Inferring of regulatory networks from expression data using Bayesian networks

Inferring Regulatory Networks from Expression Data Using Tree-Based Methods

One of the pressing open problems of computational systems biology is the elucidation of the topology of genetic regulatory networks (GRNs) using high throughput genomic data, in particular microarray gene expression data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) challenge aims to evaluate the success of GRN inference algorithms on benchmarks of simulated data. In th...

Inferring quantitative models of regulatory networks from expression data

MOTIVATION Genetic networks regulate key processes in living cells. Various methods have been suggested to reconstruct network architecture from gene expression data. However, most approaches are based on qualitative models that provide only rough approximations of the underlying events, and lack the quantitative aspects that are critical for understanding the proper function of biomolecular sy...

Improved Methods for Inferring Regulatory Networks from Temporal Expression Data

Over the past few years, the advent of microarray technology has enabled the simultaneous measurement of the expression levels of thousands of genes. When the expression levels of these genes are measured at multiple time points during an experiment, the result is a temporal expression profile. These expression profiles may be processed to extract the underlying gene regulatory network relation...

Inferring Gene Regulatory Networks from Gene Expression Data by a Dynamic Bayesian Network-Based Model

Enabled by recent advances in bioinformatics, the inference of gene regulatory networks (GRNs) from gene expression data has garnered much interest from researchers. This is due to the need of researchers to understand the dynamic behavior and uncover the vast information lay hidden within the networks. In this regard, dynamic Bayesian network (DBN) is extensively used to infer GRNs due to its ...

Inferring Regulatory Pathways in E. Coli using Dynami Bayesian Networks