Query-based biclustering of gene expression data using Probabilistic Relational Models.

Content Provider	Europe PMC
Author	Zhao, Hui Cloots, Lore Van den Bulcke, Tim Wu, Yan De Smet, Riet Storms, Valerie Meysman, Pieter Engelen, Kristof Marchal, Kathleen
Copyright Year	2011
Abstract	BackgroundWith the availability of large scale expression compendia it is now possible to view own findings in the light of what is already available and retrieve genes with an expression profile similar to a set of genes of interest (i.e., a query or seed set) for a subset of conditions. To that end, a query-based strategy is needed that maximally exploits the coexpression behaviour of the seed genes to guide the biclustering, but that at the same time is robust against the presence of noisy genes in the seed set as seed genes are often assumed, but not guaranteed to be coexpressed in the queried compendium. Therefore, we developed ProBic, a query-based biclustering strategy based on Probabilistic Relational Models (PRMs) that exploits the use of prior distributions to extract the information contained within the seed set.ResultsWe applied ProBic on a large scale Escherichia coli compendium to extend partially described regulons with potentially novel members. We compared ProBic's performance with previously published query-based biclustering algorithms, namely ISA and QDB, from the perspective of bicluster expression quality, robustness of the outcome against noisy seed sets and biological relevance.This comparison learns that ProBic is able to retrieve biologically relevant, high quality biclusters that retain their seed genes and that it is particularly strong in handling noisy seeds.ConclusionsProBic is a query-based biclustering algorithm developed in a flexible framework, designed to detect biologically relevant, high quality biclusters that retain relevant seed genes even in the presence of noise or when dealing with low quality seed sets.
Journal	BMC Bioinformatics
Volume Number	12 Suppl 1
PubMed Central reference number	PMC3044293
Issue Number	Suppl 1
PubMed reference number	21342568
e-ISSN	14712105
DOI	10.1186/1471-2105-12-s1-s37
Language	English
Publisher	BioMed Central
Publisher Date	2011-02-15
Access Restriction	Open
Rights License	This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Copyright ©2011 Zhao et al; licensee BioMed Central Ltd.
Content Type	Text
Resource Type	Article
Subject	Biochemistry Molecular Biology Applied Mathematics Structural Biology Computer Science Applications