ArticleBMC bioinformatics2021
DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- Hydrogel Based on Decellularized Bovine Trabecular Extracellular Matrix Enriched with Type I Collagen.ACS omega · 2025Article
- DECEPTICON: a correlation-based strategy for RNA-seq deconvolution inspired by a variation of the Anna Karenina principle.Briefings in bioinformatics · 2025Article
- Community assessment of methods to deconvolve cellular composition from bulk gene expression.Nature communications · 2024Article
- Challenges and perspectives in computational deconvolution of genomics data.Nature methods · 2024Review
- Decomprolute is a benchmarking platform designed for multiomics-based tumor deconvolution.Cell reports methods · 2024Article
- Codabench: Flexible, easy-to-use, and reproducible meta-benchmark platform.Patterns (New York, N.Y.) · 2022Article
Corrections and comments
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Authors and funding
15 authors at 8 institutions in 2 countries.
Funding
Abstract
backgroundQuantification 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.
resultsWe present DECONbench, a standardized unbiased benchmarking resource, applied to the evaluation of computational methods quantifying cell-type heterogeneity in cancer. DECONbench includes gold standard simulated benchmark datasets, consisting of transcriptome and methylome profiles mimicking pancreatic adenocarcinoma molecular heterogeneity, and a set of baseline deconvolution methods (reference-free algorithms inferring cell-type proportions). DECONbench performs a systematic performance evaluation of each new methodological contribution and provides the possibility to publicly share source code and scoring.
conclusionDECONbench allows continuous submission of new methods in a user-friendly fashion, each novel contribution being automatically compared to the reference baseline methods, which enables crowdsourced benchmarking. DECONbench is designed to serve as a reference platform for the benchmarking of deconvolution methods in the evaluation of cancer heterogeneity. We believe it will contribute to leverage the benchmarking practices in the biomedical and life science communities. DECONbench is hosted on the open source Codalab competition platform. It is freely available at: https://competitions.codalab.org/competitions/27453 .
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.