Selected publication



  • Yi Yang, Xingjie Shi, Yuling Jiao, Jian Huang, Min Chen, Xiang Zhou, Lei Sun, Xinyi Lin, Can Yang, and Jin Liu. CoMM-S2: a collaborative mixed model using summary statistics in transcriptome-wide association studies. Submitted.
  • Yuan Gao, Yuling Jiao, Yang Wang, Yao Wang, Can Yang, Shunkang Zhang. Deep Generative Learning via Variational Gradient Flow. [Arxiv][Software][Demo_code]
  • Min Zhou, Mingwei Dai, Yuan Yao, Jin Liu, Can Yang, Heng Peng. BOLT-SSI: Fully Screening Interaction Effects for Ultra-High Dimensional Data. [Arxiv][Software]
  • Mingxuan Cai, Lin Chen, Jin Liu, Can Yang. Quantifying the impact of genetically regulated expression on complex traits and diseases. [BioRxiv][Software]
  • Tao Wang, Can Yang, Hongyu Zhao. Prediction analysis for microbiome sequencing data. Biometrics. [arXiv]
  • Xingjie Shi, Yuling Jiao, Yi Yang, Ching-Yu Cheng, Can Yang, Xinyi Lin, Jin Liu. VIMCO: Variational Inference for Multiple Correlated Outcomes in Genome-wide Association Studies. Bioinformatics. [Arxiv][Software]
  • Mingxuan Cai, Mingwei Dai, Jingsi Ming, Heng Peng, Jin Liu, Can Yang. BIVAS: A scalable Bayesian method for bi-level variable selection. Journal of Computational and Graphical Statistics. [Arxiv] [Software]


  • Jia Zhao, Jingsi Ming, Xianghong Hu, Jin Liu, Can Yang. Bayesian Weighted Mendelian Randomization for Causal Inference based on Summary Statistics. [Arxiv][Software]
  • Jian Huang, Yuling Jiao, Bangti Jin, Jin Liu, Xiliang Lu, Can Yang. A unified primal dual active set algorithm for nonconvex sparse recovery. [Arxiv][Software].
  • Jian Huang, Yuling Jiao, Jin Liu, Can Yang. REMI: Regression with marginal information and its application in genome-wide association studies. [Arxiv][Software]
  • Jingsi Ming, Tao Wang, Can Yang. LPM: A latent probit model to characterize relationship among complex diseases using summary-statistics from multiple GWAS and functional annotations. [BioRxiv][Software]
  • Xiangyu Luo, Can Yang, Yingying Wei. Detection of cell-type-specific risk-CpG sites in epigenome-wide association studies. [BioRvix][Software at Bioconductor]
  • Can Yang, Xiang Wan, Xinyi Lin, Mengjie Chen, Xiang Zhou, Jin Liu. CoMM: a collaborative mixed model to dissecting genetic contributions to complex traits by leveraging regulatory information. Bioinformatics. [link][Software]
  • Mingwei Dai, Xiang Wan, Peng Hao, Yao Wang, Yue Liu, Jin Liu, Zongben Xu, and Can Yang. Joint analysis of Individual-level and summary-level GWAS data by leveraging pleiotropy. Bioinformatics. [link][Software]
  • Yi Yang, Mingwei Dai, Jian Huang, Xinyi Lin, Can Yang, Jin Liu, Min Chen. LPG: a four-groups probabilistic approach to leveraging pleiotropy in genome-wide association studies. BMC Genomics. [Link][Software]
  • Jingsi Ming, Mingwei Dai, Mingxuan Cai, Xiang Wan, Jin Liu, Can Yang. LSMM: A statistical approach to integrating functional annotations with genome-wide association studies. Bioinformatics. March, 2018. [Bioinformatics link][Software]


  • C. Wu, C. Yang, H. Zhao, J. Zhu. On the convergence of the EM algorithm: from the statistical perspective. [arXiv]
  • Y. Hu, Q. Lu, R. Powles, X. Yao, C. Yang, F. Fang, X. Xu, H. Zhao. Leveraging Functional Annotations in Genetic Risk Prediction for Human Complex Diseases. PLoS Computational Biology. [PLoS Computational Biology link] [Biorxiv]
  • J. Liu, X. Wan, C. Wang, Ch. Yang, X. Zhou, C. Yang. LLR: A latent low-rank approach to colocalizing genetic risk variants in multiple GWAS. Bioinformatics. 2017. [Bioinformatics link]
  • M. Dai, J. Ming, M., Cai, J. Liu, C. Yang, X. Wan, and Z. Xu. IGESS: A statistical approach to integrating individual level genotype data and summary statistics in genome wide association studies. Bioinformatics. 2017. [Bioinformatics link][software]
  • Z. Lin, T. Wang, C. Yang, H. Zhao. On Joint estimation of Gaussian graphical models for spatial and temporal data. Biometrics, DOI: 10.1111/biom.12650. 2017. [Arxiv version][software]


  • Z. Lin, C. Yang, Y. Zhu, J. Duchi, Y. Fu, Y. Wang, B. Jiang, M. Zamanighomi, X. Xu, M. Li, N. Sestan, H. Zhao, and W. Wong. Simultaneous dimension reduction and adjustment for confounding variation. Proceedings of the National Academy of Sciences. [PNAS version]. doi: 10.1073/pnas.1617317113, vol. 113 no. 51, 14662-14667. December 20, 2016. [software]
  • J. Liu*, C. Yang*, X. Shi, C. Li, J. Huang, H. Zhao and S. Ma. Analyzing Association Mapping in Pedigree-based GWAS Using A Penalized Multi-trait Mixed Model. Genetic Epidemiology. 2016. *Joint first author. [link] An Early version on [arXiv].
  • C. Yang, X. Wan, J. Liu, and M. Ng. Introduction to statistical methods for integrative data analysis in genome-wide association studies. Book Chapter. Big Data Analytics in Genomics, Springer. 2016. [link]
  • H.Liu, Y. Wang, C. Yang. Mathematical design of a novel gesture-based instruction/input device using wave detection. SIAM Journal on Imaging Sciences. 2016. [arxiv][SIAM version]
  • J. Liu, X. Wan, S. Ma, and C. Yang. EPS: An empirical Bayes approach to integrating pleiotropy and tissue-specific information for prioritizing risk genes. Bioinformatics. 2016.
  • J. Wu, Z. He, X. Liu, F. Gu, J. Zhou, C. Yang. Computing Exact Permutation p-Values for Association Rules. Information Science. 2016.
  • J. Jiang, C. Li, D. Paul, C. Yang, H. Zhao. On high dimensional misspecified mixed model analysis in genome-wide association studies. Annals of Statistics 2016, Vol. 44, No. 5, 2127-2160. [arXiv]
  • C. Yang, C. Li, D. Chung, M. Chen, J. Gelernter and H. Zhao. Introduction to statistical methods in genome-wide association studies. Book Chapter. Genome-Wide Association Studies From Polymorphism to Personalized Medicine, edited by Appasani K, Cambridge University Press. Jan. 2016. [Book link]
  • Chen M, Yang C, Li C, Zhao H: eQTL mapping in Genome-Wide Association Studies: From Polymorphism to Personalized Medicine. Book Chapter. Genome-Wide Association Studies From Polymorphism to Personalized Medicine, edited by Appasani K, Cambridge University Press. Jan. 2016. [Book link]
  • W. Cao, Y. Wang, J. Sun, D. Meng, C. Yang, A. Cichocki, Z. Xu. A Novel Tensor Robust PCA Approach for Background Subtraction from Compressive Measurements. [Arxiv]. IEEE Transactions on Image Processing. 2016.


  • C. Yang, C. Li, Q. Wang, D. Chung, H. Zhao. Implications of pleiotropy: Challenges and opportunities for mining Big Data in Biomedicine. Frontiers in Genetics. 2015. [full text website][pdf]
  • R. Polimanti, C. Yang, H. Zhao, J. Gelernter. Dissecting ancestry genomic background in substance dependence genome-wide association studies. Pharmacogenomics. 2015. [link]
  • J. Liu, F. Wang, H, Zhang, X Gao, and C. Yang. A penalized regression approach for integrative analysis in genome-wide association studies. Journal of Biometrics and Biostatistics. 2015.
  • Q. Wang*, C. Yang*, J. Gelernter, H. Zhao. Pervasive pleiotropy between psychiatric disorders and immune disorders revealed by integrative analysis of multiple GWAS. *Joint first author [BioRxiv]. Human Genetics. [pdf]. 2015. [Yale News]
  • C. Li, C. Yang, G. Hather, R. Liu and H. Zhao. Efficient drug-pathway association analysis via integrative penalized matrix decomposition. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2015.
  • X, Zhou, C. Yang, H. Zhao and W. Yu. Low-Rank Modeling and Its Applications in Image Analysis. ACM Computing Surveys. Vol. 47, No. 2, Article 36, January 2015. [The Matlab code to produce the results presented in this paper]
  • W. Cao, Y. Wang, C. Yang, X. Chang, Z. Han, Z. Xu. Folded-concave penalization approaches to tensor completion. Neurocomputing. 152: 261–273, 2015. [pdf]
  • B. Teng, C. Yang, J. Liu, Z. Cai, X. Wan. Exploring the genetic patterns of complex diseases via the integrative genome-wide approach. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2015. [Arxiv version]
  • C. Li, C. Yang, H. Zhao. Data integration and drug discovery: pathway-based approaches. Book Chapter. Integrating Omics Data, edited by G. Tseng, D. Ghosh, J. Zhou. Cambridge University Press. Sept. 2015. [Book link][pdf]









  • W. Kong, S. Zhu and C. Yang. GPC algorithm and queuing-selecting for networked level Control, the IEEE International Conference on Control and Automation Guangzhou, China, 2007.


  • C. Yang, J. Meng and S. Zhu. Cluster-based input selection for transparent fuzzy modeling, International Journal of Data Warehousing and Mining, 2(3), 57- 75, 2006.
  • C. Yang, S. Zhu, W. Kong and L. Lu. Application of generalized predictive control in networked control system, Journal of Zhejiang University SCIENCE, 7(2), 225-233, 2006.
  • J. Meng and C. Yang. The research of multi-variables hierarchical fuzzy decouplecontrol strategy based on human knowledge, the IEEE 6th World Congress on Intelligent Control and Automation, Dalian, China, 2006.


  • C. Yang, S. Zhu, J. Meng and L. Lu. Transparent fuzzy modeling based on minimum cluster volume, the IEEE Fifth International Conference on Controland Automation, Budapest, Hungary, 2005.
  • C. Yang and J. Meng. Optimal fuzzy modeling based on minimum cluster volume,the First International Conference on Advanced Data Mining and Applications, Wuhan, China, 2005.