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dc.contributor.authorVillaverde, A. F.es_ES
dc.contributor.authorBecker, Koljaes_ES
dc.contributor.authorBanga, Julio R.es_ES
dc.date.accessioned2016-10-21T11:30:04Z-
dc.date.available2016-10-21T11:30:04Z-
dc.date.issued2016-
dc.identifier.citationComputational Methods in Systems Biology: 323-329 (2016)es_ES
dc.identifier.isbn978-3-319-45176-3-
dc.identifier.other978-3-319-45177-0-
dc.identifier.urihttp://hdl.handle.net/10261/138834-
dc.description7 páginas, 3 figuras.-- 14th International Conference, CMSB 2016, Cambridge, UK, September 21-23, 2016, Proceedingses_ES
dc.description.abstractA common approach for reverse engineering biological networks from data is to deduce the existence of interactions among nodes from information theoretic measures. Estimating these quantities in a multidimensional space is computationally demanding for large datasets. This hampers the application of elaborate algorithms – which are crucial for discarding spurious interactions and determining causal relationships – to large-scale network inference problems. To alleviate this issue we have developed PREMER, a software tool which can automatically run in parallel and sequential environments, thanks to its implementation of OpenMP directives. It recovers network topology and estimates the strength and causality of interactions using information theoretic criteria, and allowing the incorporation of prior knowledge. A preprocessing module takes care of imputing missing data and correcting outliers if needed. PREMER (https://​sites.​google.​com/​site/​premertoolbox/​) runs on Windows, Linux and OSX, it is implemented in Matlab/Octave and Fortran 90, and it does not require any commercial softwarees_ES
dc.description.sponsorshipAFV acknowledges funding from the Galician government (Xunta de Galiza) through the I2C fellowship ED481B2014/133-0. KB was supported by the German Federal Ministry of Research and Education (BMBF, OncoPath consortium). JRB acknowledges funding from the Spanish government (MINECO) and the European Regional Development Fund (ERDF) through the project \SYN- BIOFACTORY" (grant number DPI2014-55276-C5-2-R). This project has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No 686282 (CanPathPro).es_ES
dc.language.isoenges_ES
dc.publisherSpringer Naturees_ES
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/686282es_ES
dc.relationinfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2014-55276-C5-2-R-
dc.relation.ispartofseriesLecture Notes in Computer Science, v. 9859es_ES
dc.relation.isversionofPostprintes_ES
dc.rightsopenAccesses_ES
dc.subjectNetwork inferencees_ES
dc.subjectInformation theoryes_ES
dc.subjectParallel computinges_ES
dc.titlePREMER: parallel reverse engineering of biological networks with information theoryes_ES
dc.typecapítulo de libroes_ES
dc.identifier.doi10.1007/978-3-319-45177-0_21-
dc.description.peerreviewedPeer reviewedes_ES
dc.relation.publisherversionhttp://dx.doi.org/10.1007/978-3-319-45177-0_21es_ES
dc.contributor.funderEuropean Commissiones_ES
dc.contributor.funderXunta de Galicia-
dc.contributor.funderFederal Ministry of Education and Research (Germany)-
dc.contributor.funderMinisterio de Economía y Competitividad (España)-
dc.relation.csices_ES
oprm.item.hasRevisionno ko 0 false*
dc.identifier.funderhttp://dx.doi.org/10.13039/501100000780es_ES
dc.identifier.funderhttp://dx.doi.org/10.13039/501100003329es_ES
dc.identifier.funderhttp://dx.doi.org/10.13039/501100002347es_ES
dc.identifier.funderhttp://dx.doi.org/10.13039/501100010801es_ES
dc.type.coarhttp://purl.org/coar/resource_type/c_3248es_ES
item.fulltextWith Fulltext-
item.openairetypecapítulo de libro-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.grantfulltextopen-
item.languageiso639-1en-
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