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• W2113292893 abstract "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 Pro Bic, 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 Pro Bic on a large scale Escherichia coli compendium to extend partially described regulons with potentially novel members. We compared Pro Bic'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 Pro Bic 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 Pro Bic 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." @default.
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• W2113292893 date "2011-02-15" @default.
• W2113292893 modified "2023-10-09" @default.
• W2113292893 title "Query-based biclustering of gene expression data using Probabilistic Relational Models" @default.
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• W2113292893 doi "https://doi.org/10.1186/1471-2105-12-s1-s37" @default.
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