作者，s指导的学生）

1.      Chen, X., Ran, Q., Tang, J., Chen, Z., Huang, S., Shi, X.* and Xi, R.* (2025) Benchmarking algorithms for spatially variable gene identification in spatial transcriptomics. Bioinformatics, p.btaf131.

2.      Dong, Q., Yang, Y., Luo, Z., Shen, H., Shi, X.* and Liu, J.* (2025) Robust Spatial Cell‐Type Deconvolution with Qualitative Reference for Spatial Transcriptomics. Small Methods, p.2401145.

3.      Gu, Z., Liu, S., Ma, H., Long, Y.^S, Jiao, X., Gao, X., Du, B., Bi, X. and Shi, X.*(2025) Estimation of Machine Learning–Based Models to Predict Dementia Risk in Patients With Atherosclerotic Cardiovascular Diseases: UK Biobank Study. JMIR aging, 8, p.e64148.

4.      Shi, X.*^,#; Yang, Y.^#; Ma X.^S; Zhou Y.; Guo Z.; Wang C.; Liu J.* (2023) Probabilistic cell/domain-type assignment of spatial transcriptomics data with SpatialAnno, Nucleic Acids Research, 51(22),gkad1023

5.      Zhang, X.; Liu, X.; Shi, X.* (2023) Model Selection for Varying Coefficient Nonparametric Transformation Model, Econometric Journal, 26(3), 492-512

6.      Zhang, X.; Shi, X.*; Liu, Y.; Liu, X.; Ma, S. (2023) A General Framework for Identifying Hierarchical Interactions and Its Application to Genomics Data, Journal of Computational and Graphical Statistics, 32(3), 873-883

7.      Liu W.; Liao X; Luo Z.; Yang Y.; Lau M.; Jiao Y.; Shi X.; Zhai W.; Ji H.; Yeong J.; Liu J. (2023) Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST, Nature Communications, 14(1), 296

8.      Liu, W.; Liao, X.; Yang, Y.; Lin, H.; Yeong , J.; Zhou, X.*; Shi,X. *; Liu J.* (2022) Joint dimension reduction and clustering analysis of single-cell RNA-seq and spatial transcriptomics data, Nucleic Acids Research, 50(12), gkac219

9.      Yang, Y.^#; Shi, X.^#; Zhou, Q.; Sun, L.; Yeong, J.^∗, Liu, J.^∗ (2022) SC-MEB: spatial clustering with hidden Markov random field using empirical Bayes, Briefing in Bioinformatics, 23(1): bbab466

10.   Cheng, Q.; Qiu, T.; Chai, X.; Sun, B.; Xia, Y.; Shi, X.*; Liu, J.* (2022) MR-Corr²: A two-sample Mendelian randomization method that accounts for correlated horizontal pleiotropy using correlated instrumental variants, Bioinformatics, 38(2): 303-310

11.   Shi, X. and Chai, X. and Yang, Y. and Cheng, Q. and Jiao, Y. and Chen, H. and Huang, J. and Yang, C. and Liu, J. (2020) A tissue-specific collaborative mixed model for jointly analyzing multiple tissues in transcriptome-wide association studies. Nucleic Acids Research, 48(19): e109 [SCI, Impact Factor: 17]

12.   Cheng Q., Yang Y., Shi X., Yeung K., Yang C., Peng H., Liu J. (2020). MR-LDP: a two-sample Mendelian randomization for GWAS summary statistics accounting for linkage disequilibrium and horizontal pleiotropy. NAR Genomics and Bioinformatics, 2(2):lqaa028

13.   Shi, X. and Ma, S. and Huang, Y. (2020). Promoting sign consistency in the Cox proportional hazards curemodel selection.Statistical Methods in Medical Research,29(1):15-28. [SCI]

14.   Yang Y., Shi X., Jiao Y., Huang J., Chen M., Zhou X., Sun L., Lin X., Yang C. and Liu J. (2020) CoMM-S2: a collaborative mixed model using summary statistics in transcriptome-wide association studies. Bioinformatics, 36(7): 2009-16[SCI]

15.   Liao X., Chai X., Shi X., Chen L., Liu J.(2020) The statistical practice of the GTEx Project: from single to multiple tissues. Quantitative Biology [SCI]

16.   史兴杰，王赛旎，李扬. (2020). 高维数据的稳健二分类方法. 统计研究.37(9):95-105

17.   张晶，方匡南*，张喆，史兴杰，郑陈璐. (2020). 基于稀疏结构连续比率模型的消费金融风控研究. 统计研究. 37(11):57-67

18.   Shi, X. and Yang Y. and Jiao Y. and Cheng C. and Yang C. and Lin X. and Liu J. (2019). VIMCO: variational inference for multiple correlated outcomes in genome-wide association studies. Bioinformatics, 35(19), 3693-3700. [SCI]

19.   S. Wang, X. Shi, M. Wu, and S. Ma (2019) Horizontal and vertical integrative analysis methods for mental disorders omics data. Scientific Report, 9(1):13430 [SCI]

20.   孙怡凡，吴梦云，史兴杰. (2019).高维大数据基因网络中的社区发现——以NC方法为例. 统计研究, 36(3), 124-128.

21.   Shi, X., Huang, Y. and Huang, J and Ma, S. (2018). A forward and backward stagewise algorithm for nonconvex loss functions and adaptive lasso. Computational Statistics and Data Analysis, 124, 235-251.

22.   Chai, H*. and Shi, X.* and Zhang, Q and Zhao, Q and Huang, Y and Ma, S. (2017). Analysis of cancer gene expression data with an assisted robust marker identification approach. Genetic Epidemiology,41, 779– 789. [SCI]

23.   Liu, J. and Yang, C. and Shi, X. and Li, C. and Huang, J. and Zhao, H. and Ma, S. (2016). Analyzing Association Mapping in Pedigree‐Based GWAS Using a Penalized Multitrait Mixed Model. Genetic Epidemiology, 40(5), 382-393. [SCI]

24.   Jiang, Y*. andShi, X. * and Zhao, Q. and M. Krauthammer and BE Rothberg and Ma, S. (2016). Integrated analysis ofmultidimensional omics data on cutaneous melanoma prognosis. Genomics,107(6), 223-30.

25.   Shi, X. and Zhao, Q. and Huang, J. and Xie, Y. and Ma, S. (2015). Deciphering the association between gene expression and copy number alteration using a sparse double Laplacian shrinkage approach. Bioinformatics, 31(24), 3977-3983. [SCI]

26.   Shi, X. * and Yi, H* and Ma, S. (2015). Measures for the degree of overlap of gene signatures and applications to TCGA. Briefings in Bioinformatics,16(5), 735-744.  [SCI]

27.   Wu, C. and Shi, X. and Cui, Y. and Ma, S. (2015). A penalized robust semiparametric approach for gene–environment interactions. Statistics in Medicine, 34(30), 4016-30. [SCI]

28.   Zhao, Q.* and Shi, X.* and Xie, Y. and Huang, J. and Ben-Chang Shia and Ma, S. (2015). Combining Multidimensional Genomic Measurements for Predicting Cancer Prognosis: Observations from TCGA. Briefing in Bioinformatics, 16(2), 291-303. [SCI]

29.   Zhao, Q. and Shi, X. and Huang, J. and Liu, J. and Li, Y. and Ma, S. (2015). Integrative analysis of -omics data using penalty functions. WIREs Computational Statistics,7(1), 99-108.

30.   Shi, X. and Liu, J. and Huang, J. and Zhou, Y. and Ben-Chang Shia and Ma, S. (2014). Integrative Analysis of Cancer Prognosis Data with Contrasted Group Bridge Penalization. Genetic Epidemiology, 38(2), 141-151.  [SCI]

31.   Shi,X. and Liu, J. and Huang, J. and Zhou, Y. and Xie, Y. and Ma, S. (2014). A Penalized Robust Method for Identifying Gene-Environment Interactions. Genetic Epidemiology, 38(3), 220-230.   [SCI]

32.   Shi, X. and Shen, S. and Liu, J. and Huang, J. and Zhou, Y. and Ma, S.(2014). Similarity of Markers Identified from Cancer Gene Expression Studies: Observations from GEO. Briefing in Bioinformatics, 15(5), 671-684. [SCI]

•  上海市优秀博士学位论文

•  首届《统计研究》优秀论文三等奖

•  “第十一届全国大学生市场调查与分折大赛总决赛”一等奖指导教师