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dc.contributor.authorBellot, Pau
dc.contributor.authorSalembier Clairon, Philippe Jean
dc.contributor.authorPham, Ngoc C.
dc.contributor.authorMeyer, Patrick E.
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.date.accessioned2020-02-05T18:40:37Z
dc.date.issued2019
dc.identifier.citationBellot, P. [et al.]. Unsupervised GRN Ensemble. A: "Gene regulatory networks: methods and protocols". Berlín: Springer, 2019, p. 283-302.
dc.identifier.isbn978-1-4939-8881-5
dc.identifier.urihttp://hdl.handle.net/2117/176898
dc.description.abstractInferring gene regulatory networks from expression data is a very challenging problem that has raised the interest of the scientific community. Different algorithms have been proposed to try to solve this issue, but it has been shown that different methods have some particular biases and strengths, and none of them is the best across all types of data and datasets. As a result, the idea of aggregating various network inferences through a consensus mechanism naturally arises. In this chapter, a common framework to standardize already proposed consensus methods is presented, and based on this framework different proposals are introduced and analyzed in two different scenarios: Homogeneous and Heterogeneous. The first scenario reflects situations where the networks to be aggregated are rather similar because they are obtained with inference algorithms working on the same data, whereas the second scenario deals with very diverse networks because various sources of data are used to generate the individual networks. A procedure for combining multiple network inference algorithms is analyzed in a systematic way. The results show that there is a very significant difference between these two scenarios, and that the best way to combine networks in the Heterogeneous scenario is not the most commonly used. We show in particular that aggregation in the Heterogeneous scenario can be very beneficial if the individual networks are combined with our new proposed method ScaleLSum.
dc.format.extent20 p.
dc.language.isoeng
dc.publisherSpringer
dc.subjectÀrees temàtiques de la UPC::Informàtica::Informàtica teòrica::Algorísmica i teoria de la complexitat
dc.subject.lcshDevelopmental genetics
dc.subject.lcshGene expression
dc.subject.otherConsensus network algorithms
dc.subject.otherMeta-analysis
dc.subject.otherGene regulatory networks
dc.subject.otherGene expression data
dc.titleUnsupervised GRN Ensemble
dc.typePart of book or chapter of book
dc.subject.lemacGenètica del desenvolupament
dc.subject.lemacExpressió gènica
dc.contributor.groupUniversitat Politècnica de Catalunya. GPI - Grup de Processament d'Imatge i Vídeo
dc.identifier.doi10.1007/978-1-4939-8882-2
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://link.springer.com/book/10.1007/978-1-4939-8882-2
dc.rights.accessRestricted access - publisher's policy
local.identifier.drac26697568
dc.description.versionPostprint (published version)
dc.date.lift10000-01-01
local.citation.authorBellot, P.; Salembier, P.; Pham, N C.; Meyer, P. E.
local.citation.pubplaceBerlín
local.citation.publicationNameGene regulatory networks: methods and protocols
local.citation.startingPage283
local.citation.endingPage302


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