<p>As an example, DM_BN takes as input deletion mutant gene expression profiles. The relative change of the mRNA expression levels of the deletion mutant strains versus the wild type (WT) is represented by 1 (significant up-regulation), −1 (significant down-regulation) and 0 (no significant change). The algorithm incorporate a new kernel to model the consistent gene expression changes upon perturbation and it employs a template of all potential regulator-regulator interactions to enable more accurate and much faster BN learning. After learning the BN structure, Meek's rule <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003757#pgen.1003757-Meek1" target="_blank">[28]</a> is used to infer compelled (directed) and ...
In recent years, we have seen an increased interest in applications of Bayesian Networks (BNs) in mo...
An important problem in systems biology is the inference of biochemical pathways and regulatory net...
Abstract: To understand most cellular processes, one must understand how genetic information is proc...
Genome-wide gene expression profiles accumulate at an alarming rate, how to integrate these expressi...
Recent advances in high-throughput molecular biology has motivated in the field of bioinformatics th...
Microarray experiments generate vast amounts of data that evidently reflect many aspects of the unde...
International audienceBACKGROUND: Inferring gene regulatory networks from data requires the developm...
Phenotypic traits are now known to stem from the interplay between genetic variables across many if ...
Reconstructing gene regulatory networks (GRNs) from gene expression data is a challenging problem. E...
This paper provides a brief introduction to learning Bayesian networks from gene-expression data. Th...
National audienceIn this work, we reconstruct the gene regulation networks from the microarray exper...
Background: Dynamic Bayesian Network (DBN) is an approach widely used for reconstruction of gene reg...
Gene regulatory network is a model of a network that describes the relationships among genes in a gi...
The Dialogue for Reverse Engineering Assessments and Methods (DREAM) project was initiated in 2006 a...
Inferring gene regulatory networks from data requires the development of algorithms devoted to struc...
In recent years, we have seen an increased interest in applications of Bayesian Networks (BNs) in mo...
An important problem in systems biology is the inference of biochemical pathways and regulatory net...
Abstract: To understand most cellular processes, one must understand how genetic information is proc...
Genome-wide gene expression profiles accumulate at an alarming rate, how to integrate these expressi...
Recent advances in high-throughput molecular biology has motivated in the field of bioinformatics th...
Microarray experiments generate vast amounts of data that evidently reflect many aspects of the unde...
International audienceBACKGROUND: Inferring gene regulatory networks from data requires the developm...
Phenotypic traits are now known to stem from the interplay between genetic variables across many if ...
Reconstructing gene regulatory networks (GRNs) from gene expression data is a challenging problem. E...
This paper provides a brief introduction to learning Bayesian networks from gene-expression data. Th...
National audienceIn this work, we reconstruct the gene regulation networks from the microarray exper...
Background: Dynamic Bayesian Network (DBN) is an approach widely used for reconstruction of gene reg...
Gene regulatory network is a model of a network that describes the relationships among genes in a gi...
The Dialogue for Reverse Engineering Assessments and Methods (DREAM) project was initiated in 2006 a...
Inferring gene regulatory networks from data requires the development of algorithms devoted to struc...
In recent years, we have seen an increased interest in applications of Bayesian Networks (BNs) in mo...
An important problem in systems biology is the inference of biochemical pathways and regulatory net...
Abstract: To understand most cellular processes, one must understand how genetic information is proc...