Research interests lie in development and application of statistical and computational methods to address scientific problems in genomics, systems biology, and complex diseases. Current methodological research topics include statistical learning and inference, high-dimensional correlated or structured data analysis, graphical models, causal inference, and network analysis.
Funding Acknowledgment: University of Connecticut and NSF.
Google Citations My Github Link Zhang-Data-Science-Research-Lab
Trainee author
Linder, H., Zhang, Y., Wang, Y., Ouyang, Z. (2024) Integrative pathway analysis with gene expression, miRNA, methylation and copy number variation for breast cancer subtypes. Statistical Applications in Genetics and Molecular Biology. 23(1), p.20190050.
Zhang, Y. and Ouyang, Z. (2024) Joint model-based distance embedding of multi-track Hi-C data for chromosomal conformation learning. Statistics and Its Interface, 17(3), 565- 571.
Liu, Q. and Zhang, Y. (2023) Integrative Structural Learning of Mixed Graphical Models via Pseudo-likelihood. Statistics in Biosciences, pp.1-21.
Linder, H. and Zhang, Y. (2022). MiRNA–Gene Activity Interaction Networks (miGAIN): Integrated Joint Models of miRNA–Gene Targeting and Disturbance in Signaling Pathways. In Advances and Innovations in Statistics and Data Science (pp. 3-21) Springer, Cham.
Lin, J., Chen, Y., Zhang, Y., Lin, H. and Ouyang, Z., (2022). Deciphering the role of RNA structure in translation efficiency. BMC bioinformatics, 23(3), pp.1-15.
Cheng, A., Mao, D., Zhang, Y., Glaz, J. and Ouyang, Z. (2022) Translocation Detection from Hi-C data via Scan Statistics. Biometrics. doi.org/10.1111/biom.13724.
Zhang, Y., Mao, D. and Ouyang, Z. (2022) Model-based distance embedding with applications to chromosomal conformation biology. The Annals of Applied Statistics,16(3), pp.1253-1267.
Liu, Q., Zhang, Y. and Ouyang, Z., (2021). Structural inference of time‐varying mixed graphical models. Stat, 10(1), p.e414.
Chen, Y., Zhang, Y., Li, J., and Ouyang, Z. (2021) LISA2: learning complex single cell trajectory and expression trends. Frontiers in Genetics. DOI: 10.3389/fgene.2021.681206.
Linder, H. and Zhang, Y., (2021). A Pan-Cancer Network Analysis with Integration of miRNA-Gene Targeting for Multiomics Datasets. Journal of Data Science, 19(4), pp.555-568.
Zhang, Y. (2022) Principal wave analysis for high-dimensional structured data with applications to epigenomics and neuroimaging studies. Statistics and Its Interface, 15(2), 225-236.
Liu, Q. and Zhang, Y. (2020) Fast variational inference for joint mixed sparse graphical models. IEEE Journal on Selected Areas in Information Theory, vol. 1, no. 3, pp. 908-913, Nov. 2020, doi: 10.1109/JSAIT.2020.3042124.
Zhang, Y., Ouyang, Z., Qian, W., Smith, R., Wong, W.H., and Davis, R.W. (2020) Meta-analysis of peptides to detect protein significance. Statistics and Its Interface. 13(4):465-474.
Lin, J., Chen, Y., Zhang, Y., Ouyang, Z. (2020) Identification and analysis of RNA structural disruptions induced by single nucleotide variants using Riprap and RiboSNitchDB. NAR Genomics and Bioinformatics. 2(3):lqaa057. https://doi.org/10.1093/nargab/lqaa057
Mohammed, S., Dey, D., Zhang, Y. (2020) Classification of high-dimensional electroencephalography data with location selection using structured spike-and-slab prior. Statistical Analysis and Data Mining. https://doi.org/10.1002/sam.11477
Liu, Q. and Zhang, Y. (2020) Joint estimation of heterogeneous exponential Markov random fields through an approximate likelihood inference. Journal of Statistical Planning and Inference. https://doi.org/10.1016/j.jspi.2020.04.003.
Wu, H.†, Mao, D.†, Zhang, Y.†, Chi, Z., Stitzel, M. and Ouyang, Z. (2020) A new graph-based clustering method with application to single-cell RNA-seq data from human pancreatic islets. Accepted at NAR Genomics and Bioinformatics. LrSClust R-package († joint first authors)
Chen, Y., Mao, D., Zhang, Y. and Ouyang, Z. (2020). An unsupervised learning method for reconstructing cell spatial organization with application to the DREAM Single Cell Transcriptomics Challenge. F1000Research, 9(124), p.124.
Tanevski, J., Nguyen, T., Truong, B., Karaiskos, N., Ahsen, M.E., Zhang, X., Shu, C., Hu, Y., Pham, H.V., Li, X., Le, T.D., Tarca, A.L., Bhatti, G., Romero, R., Karathanasis, N., Loher, P., Chen, Y., Ouyang, Z., Mao, D., Zhang, Y., Zand, M., Ruan, J., Hafemeister, C., Qiu, P., Tran, D., Nguyen, T., Gabor, A., Yu, T., Glaab, E., Krause, R., Banda, P., DREAM SCTC Consortium, Stolovitzky, G., Rajewsky, N., Saez-Rodriguez, J. and Meyer, P. (2020) Predicting cellular position in the Drosophila embryo from Single-Cell Transcriptomics data. bioRxiv, p.796029. [Paper] Life Science Alliance, 3 (11) e202000867; DOI: 10.26508/lsa.202000867.
Linder, H. and Zhang, Y. (2019) A pan-cancer integrative pathway analysis of multi-omics data. Quantitative Biology. doi.org/10.1007/s40484-019-0185-6
Mohammed, S., Dey, D., Zhang, Y. (2019) Bayesian variable selection using spike and slab prior with application to high dimensional EEG data by local modeling. Journal of the Royal Statistical Society Series C. https://doi.org/10.1111/rssc.12369
Lin, J., Zhang, Y., Frankel, W., Ouyang, Z. (2019) PRAS: predicting functional targets of RNA binding proteins based on CLIP-seq peaks. PLOS Computational Biology. 15(8): e1007227. https://doi.org/10.1371/journal.pcbi.1007227
Linder, H., Zhang, Y. (2019) Iterative integrated imputation for missing data and pathway models with applications to breast cancer subtypes. Communications for Statistical Applications and Methods. 26:411-430 https://doi.org/10.29220/CSAM.2019.26.4.411
Zhang, Y., Chen, Y., Ouyang, Z. (2020) PATH: An interactive web platform for analysis of time-course high-dimensional genomic data. International Journal of Computational Biology and Drug Design. 2020 Vol.13 No.5/6, pp.529 – 538. DOI: 10.1504/IJCBDD.2020.113861.
Zhang, Y. (2018) Lagged principal trend analysis for longitudinal high-dimensional data. Stat. 2019; 8:e213. https://doi.org/10.1002/sta4.213
Chen, Y., Zhang, Y., Ouyang, Z. (2018) LISA: Accurate reconstruction of cell trajectory and pseudo-time for massive single cell RNA-seq data. Pacific Symposium on Biocomputing. 24:338-349 (2019)
Gonzalez-Hernandez, G., Lu, Z., Leaman, R., Weissenbacher, D., Boland, M. R., Chen, Y., Du, J., Fluck J., Greene, C., Holmes, J., Kashyap, A., Nielsen, R., Ouyang, Z., Schaaf, S., Taroni, J., Tao, C., Zhang, Y., Liu, H. (2018) PSB 2019 Workshop on Text Mining and Visualization for Precision Medicine. Pacific Symposium on Biocomputing. 24:449-454 (2019).
Zhang, Y., and Ouyang, Z. (2017) Joint principal trend analysis for longitudinal high-dimensional data. Biometrics, doi:10.1111/biom.12751.
Zhang, Y., Ouyang, Z. and Zhao, H. (2017). A statistical framework for data integration through graphical models with application to cancer genomics. The Annals of Applied Statistics, 11(1), 161-184. pdf
Zhang, Y., Linder, M. H., Shojaie, A., Ouyang, Z., Shen, R., Baggerly, K. A., Baladandayuthapani V. and Zhao, H. (2017). Dissecting pathway disturbances using network topology and multi-platform genomics data. Statistics in Biosciences, doi:10.1007/s12561-017-9193-0.
Wang, C., Chen, M., Wu, J., Yan, J., Zhang, Y. and Schifano, E. (2017) Online updating method with new variables for big data streams. The Canadian Journal of Statistics, doi:10.1002/cjs.11330.
Zou, C., Zhang, Y., and Ouyang, Z. (2016). HSA: integrated multi-track Hi-C data modeling for genome-scale reconstruction of 3D chromatin structure. Genome Biology, 17: 40.
Gan, G., Zhang, Y. and Dey, D. (2016). Clustering by propagating probabilities between data points. Applied Soft Computing, 41, 390-399.
Zhang, Y., and Ouyang, Z. (2014). Predicting quantitative outcomes of patients using longitudinal gene expression. Sri Lankan Journal of Applied Statistics, Special Issue “Modern Statistical Methodologies in the Cutting Edge of Science”, 5(4), 117-126.
Zhang, Y. and Davis, R.W. (2013) Principal trend analysis for time-course data with applications in genomic medicine. The Annals of Applied Statistics, 7(4), 2205-2228.
Hardee, J., Ouyang, Z., Zhang, Y., Kundaje, A., Lacroute, P., and Snyder, M. (2013). STAT3 targets suggest mechanisms of aggressive tumorigenesis in diffuse large B-cell lymphoma. G3: Genes Genomes Genetics, 3(12), 2173-2185.
Zhang, Y., Tibshirani, R., Davis, R.W. (2013) Classification of patients from time-course gene expression. Biostatistics, 14(1), 87-98. Epub 2012 Aug 27.
Jia, C., Liu, X. F., Qian, M. P., Jiang, D. Q., and Zhang, Y. (2012). Kinetic behavior of the general modifier mechanism of Botts and Morales with non-equilibrium binding. Journal of Theoretical Biology, 296, 13-20.
Xiao, W., Mindrinos, M.N., Seok, J., Cuschieri, J., Cuenca, A.G., Gao, H., Hayden, D.L., Hennessy, L., Moore, E.E., Minei, J.P., Bankey, P.E., Johnson, J.L., Sperry, J., Nathens, A.B., Billiar, T.R., West, M.A., Brownstein, B.H., Mason, P.H., Baker, H.V., Finnerty17, C.C., Jeschke, M.G., Lpez, M.C., Klein, M.B., Gamelli, R.L., Gibran, N.S., Arnoldo, B., Xu, W., Zhang, Y., Calvano, S.E., McDonald-Smith, G.P., Schoenfeld, D.A., Storey, J.D., Cobb, J.P., Warren, H.S., Moldawer, L.L., Herndon, D.N., Lowry, S.F., Maier, R.V., Davis, R.W. and Tompkins, R.G., and the Inflammation and Host Response to Injury Large-Scale Collaborative Research Program (2011). A genomic storm in critically injured humans. The Journal of Experimental Medicine, 208(13), 2581-2590.
Li, Z., Ni, M., Li, J., Zhang, Y., Ouyang, Q., and Tang, C. (2011). Decision making of the p53 network: Death by integration. Journal of Theoretical Biology, 271(1), 205-211.
Zhang, Y., Tibshirani, R., Davis, R.W. (2010) Predicting patient survival from longitudinal gene expression. Statistical Applications in Genetics and Molecular Biology, 9(1): 41.
Zhang, Y., Qian, M.P. (2007) The stochastic model and metastability of gene network. Networks: from biology to theory, edited by Feng, J., and Jost, J. London: Springer Verlag, 270-290.
Wang, S.†, Zhang, Y.†, and Ouyang, Q. (2006). Stochastic model of coliphage lambda regulatory network. Physical Review E, 73(4), 041922. († joint first authors)
Zhang, Y., Yu, H., Deng, M., Qian, M.P. (2006) Nonequilibrium model for yeast cell cycle. Lecture Notes in Computer Science (LNCS) 4115:786-791. London: Springer Verlag.
Zhang, Y., Qian, M.P., Ouyang, Q., Deng, M., Li, F., Tang, C. (2006) Stochastic model of yeast cell-cycle network. Physica D: Nonlinear Phenomena, 219: 35-39.