The development of high-throughput RNA sequencing (RNA-seq) at the single-cell level has
already led to profound new discoveries in biology, ranging from the identification of novel …

Omics data contain signals from the molecular, physical, and kinetic inter-and intracellular
interactions that control biological systems. Matrix factorization (MF) techniques can reveal …

Abstract Background COnstraint-Based Reconstruction and Analysis (COBRA) methods are
widely used for genome-scale modeling of metabolic networks in both prokaryotes and …

Biological processes are often dynamic, thus researchers must monitor their activity at
multiple time points. The most abundant source of information regarding such dynamic …

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 …

We propose 'trans-omic'analysis for reconstructing global biochemical networks across
multiple omic layers by use of both multi-omic measurements and computational data …

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

The merging of network theory and microarray data analysis techniques has spawned a new
field: gene coexpression network analysis. While network methods are increasingly used in …

The latest advances in high-throughput techniques during the past decade allowed the
systems biology field to expand significantly. Today, the focus of biologists has shifted from …

Network inference, which is the reconstruction of biological networks from high-throughput
data, can provide valuable information about the regulation of gene expression in cells …