Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Enzyme-Linked Immunosorbent Assay01:33

Enzyme-Linked Immunosorbent Assay

In 1971, Peter Perlman and Eva Engvall developed an Enzyme-linked immunosorbent assay (ELISA or EIA). ELISA differs from western blot in that the assays are conducted in microtiter plates or in vivo rather than on an absorbent membrane.
There are many different types of ELISAs, but they all involve an antibody molecule whose constant region binds an enzyme, leaving the variable region free to bind its specific antigen.  Enzyme-substrate reaction allows the antigen to be visualized or quantified.

You might also read

Related Articles

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

Sort by
Same author

Sex-based differences in tolerability of developmental antibody-drug conjugates (ADCs) in non-small-cell lung cancer (NSCLC).

ESMO open·2026
Same author

Lung cancer brain metastases management at the dawn of personalized medicine: are we ready to break the barriers?

Annals of oncology : official journal of the European Society for Medical Oncology·2026
Same author

Uveal melanoma: ESMO-EURACAN Clinical Practice Guideline for diagnosis, treatment and follow-up.

ESMO open·2026
Same author

Balancing clinical relevance, legal boundaries, and technological solutions: a case-based analysis of secondary use of electronic health records in Sweden.

ESMO real world data and digital oncology·2026
Same author

Corrigendum to "Cost-effectiveness of first-line osimertinib informed by electronic medical records via text-mining: a real-world Italian case study of <i>EGFR</i>-mutated advanced NSCLC patients": [ESMO Real World Data and Digital Oncology Volume 10, December 2025, 100198].

ESMO real world data and digital oncology·2026
Same author

Predicting efficacy in patients with locally advanced/metastatic urothelial carcinoma (mUC) treated with immunotherapy using explainable machine learning approaches: the SamUR-AI trial on behalf of the Meet-URO group.

ESMO real world data and digital oncology·2026

Related Experiment Video

Updated: Jul 2, 2026

MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier MSC for Lung Cancer Screening
08:14

MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier MSC for Lung Cancer Screening

Published on: October 26, 2017

16.1K

ESMO basic requirements for AI-based biomarkers in oncology (EBAI).

M Aldea1, M Salto-Tellez2, A Marra3

  • 1Department of Cancer Medicine, Gustave Roussy, Villejuif, France; Faculty of Medicine, Paris-Saclay University, Kremlin Bicêtre, France; Lowe Center for Thoracic Oncology, Dana-Farber Cancer Institute, Boston, USA.

Annals of Oncology : Official Journal of the European Society for Medical Oncology
|November 19, 2025
PubMed
Summary

The European Society for Medical Oncology (ESMO) developed the Basic Requirements for AI-based Biomarkers In Oncology (EBAI) framework. EBAI provides criteria for adopting artificial intelligence biomarkers in routine oncology care.

Keywords:
EBAIartificial intelligencebiomarkercancerscalevalidation

More Related Videos

Evaluation of Biomarkers in Glioma by Immunohistochemistry on Paraffin-Embedded 3D Glioma Neurosphere Cultures
06:32

Evaluation of Biomarkers in Glioma by Immunohistochemistry on Paraffin-Embedded 3D Glioma Neurosphere Cultures

Published on: January 9, 2019

8.2K
Author Spotlight: Advancements in Molecular Biomarker Testing for Non-Squamous Non-Small Cell Lung Cancer
07:59

Author Spotlight: Advancements in Molecular Biomarker Testing for Non-Squamous Non-Small Cell Lung Cancer

Published on: September 8, 2023

1.6K

Related Experiment Videos

Last Updated: Jul 2, 2026

MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier MSC for Lung Cancer Screening
08:14

MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier MSC for Lung Cancer Screening

Published on: October 26, 2017

16.1K
Evaluation of Biomarkers in Glioma by Immunohistochemistry on Paraffin-Embedded 3D Glioma Neurosphere Cultures
06:32

Evaluation of Biomarkers in Glioma by Immunohistochemistry on Paraffin-Embedded 3D Glioma Neurosphere Cultures

Published on: January 9, 2019

8.2K
Author Spotlight: Advancements in Molecular Biomarker Testing for Non-Squamous Non-Small Cell Lung Cancer
07:59

Author Spotlight: Advancements in Molecular Biomarker Testing for Non-Squamous Non-Small Cell Lung Cancer

Published on: September 8, 2023

1.6K

Area of Science:

  • Oncology
  • Medical Informatics
  • Biomarker Discovery

Background:

  • Artificial intelligence (AI) is increasingly generating novel biomarkers in oncology.
  • A need exists to bridge the gap between oncology and computer science for clinical AI biomarker implementation.
  • The European Society for Medical Oncology (ESMO) proposed the Basic Requirements for AI-based Biomarkers In Oncology (EBAI) framework.

Purpose of the Study:

  • To establish recommendations for AI-based biomarkers suitable for routine clinical use in oncology.
  • To define a common language and criteria for the adoption of AI biomarkers.

Main Methods:

  • A modified Delphi methodology was employed.
  • A multidisciplinary panel of 37 experts participated in four consensus rounds.
  • AI biomarkers were classified into three classes (A, B, C) with defined validation requirements.

Main Results:

  • AI biomarkers classified as Class A (quantification), Class B (pre-screening), and Class C (novel, C1 prognostic, C2 predictive).
  • Essential criteria include ground truth, performance, and generalizability; fairness is recommended.
  • Specific validation requirements detailed for each class, emphasizing concordance, analytical, and clinical validation.

Conclusions:

  • The EBAI framework provides essential criteria for the routine clinical adoption of AI-based biomarkers.
  • EBAI facilitates a shared understanding among clinicians, AI developers, and researchers.
  • Standardized criteria ensure robust validation and appropriate application of AI biomarkers in oncology.