Network Medicine applies network science approaches to investigate disease
pathogenesis. Many different analytical methods have been used to infer relevant molecular …

High-throughput genome-wide molecular assays, which probe cellular networks from
different perspectives, have become central to molecular biology. Probabilistic graphical …

This book unifies and extends latent variable models, including multilevel or generalized
linear mixed models, longitudinal or panel models, item response or factor models, latent …

Machine learning approaches offer the potential to systematically identify transcriptional
regulatory interactions from a compendium of microarray expression profiles. However …

Much of a cell's activity is organized as a network of interacting modules: sets of genes
coregulated to respond to different conditions. We present a probabilistic method for …

Systems biology aims to develop mathematical models of biological systems by integrating
experimental and theoretical techniques. During the last decade, many systems biological …

The complexity of cellular gene, protein, and metabolite networks can hinder attempts to
elucidate their structure and function. To address this problem, we used systematic …

Motivation: Network inference algorithms are powerful computational tools for identifying
putative causal interactions among variables from observational data. Bayesian network …

Regulatory network reconstruction is a fundamental problem in computational biology.
There are significant limitations to such reconstruction using individual datasets, and …

Motivation: Bayesian networks have been applied to infer genetic regulatory interactions
from microarray gene expression data. This inference problem is particularly hard in that …