Reconstruction of gene co-expression networks is a powerful tool for better understanding of
gene function, biological processes, and complex disease mechanisms. In essence, co …

The study of biological systems at a system level has become a reality due to the increasing
powerful computational approaches able to handle increasingly larger datasets. Uncovering …

Background: A central problem of systems biology is the reconstruction of the topology of
gene regulatory networks (GRNs) using high throughput genomic data like microarray gene …

Reconstruction of gene regulatory networks orreverse-engineering'is a process of identifying
gene interaction networks from experimental microarray gene expression profile through …

In this study, in order to deal with the noise and uncertainty in gene expression data,
learning networks, especially Bayesian networks, that have the ability to use prior …

We are in the big data era which has affected several domains including biomedicine and
healthcare. This revolution driven by the explosion of biomedical data offers the potential for …

High-throughput, genome-scale data present a unique opportunity to link host to pathogen
on a molecular level. Forging such connections will help drive the development of …

Inferring gene regulatory network (GRN) is one of the major challenges in bioinformatics. A
great amount of gene expression data is being produced raising the issue of GRN …

Gene Regulatory Network (GRN) plays an important role to understand the interactions and
dependencies of genes in different conditions from gene expression data. An information …

For understanding the complex processes of regulation within the system of cellular and
every process of life in different developmental and environmental contexts, reconstructing …