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| Author | Clementine Decamps; Alexis Arnaud; Florent Petitprez; Mira Ayadi; Aurelia Baures; Lucile Armenoult; Sergio Escalera; Isabelle Guyon; Remy Nicolle; Richard Tomasini; Aurelien de Reynies; Jerome Cros; Yuna Blum; Magali Richard | ||||
| Title | DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification | Type | Journal Article | ||
| Year | 2021 | Publication | BMC Bioinformatics | Abbreviated Journal  |
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| Volume | 22 | Issue | Pages | 473 | |
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| Abstract | Quantification of tumor heterogeneity is essential to better understand cancer progression and to adapt therapeutic treatments to patient specificities. Bioinformatic tools to assess the different 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. | ||||
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| Notes | HUPBA; no proj;MILAB | Approved | no | ||
| Call Number | Admin @ si @ DAP2021 | Serial | 3650 | ||
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