Inferring regulatory networks from expression data using tree-based methods
published: Nov. 8, 2010, recorded: October 2010, views: 5062
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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 [11–13]. In this article, we present a new algorithm for the inference of GRNs that was best performer in the DREAM4 In Silico Multifactorial challenge3. In addition, we show that the algorithm compares favorably with existing algorithms to decipher the genetic regulatory network of Escherichia coli. It doesn’t make any assumption about the nature of gene regulation, can deal with combinatorial and non-linear interactions, produces directed GRNs, and is fast and scalable.
An extended version of this works appears in . Our software is freely available from http://www.montefiore.ulg.ac.be/~huynh-thu/software.html.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !