5 citations · 5 across the 4 of their papers we have counts for
6 papers · 1 filter
Functional Bayesian Networks for Discovering Causality from Multivariate Functional Data
Fangting Zhou, Kejun He, Kunbo Wang +2
Multivariate functional data arise in a wide range of applications. One fundamental task is to understand the causal relationships among these functional objects of interest, which…
Causal Discovery with Heterogeneous Observational Data
Fangting Zhou, Kejun He, Yang Ni
We consider the problem of causal discovery (structure learning) from heterogeneous observational data. Most existing methods assume a homogeneous sampling scheme, which leads to m…
Phylogenetically informed Bayesian truncated copula graphical models for microbial association networks
Hee Cheol Chung, Irina Gaynanova, Yang Ni
Microorganisms play a critical role in host health. The advancement of high-throughput sequencing technology provides opportunities for a deeper understanding of microbial interact…
BAGEL: A Bayesian Graphical Model for Inferring Drug Effect Longitudinally on Depression in People with HIV
Yuliang Li, Yang Ni, Leah H. Rubin +2
Access and adherence to antiretroviral therapy (ART) has transformed the face of HIV infection from a fatal to a chronic disease. However, ART is also known for its side effects. S…
Bayesian biclustering for microbial metagenomic sequencing data via multinomial matrix factorization
Fangting Zhou, Kejun He, Qiwei Li +2
High-throughput sequencing technology provides unprecedented opportunities to quantitatively explore human gut microbiome and its relation to diseases. Microbiome data are composit…
A Bayesian Nonparametric Approach for Inferring Drug Combination Effects on Mental Health in People with HIV
Wei Jin, Yang Ni, Leah H. Rubin +2
Although combination antiretroviral therapy (ART) is highly effective in suppressing viral load for people with HIV (PWH), many ART agents may exacerbate central nervous system (CN…