Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Computed Tomography

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

Deconvolution

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Emergency integrative supportive care program for frail patients with advanced pancreatic cancer: A prospective GERCOR ARCAD study.

medRxiv : the preprint server for health sciences·2026

Same author

ASO Author Reflections: The Role of Neoadjuvant Chemotherapy in the Management of Primary Retroperitoneal Germ Cell Tumors in Children.

Annals of surgical oncology·2026

Same author

Challenges in Managing Primary Retroperitoneal Germ Cell Tumors in Children: The Role of Neoadjuvant Chemotherapy.

Annals of surgical oncology·2026

Same author

Deciphering the single-cell molecular landscape of ampullary cancer: A rare gastrointestinal malignancy.

iScience·2026

Same author

Local Tumor Microenvironment Niches Correlate With Survival And Immunotherapy Response In Human Glioblastoma.

bioRxiv : the preprint server for biology·2026

Same author

Deep Learning of Histopathology Predicts Outcomes After Surgery for Pancreatic Cancer.

JCO clinical cancer informatics·2026

Same journal

Covariance decomposition for distance based species tree estimation.

BMC bioinformatics·2026

Same journal

SNPio: a Python interface for population genomic data processing.

BMC bioinformatics·2026

Same journal

SpaHNR: a spatial domain identification method via sparse attention-based hierarchical node representation and multi-view contrastive learning.

BMC bioinformatics·2026

Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026

Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026

Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 16, 2026

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

Published on: October 25, 2011

DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification.

Clémentine Decamps¹, Alexis Arnaud², Florent Petitprez³

¹Laboratory TIMC-IMAG, UMR 5525, CNRS, Univ. Grenoble Alpes, Grenoble, France.

BMC Bioinformatics

|October 3, 2021

Summary

This summary is machine-generated.

DECONbench provides a standardized resource for evaluating cancer cell heterogeneity deconvolution methods using simulated datasets. This crowdsourced platform enables systematic comparison of new algorithms to baseline methods for improved cancer research.

Keywords:

Benchmarking platform Cancer Cellular heterogeneity DNA methylation Deconvolution Omics integration Transcriptome

More Related Videos

Building Up a High-throughput Screening Platform to Assess the Heterogeneity of HER2 Gene Amplification in Breast Cancers

Building Up a High-throughput Screening Platform to Assess the Heterogeneity of HER2 Gene Amplification in Breast Cancers

Published on: December 5, 2017

Three-Dimensional Imaging of Tumor-Bearing Tissue Using the Iterative Bleaching Extends Multiplexity Approach

Three-Dimensional Imaging of Tumor-Bearing Tissue Using the Iterative Bleaching Extends Multiplexity Approach

Published on: April 25, 2025

Related Experiment Videos

Last Updated: Jun 16, 2026

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

Published on: October 25, 2011

Building Up a High-throughput Screening Platform to Assess the Heterogeneity of HER2 Gene Amplification in Breast Cancers

Building Up a High-throughput Screening Platform to Assess the Heterogeneity of HER2 Gene Amplification in Breast Cancers

Published on: December 5, 2017

Three-Dimensional Imaging of Tumor-Bearing Tissue Using the Iterative Bleaching Extends Multiplexity Approach

Three-Dimensional Imaging of Tumor-Bearing Tissue Using the Iterative Bleaching Extends Multiplexity Approach

Published on: April 25, 2025

Area of Science:

Bioinformatics
Computational Biology
Cancer Research

Background:

Quantifying tumor heterogeneity is crucial for understanding cancer progression and tailoring treatments.
Existing bioinformatic tools for cell population assessment from single-omic data include reference-based and reference-free methods.
There is a need for systematic tools to evaluate deconvolution algorithms using controlled data, especially for multi-omic datasets.

Purpose of the Study:

To introduce DECONbench, a standardized benchmarking resource for evaluating computational methods that quantify cell-type heterogeneity in cancer.
To provide gold standard simulated datasets for transcriptome and methylome profiles of pancreatic adenocarcinoma.
To establish a platform for systematic performance evaluation and crowdsourced benchmarking of deconvolution algorithms.

Main Methods:

Development of DECONbench, a benchmarking resource with simulated datasets.
Inclusion of baseline deconvolution methods (reference-free algorithms).
Systematic performance evaluation of new and existing methods.

Main Results:

DECONbench offers standardized benchmark datasets mimicking pancreatic cancer heterogeneity.
It includes baseline reference-free deconvolution algorithms for comparison.
The platform facilitates systematic evaluation and public sharing of method performance and code.

Conclusions:

DECONbench enables continuous, user-friendly submission and automatic comparison of new deconvolution methods.
It serves as a reference platform for benchmarking cancer heterogeneity deconvolution methods.
The resource aims to advance benchmarking practices in biomedical and life sciences and is available on Codalab.