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dc.contributor.authorBellot Pujalte, Pau
dc.contributor.authorSalembier Clairon, Philippe Jean
dc.contributor.authorOliveras Vergés, Albert
dc.contributor.authorMeyer, Patrick
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.date.accessioned2016-02-18T15:00:19Z
dc.date.issued2015
dc.identifier.citationBellot, P., Salembier, P., Oliveras, A., Meyer, P. Study of normalization and aggregation approaches for consensus network estimation. A: IEEE Symposium Series on Computational Intelligence. "IEEE SSCI 2015: 2015 IEEE Symposium Series on Computational Intelligence; 7-10 December 2015, Cape Town, South Afrika". Cape Town: Institute of Electrical and Electronics Engineers (IEEE), 2015, p. 1-6.
dc.identifier.isbn978-1-4799-7560-0
dc.identifier.urihttp://hdl.handle.net/2117/83130
dc.description.abstractInferring gene regulatory networks from expression data is a very difficult 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 the 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 paper, 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 the 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.extent6 p.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació
dc.subject.lcshComputer network
dc.titleStudy of normalization and aggregation approaches for consensus network estimation
dc.typeConference report
dc.subject.lemacOrdinadors, Xarxes d'
dc.contributor.groupUniversitat Politècnica de Catalunya. GPI - Grup de Processament d'Imatge i Vídeo
dc.description.peerreviewedPeer Reviewed
dc.rights.accessRestricted access - publisher's policy
local.identifier.drac17431918
dc.description.versionPostprint (published version)
dc.date.lift10000-01-01
local.citation.authorBellot, P.; Salembier, P.; Oliveras, A.; Meyer, P.
local.citation.contributorIEEE Symposium Series on Computational Intelligence
local.citation.pubplaceCape Town
local.citation.publicationNameIEEE SSCI 2015: 2015 IEEE Symposium Series on Computational Intelligence; 7-10 December 2015, Cape Town, South Afrika
local.citation.startingPage1
local.citation.endingPage6


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