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Genomic transcription regulatory element location analysis via poisson weighted lasso

hal.structure.identifierDepartment of Electrical and Computer Engineering [Durham] [ECE]
dc.contributor.authorJiang, Xin
hal.structure.identifier
dc.contributor.authorReynaud-Bouret, Patricia
HAL ID: 8239
hal.structure.identifierCEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
dc.contributor.authorRivoirard, Vincent
hal.structure.identifier
dc.contributor.authorSansonnet, Laure
HAL ID: 15296
hal.structure.identifier
dc.contributor.authorWillett, Rebecca
dc.date.accessioned2020-06-10T12:50:19Z
dc.date.available2020-06-10T12:50:19Z
dc.date.issued2016
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/20871
dc.language.isoenen
dc.subjectDNA Transcription Regulatory Elementsen
dc.subjectPoisson Noiseen
dc.subjectWeighted LASSOen
dc.subjectGenomicsen
dc.subjectBioinformaticsen
dc.subjectInverse problemsen
dc.subjectConvolutionen
dc.subjectConferencesen
dc.subject.ddc621.3en
dc.titleGenomic transcription regulatory element location analysis via poisson weighted lassoen
dc.typeCommunication / Conférence
dc.description.abstractenThe distances between DNA Transcription Regulatory Elements (TRE) provide important clues to their dependencies and function within the gene regulation process. However, the locations of those TREs as well as their cross distances between occurrences are stochastic, in part due to the inherent limitations of Next Generation Sequencing methods used to localize them, in part due to biology itself. This paper describes a novel approach to analyzing these locations and their cross distances even at long range via a Poisson random convolution. The resulting deconvolution problem is ill-posed, and sparsity regularization is used to offset this challenge. Unlike previous work on sparse Poisson inverse problems, this paper adopts a weighted LASSO estimator with data-dependent weights calculated using concentration inequalities that account for the Poisson noise. This method exhibits better squared error performance than the classical (unweighted) LASSO both in theoretical performance bounds and in simulation studies, and can easily be computed using off-the-shelf LASSO solvers.en
dc.relation.ispartoftitleStatistical Signal Processing Workshop (SSP), 2016 IEEEen
dc.relation.ispartofpublnameIEEEen
dc.subject.ddclabelTraitement du signalen
dc.relation.conftitle2016 IEEE Statistical Signal Processing Workshop (SSP)en
dc.relation.confdate2016
dc.relation.confcityPalma de Mallorcaen
dc.relation.confcountrySpainen
dc.relation.forthcomingnonen
dc.identifier.doi10.1109/SSP.2016.7551831en
dc.description.ssrncandidatenonen
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewednonen
dc.relation.Isversionofjnlpeerreviewednonen
dc.date.updated2020-06-10T12:44:40Z
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