Please use this identifier to cite or link to this item:
http://hdl.handle.net/2445/184089
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 Cancer 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: https://doi.org/10.1186/s12859-021-04381-4 |
It is part of: | BMC Bioinformatics, 2021, vol. 2021, num. 22 |
URI: | http://hdl.handle.net/2445/184089 |
Related resource: | https://doi.org/10.1186/s12859-021-04381-4 |
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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