We have long standing interest to infer biological networks from omics
data and integrate the inferred network information for disease mechanism
and treatment studies. We cover theoretical and methodological aspects of
network reconstructions, and their applications to disease mechanism studies.
[1]K.-Y. Lee, D. Ji, L. Li, T. Constable, H. Zhao (2023) Conditional functional graphical models. Journal of American Statistical Association, 118: 257-271.
[2] K.-Y. Lee, L. Li, B. Li, H. Zhao (2022) Nonparametric functional graphical modeling through functional additive regression operator. Journal of American Statistical Association, in press.
[3] K.-Y. Lee, D. Ji, L. Li, T. Constable, H. Zhao (2022) Conditional functional graphical models. Journal of American Statistical Association, in press.
[4] K-Y Lee, T. Liu, B. Li, H. Zhao (2020) Learning causal networks via additive faithfulness. Journal of Machine Learning Research, 21: 1−38.
[5] Z. Lin, T. Wang, C. Yang, H. Zhao (2017) On joint estimation of Gaussian graphical models for spatial and temporal data. Biometrics, 73: 769-779.
[6] Y. Hu, H. Zhao (2016) CCor: a whole genome network-based similarity measure between two genes. Biometrics, 72: 1216-1225.
[7] K. Lee, B. Li, H. Zhao (2016) Variable selection via additive conditional independence. Journal of the Royal Statistical Society - Series B, 78: 1037-1055.
[8] B. Li, H. Chun, H. Zhao (2014) On an additive semi-graphoid model for statistical networks with application to pathway analysis. Journal of American Statistical Association, 109: 1188-1204.
[9] B. Li, H. Chun, H. Zhao (2012) Sparse estimation of conditional graphical models with application to gene networks. Journal of American Statistical Association, 107: 152-167.
© 2022 Hongyu Zhao, Ph.D.
Created by Eddie, Chen and Wangjie.