Motivation Differential network analysis is an important tool to investigate the rewiring of
gene interactions under different conditions. Several computational methods have been …

In Gaussian graphical models, the zero entries in the precision matrix determine the
dependence structure, so estimating that sparse precision matrix and, thereby, learning this …

Reconstructing gene networks from gene expression data is a long-standing challenge. In
most applications, the observations can be divided into several distinct but related …

To identify biological network rewiring under different conditions, we develop a user-friendly
R package, named DiffNetFDR, to implement two methods developed for testing the …

Background During transcription, numerous transcription factors (TFs) bind to targets in a
highly coordinated manner to control the gene expression. Alterations in groups of TF …

Abstract Motivation Gaussian graphical models (GGMs) are network representations of
random variables (as nodes) and their partial correlations (as edges). GGMs overcome the …

Heterogeneity is a hallmark of many complex human diseases, and unsupervised
heterogeneity analysis has been extensively conducted using high-throughput molecular …

Many biological and biomedical research areas such as drug design require analyzing the
Gene Regulatory Networks (GRNs) to provide clear insight and understanding of the cellular …

Motivation Reconstruction of cancer gene networks from gene expression data is important
for understanding the mechanisms underlying human cancer. Due to heterogeneity, the …

Cancer is generally caused by genetic mutations, and differentially expressed genes are
closely associated with genetic mutations. Therefore, mutated genes and differentially …