title: Query-based biclustering of gene expression data using Probabilistic Relational Models.
abstract: BACKGROUND
  With 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.
  
  
  RESULTS
  We 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.
  
  
  CONCLUSIONS
  ProBic 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.
authors.affiliation: Microbial and Molecular Systems, KU Leuven, Leuven 3001, Belgium. hui.zhao@biw.kuleuven.be
authors.forename: Hui
authors.initials: H
authors.lastname: Zhao
authors.forename: Lore
authors.initials: L
authors.lastname: Cloots
authors.forename: Tim
authors.initials: T
authors.lastname: Van den Bulcke
authors.forename: Yan
authors.initials: Y
authors.lastname: Wu
authors.forename: Riet
authors.initials: R
authors.lastname: De Smet
authors.forename: Valerie
authors.initials: V
authors.lastname: Storms
authors.forename: Pieter
authors.initials: P
authors.lastname: Meysman
authors.forename: Kristof
authors.initials: K
authors.lastname: Engelen
authors.forename: Kathleen
authors.initials: K
authors.lastname: Marchal
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D030541:Databases, Genetic; D004926:Escherichia coli / Q000235:genetics; D020869:Gene Expression Profiling / Q000379:methods*; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis; D018085:Regulon