In the recent years, the vast amount of genetic information generated by new-generation
approaches, have led to the need of new data handling methods. The integrative analysis of …

Gene regulatory networks (GRNs) serve as useful abstractions to understand transcriptional
dynamics in developmental systems. Computational prediction of GRNs has been …

With the recent availability of tissue-specific gene expression data, eg, provided by the GTEx
Consortium, there is interest in comparing gene co-expression patterns across tissues. One …

One of the key challenges in systems biology is to derive gene regulatory networks (GRNs)
from complex high-dimensional sparse data. Bayesian networks (BNs) and dynamic …

Results In this study, we proposed a novel network denoising method named REverse
Network Diffusion On Random walks (RENDOR). RENDOR is designed to enhance the …

Gene regulatory networks (GRNs) research reveals complex life phenomena from the
perspective of gene interaction, which is an important research field in systems biology …

Reconstructing gene regulatory networks (GRNs) is an increasingly hot topic in
bioinformatics. Dynamic Bayesian network (DBN) is a stochastic graph model commonly …

Physiological RNA dynamics cause problems in transcriptome analysis. Physiological RNA
accumulation affects the analysis of RNA quantification, and physiological RNA degradation …

Background Relatively small changes to gene expression data dramatically affect co-
expression networks inferred from that data which, in turn, can significantly alter the …

Mammalian genomes harbor many more enhancers than genes, which greatly complicates
the elucidation of cell-state-specific regulatory networks. Here, we developed a …