Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/184089
Full metadata record
DC FieldValueLanguage
dc.contributor.authorDecamps, Clémentine-
dc.contributor.authorArnaud, Alexis-
dc.contributor.authorPetitprez, Florent-
dc.contributor.authorAyadi, Mira-
dc.contributor.authorBaurès, Aurélia-
dc.contributor.authorArmenoult, Lucile-
dc.contributor.authorEscalera Guerrero, Sergio-
dc.contributor.authorGuyon, Isabelle-
dc.contributor.authorNicolle, Rémy-
dc.contributor.authorTomasini, Richard-
dc.contributor.authorReyniès, Aurélien de-
dc.contributor.authorCros, Jérôme-
dc.contributor.authorBlum, Yuna-
dc.contributor.authorRichard, Magali-
dc.date.accessioned2022-03-14T09:36:21Z-
dc.date.available2022-03-14T09:36:21Z-
dc.date.issued2021-10-02-
dc.identifier.issn1471-2105-
dc.identifier.urihttp://hdl.handle.net/2445/184089-
dc.description.abstractQuantifcation of tumor heterogeneity is essential to better understand cancer progression and to adapt therapeutic treatments to patient specifcities. Bioinformatic tools to assess the diferent cell populations from single-omic datasets as bulk transcriptome or methylome samples have been recently developed, including reference-based and reference-free methods. Improved methods using multi-omic datasets are yet to be developed in the future and the community would need systematic tools to perform a comparative evaluation of these algorithms on controlled data.-
dc.format.mimetypeapplication/pdf-
dc.language.isoeng-
dc.publisherBioMed Central-
dc.relation.isformatofReproducció del document publicat a: https://doi.org/10.1186/s12859-021-04381-4-
dc.relation.ispartofBMC Bioinformatics, 2021, vol. 2021, num. 22-
dc.relation.urihttps://doi.org/10.1186/s12859-021-04381-4-
dc.rightscc-by (c) Decamps, Clémentine et al., 2021-
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/-
dc.sourceArticles publicats en revistes (Matemàtiques i Informàtica)-
dc.subject.classificationCàncer-
dc.subject.classificationClassificació de tumors-
dc.subject.classificationAlgorismes computacionals-
dc.subject.otherCancer-
dc.subject.otherTumors classification-
dc.subject.otherComputer algorithms-
dc.titleDECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification-
dc.typeinfo:eu-repo/semantics/article-
dc.typeinfo:eu-repo/semantics/publishedVersion-
dc.identifier.idgrec714544-
dc.date.updated2022-03-14T09:36:22Z-
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/826121/EU//iPC-
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess-
Appears in Collections:Articles publicats en revistes (Matemàtiques i Informàtica)
Publicacions de projectes de recerca finançats per la UE

Files in This Item:
File Description SizeFormat 
714544.pdf2.22 MBAdobe PDFView/Open


This item is licensed under a Creative Commons License Creative Commons