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 Experiment Videos

Machine learning-driven cancer diagnostics with improved robustness and interpretability.

Pengfei Li1, Zhen Liu1

  • 1State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry, Nanjing University Nanjing 210023 Jiangsu China zhenliu@nju.edu.cn +86-25-8968-5639.

Chemical Science
|June 11, 2026
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

A Bioinspired Nanozyme Enables Glycoimmune Therapy via Precision Sialoglycan Trimming.

Angewandte Chemie (International ed. in English)·2026
Same author

The design and development of glucose probes for sensing and imaging within biological systems.

Chemical Society reviews·2026
Same author

An Artificial Antibody-Based Toolbox Accelerates Validation of Hidden Microproteins Encoded by the Dark Genome.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Analytical methods for glycoRNA detection: a systematic review.

Analytical methods : advancing methods and applications·2026
Same author

Outperforming Biorecognition: Epitope-Imprinted Nanoparticle Enables High-Species-Specificity Purification of Human IgG.

Nano letters·2025
Same author

Tri-specific molecularly imprinted lysosomal nanodegrader enables synergistic therapy of cytokine storm.

Chemical science·2025
Same journal

Recent progress in catalytic asymmetric synthesis of triarylmethanes.

Chemical science·2026
Same journal

GFP chromophore photophysics: ultrafast dynamics and hot ground state cooling in the neutral form.

Chemical science·2026
Same journal

Large Stokes shift fluorophores from <i>meta</i>-substituted zwitterions.

Chemical science·2026
Same journal

<i>In situ</i> glycosylation-directed H-aggregation of Type I photosensitizers for synergistic biofilm eradication and promoting diabetic wound healing.

Chemical science·2026
Same journal

Substituent engineering of dynamic covalent bonds enables simultaneous enhancement of performance and recyclability.

Chemical science·2026
Same journal

Visible-light-enabled three-component carboamidation of alkenes with aryl thianthrenium salts.

Chemical science·2026
See all related articles

Machine learning (ML) offers a powerful solution to enhance cancer diagnostics by optimizing assays and improving data interpretation. This approach accelerates the development of next-generation tools for earlier cancer detection and better patient outcomes.

Area of Science:

  • Oncology
  • Biomedical Data Science
  • Artificial Intelligence in Medicine

Background:

  • Cancer diagnostics are crucial for improving survival rates but face challenges in assay development and data interpretation.
  • Current diagnostic methods often rely on inefficient trial-and-error approaches and struggle with complex, high-dimensional data.
  • Machine learning (ML) presents a promising avenue to overcome these limitations in cancer diagnostics.

Purpose of the Study:

  • To explore the application of machine learning (ML) algorithms in advancing cancer diagnostics.
  • To critically compare the strengths and limitations of various ML algorithms for real-world cancer diagnostic applications.
  • To outline recent ML-driven advancements and future research directions in cancer diagnostics.

Main Methods:

Related Experiment Videos

  • Review and comparison of widely used ML algorithms in cancer diagnostics.
  • Analysis of ML algorithm performance based on data scale, class imbalance, feature structure, generalization, and interpretability.
  • Summarization of recent ML applications in analytical platform optimization and multiscale data interpretation.

Main Results:

  • ML algorithms show significant potential in addressing inefficiencies in diagnostic assay design and optimization.
  • ML facilitates the interpretation of complex, high-dimensional clinical imaging and molecular profiling data.
  • Recent advances highlight ML's role in enhancing analytical workflows and multiscale data integration for cancer diagnostics.

Conclusions:

  • Machine learning is a transformative technology for accelerating the development of next-generation cancer diagnostics.
  • Addressing challenges in data scale, interpretability, and generalization is key for robust ML implementation.
  • A roadmap for future research is proposed to fully leverage ML for improved cancer detection and patient outcomes.