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

Targeted Cancer Therapies02:57

Targeted Cancer Therapies

7.4K
The targeted cancer therapies, also known as “molecular targeted therapies,” take advantage of the molecular and genetic differences between the cancer cells and the normal cells. It needs a thorough understanding of the cancer cells to develop drugs that can target specific molecular aspects that drive the growth, progression, and spread of cancer cells without affecting the growth and survival of other normal cells in the body.
There are several types of targeted therapies against...
7.4K
Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

4.8K
Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
4.8K

You might also read

Related Articles

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

Sort by
Same author

Interpreting the Black Box: Interpretable Machine Learning and Systems Pharmacology in Small-Molecule Therapeutics.

Pharmaceutics·2026
Same author

A competitive endogenous RNA network involving RiLinc6978 and miR9474-5p regulates fruit softening in tomato.

The Plant cell·2026
Same author

Toward sustainable control of phyto-nematodes: integrating lessons from crops to advance genetic modification in tomato.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik·2026
Same author

Multi-scale EEG evidence for attention enhancement following long-term action video game training.

International journal of psychophysiology : official journal of the International Organization of Psychophysiology·2026
Same author

Associations of self-compassion and resilience with help-seeking among Chinese athletes: the mediating role of self-stigma and sex differences.

Frontiers in psychology·2026
Same author

Beyond Feature Selection: Interpretable Machine Learning for Mechanistic Insights in Metabolomics.

Biology·2026

Related Experiment Video

Updated: May 31, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.4K

Utilizing Feature Selection Techniques for AI-Driven Tumor Subtype Classification: Enhancing Precision in Cancer

Jihan Wang1, Zhengxiang Zhang1, Yangyang Wang2

  • 1Yan'an Medical College of Yan'an University, Yan'an 716000, China.

Biomolecules
|January 25, 2025
PubMed
Summary

Feature selection techniques improve machine learning (ML) models for cancer diagnosis by identifying key biomarkers. Artificial intelligence (AI) enhances this process, advancing personalized cancer therapies.

Keywords:
artificial intelligencebiomarkersdeep learningfeature selectionhigh-dimensional datamachine learningmulti-omicstumor subtype classification

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.7K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K

Related Experiment Videos

Last Updated: May 31, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.4K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.7K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K

Area of Science:

  • Oncology
  • Bioinformatics
  • Computational Biology

Background:

  • Cancer's inherent heterogeneity complicates accurate diagnosis and effective treatment strategies.
  • Identifying tumor subtypes and understanding their diverse biological behaviors remain significant challenges.

Purpose of the Study:

  • To review how feature selection techniques enhance machine learning (ML) model interpretability and performance in high-dimensional cancer datasets.
  • To explore the role of feature selection in improving cancer diagnostics and personalized therapies through multi-omics data integration.

Main Methods:

  • Examination of filter, wrapper, and embedded feature selection methods.
  • Review of machine learning (ML) algorithms and multi-omics data integration strategies.
  • Analysis of artificial intelligence (AI)-powered feature selection approaches.

Main Results:

  • Feature selection methods are crucial for identifying relevant biomarkers, thereby enhancing cancer diagnostic precision.
  • The integration of multi-omics data with ML algorithms offers a comprehensive understanding of tumor heterogeneity.
  • AI-powered feature selection shows promise in automating and refining feature extraction, addressing challenges like data quality and scalability.

Conclusions:

  • Feature selection is vital for advancing cancer diagnostics and personalized medicine.
  • AI and deep learning (DL) models, combined with integrative multi-omics strategies, hold transformative potential for robust and reproducible cancer research.
  • Addressing limitations such as data quality, overfitting, and scalability is critical for future advancements.