Area of research
Molecular Biology · Statistics and Probability
Research interest
Research interests include Gene expression and cancer classification, Statistical Methods and Inference, Bioinformatics and Genomic Networks, and Statistical Methods and Bayesian Inference.
Integrating Omics and Pathological Imaging Data for Cancer Prognosis via a Deep Neural Network-Based Cox Model.
DNN-based semiparametric AFT model for integrating genomic and pathological imaging data in cancer prognosis.
Robust Heterogeneity Adjustment for Gaussian Graphical Model With Latent Variables.
Bayesian Modeling of Cancer Outcomes Using Genetic Variables Assisted by Pathological Imaging Data.
Hierarchical Multi-Label Classification With Gene-Environment Interactions in Disease Modeling.
Ordinal Sparse Neural Networks for Modeling Gene- and Imaging-Environment Interactions.
Analysis of cross-platform health communication with a network approach.
Incorporating prior information in gene expression network-based cancer heterogeneity analysis.
Robust Transfer Learning for High-Dimensional GLM Using $$ \gamma $$ -Divergence With Applications to Cancer Genomics.
Subgroup Testing in the Change-Plane Cox Model.
GE-IA-NAM: gene-environment interaction analysis via imaging-assisted neural additive model.
Robust sparse Bayesian regression for longitudinal gene-environment interactions.
Joint modeling of mixed outcomes using a rank-based sparse neural network
HEARTSVG: a fast and accurate method for identifying spatially variable genes in large-scale spatial transcriptomics.
The spike-and-slab quantile LASSO for robust variable selection in cancer genomics studies.
Information-incorporated sparse hierarchical cancer heterogeneity analysis.
SARS-CoV-2 mRNA vaccines decouple anti-viral immunity from humoral autoimmunity
Robust Bayesian variable selection for gene-environment interactions.
Gene-environment interaction analysis via deep learning.
Prior information-assisted integrative analysis of multiple datasets.
Pathological imaging-assisted cancer gene-environment interaction analysis.
Human disease clinical treatment network for the elderly: analysis of the medicare inpatient length of stay and readmission data.
Two-level Bayesian interaction analysis for survival data incorporating pathway information.
Bi-level structured functional analysis for genome-wide association studies.
Aligned deep neural network for integrative analysis with high-dimensional input
A General Framework for Identifying Hierarchical Interactions and Its Application to Genomics Data
Survival Mixed Membership Blockmodel
FunctanSNP: an R package for functional analysis of dense SNP data (with interactions).
Unsupervised and Semisupervised Heterogeneity Analysis Based on Gaussian Graphical Models
Collaborative Research: Integrating Multi-Dimensional Omics Data for Quantifying Disease Heterogeneity
Collaborative Research: Novel methods for pharmacogenomic data analysis using gene clusters
Collaborative Proposal: Novel Semiparametric Two-part Models: New Theories and Applications