liu.seSearch for publications in DiVA
Change search
ReferencesLink to record
Permanent link

Direct link
Growing Bayesian network models of gene networks from seed genes
Linköping University, Department of Computer and Information Science, Database and information techniques. (ADIT)
Björkegren, J., Center for Genomics and Bioinformatics, Karolinska Institutet, 171 77 Stockholm, Sweden.
Linköping University, The Institute of Technology. Linköping University, Department of Physics, Chemistry and Biology, Computational Biology.
2005 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1460-2059, Vol. 21, no SUPPL. 2Article in journal (Refereed) Published
Abstract [en]

Motivation: For the last few years, Bayesian networks (BNs) have received increasing attention from the computational biology community as models of gene networks, though learning them from gene-expression data is problematic. Most gene-expression databases contain measurements for thousands of genes, but the existing algorithms for learning BNs from data do not scale to such high-dimensional databases. This means that the user has to decide in advance which genes are included in the learning process, typically no more than a few hundreds, and which genes are excluded from it. This is not a trivial decision. We propose an alternative approach to overcome this problem. Results: We propose a new algorithm for learning BN models of gene networks from gene-expression data. Our algorithm receives a seed gene S and a positive integer R from the user, and returns a BN for the genes that depend on S such that less than R other genes mediate the dependency. Our algorithm grows the BN, which initially only contains S, by repeating the following step R + 1 times and, then, pruning some genes, find the parents and children of all the genes in the BN and add them to it. Intuitively, our algorithm provides the user with a window of radius R around S to look at the BN model of a gene network without having to exclude any gene in advance. We prove that our algorithm is correct under the faithfulness assumption. We evaluate our algorithm on simulated and biological data (Rosetta compendium) with satisfactory results. © The Author 2005. Published by Oxford University Press. All rights reserved.

Place, publisher, year, edition, pages
2005. Vol. 21, no SUPPL. 2
National Category
Engineering and Technology
URN: urn:nbn:se:liu:diva-50433DOI: 10.1093/bioinformatics/bti1137OAI: diva2:271329
Available from: 2009-10-11 Created: 2009-10-11 Last updated: 2013-05-14

Open Access in DiVA

No full text

Other links

Publisher's full text

Search in DiVA

By author/editor
Peña, Jose M.Tegnér, Jesper
By organisation
Database and information techniquesThe Institute of TechnologyComputational Biology
In the same journal
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 72 hits
ReferencesLink to record
Permanent link

Direct link