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Title: DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification
Author: Decamps, Clémentine
Arnaud, Alexis
Petitprez, Florent
Ayadi, Mira
Baurès, Aurélia
Armenoult, Lucile
Escalera Guerrero, Sergio
Guyon, Isabelle
Nicolle, Rémy
Tomasini, Richard
Reyniès, Aurélien de
Cros, Jérôme
Blum, Yuna
Richard, Magali
Keywords: Càncer
Classificació de tumors
Algorismes computacionals
Tumors classification
Computer algorithms
Issue Date: 2-Oct-2021
Publisher: BioMed Central
Abstract: Quantifcation 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.
Note: Reproducció del document publicat a:
It is part of: BMC Bioinformatics, 2021, vol. 2021, num. 22
Related resource:
ISSN: 1471-2105
Appears in Collections:Articles publicats en revistes (Matemàtiques i Informàtica)
Publicacions de projectes de recerca finançats per la UE

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