10 citations · 18 across the 13 of their papers we have counts for
13 papers
Local spectral clustering for heterogeneous clustering structures
Yuanxing Chen, Qingzhao Zhang, Yuhong Yang
Classical clustering methods typically assume that all informative features support a single latent partition of the observations. This assumption can be overly restrictive for mod…
Adaptive Multi-Prior Lasso for High-Dimensional Generalized Linear Models
Fuzhi Xu, Weijuan Liang, Shuangge Ma +1
Incorporation of external information into high-dimensional modeling for gene expression data has been shown, both theoretically and empirically, to substantially enhance performan…
Hierarchical False Discovery Rate Control for High-dimensional Survival Analysis with Interactions
Weijuan Liang, Qingzhao Zhang, Shuangge Ma
With the development of data collection techniques, analysis with a survival response and high-dimensional covariates has become routine. Here we consider an interaction model, whi…
Regulation-incorporated Gene Expression Network-based Heterogeneity Analysis
Rong Li, Qingzhao Zhang, Shuangge Ma
Gene expression-based heterogeneity analysis has been extensively conducted. In recent studies, it has been shown that network-based analysis, which takes a system perspective and…
Locally sparse quantile estimation for a partially functional interaction model
Weijuan Liang, Qingzhao Zhang, Shuangge Ma
Functional data analysis has been extensively conducted. In this study, we consider a partially functional model, under which some covariates are scalars and have linear effects, w…
Heterogeneity-aware Clustered Distributed Learning for Multi-source Data Analysis
Yuanxing Chen, Qingzhao Zhang, Shuangge Ma +1
In diverse fields ranging from finance to omics, it is increasingly common that data is distributed and with multiple individual sources (referred to as ``clients'' in some studies…
Integrative Sparse Partial Least Squares
Weijuan Liang, Shuangge Ma, Qingzhao Zhang +1
Partial least squares, as a dimension reduction method, has become increasingly important for its ability to deal with problems with a large number of variables. Since noisy variab…
Robust Identification of Gene-Environment Interactions under High-Dimensional Accelerated Failure Time Models
Qingzhao Zhang, Hao Chai, Shuangge Ma
For complex diseases, beyond the main effects of genetic (G) and environmental (E) factors, gene-environment (G-E) interactions also play an important role. Many of the existing G-…
Structured gene-environment interaction analysis
Mengyun Wu, Qingzhao Zhang, Shuangge Ma
For the etiology, progression, and treatment of complex diseases, gene-environment (G-E) interactions have important implications beyond the main G and E effects. G-E interaction a…
Identifying Gene-Environment Interactions with A Least Relative Error Approach
Yangguang Zang, Yinjun Zhao, Qingzhao Zhang +3
For complex diseases, the interactions between genetic and environmental risk factors can have important implications beyond the main effects. Many of the existing interaction anal…
A Robust Approach for Identifying Gene-Environment Interactions for Prognosis
Hao Chai, Qingzhao Zhang, Yu Jiang +3
For many complex diseases, prognosis is of essential importance. It has been shown that, beyond the main effects of genetic (G) and environmental (E) risk factors, the gene-environ…
Promoting Similarity of Sparsity Structures in Integrative Analysis with Penalization
Yuan Huang, Qingzhao Zhang, Sanguo Zhang +2
For data with high-dimensional covariates but small to moderate sample sizes, the analysis of single datasets often generates unsatisfactory results. The integrative analysis of mu…
Penalized integrative analysis under the accelerated failure time model
Qingzhao Zhang, Sanguo Zhang, Jin Liu +2
For survival data with high-dimensional covariates, results generated in the analysis of a single dataset are often unsatisfactory because of the small sample size. Integrative ana…