3 citations · 5 across the 4 of their papers we have counts for
4 papers
Directed Cyclic Graph for Causal Discovery from Multivariate Functional Data
Saptarshi Roy, Raymond K. W. Wong, Yang Ni
Discovering causal relationship using multivariate functional data has received a significant amount of attention very recently. In this article, we introduce a functional linear s…
Robust Bayesian Graphical Regression Models for Assessing Tumor Heterogeneity in Proteomic Networks
Tsung-Hung Yao, Yang Ni, Anindya Bhadra +2
Graphical models are powerful tools to investigate complex dependency structures in high-throughput datasets. However, most existing graphical models make one of the two canonical…
Covariate-Assisted Bayesian Graph Learning for Heterogeneous Data
Yabo Niu, Yang Ni, Debdeep Pati +1
In a traditional Gaussian graphical model, data homogeneity is routinely assumed with no extra variables affecting the conditional independence. In modern genomic datasets, there i…
Reciprocal Graphical Models for Integrative Gene Regulatory Network Analysis
Yang Ni, Yuan Ji, Peter Mueller
Constructing gene regulatory networks is a fundamental task in systems biology. We introduce a Gaussian reciprocal graphical model for inference about gene regulatory relationships…