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

Reliable classification of two-class cancer data using evolutionary algorithms.

Kalyanmoy Deb1, A Raji Reddy

  • 1Kanpur Genetic Algorithms Laboratory (KanGAL), Indian Institute of Technology Kanpur, Kanpur 208 016, India. deb@iitk.ac.in

Bio Systems
|December 4, 2003
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

Artificial Intelligence, Optimization, and Modeling Techniques in Water Resources Management: Challenges and Future Directions.

Water environment research : a research publication of the Water Environment Federation·2026
Same author

Artificial Intelligence, Optimization, and Modeling Techniques in Water Resource Management: Interconnections and Emerging Synergies.

Water environment research : a research publication of the Water Environment Federation·2026
Same author

Toward Interpretable-AI Policies Using Evolutionary Nonlinear Decision Trees for Discrete-Action Systems.

IEEE transactions on cybernetics·2022
Same author

Neural Architecture Transfer.

IEEE transactions on pattern analysis and machine intelligence·2021
Same author

Interpretable Rule Discovery Through Bilevel Optimization of Split-Rules of Nonlinear Decision Trees for Classification Problems.

IEEE transactions on cybernetics·2020
Same author

Effect of Objective Normalization and Penalty Parameter on Penalty Boundary Intersection Decomposition-Based Evolutionary Many-Objective Optimization Algorithms.

Evolutionary computation·2020
Same journal

The Quantum-to-Classical Transducer: A Thermodynamic and Quantum Mechanical Framework for the Emergence of Bioenergetics.

Bio Systems·2026
Same journal

Forward-backward gene expression binarization for boolean state inference over a known regulatory network.

Bio Systems·2026
Same journal

Partial-Label Metric Ceilings for Evaluating Gene Regulatory Networks Inferred from Single-Cell Foundation Models.

Bio Systems·2026
Same journal

The impedance mismatch theory: A non-equilibrium thermodynamic framework for a shared energetic stress pathway in neurodegeneration.

Bio Systems·2026
Same journal

Immune signal-status misclassification: A theoretical framework for biological status assignment and failed status resolution.

Bio Systems·2026
Same journal

Contextuality, incompatibility, and intra-system entanglement of mental markers: From cognition and decision making to medicine.

Bio Systems·2026
See all related articles

This study introduces a novel multiobjective evolutionary algorithm (NSGA-II) for identifying minimal gene subsets crucial for accurate disease classification. The approach successfully identified small gene sets for classifying leukemia, lymphoma, and colon cancer samples with high accuracy.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Accurate classification of disease subtypes from gene expression data is critical in bioinformatics.
  • Traditional methods like clustering and machine learning face challenges with high dimensionality and limited samples.
  • Optimization problems for gene subset identification are complex due to large search spaces and few samples.

Purpose of the Study:

  • To develop an effective method for identifying minimal gene subsets for accurate disease classification.
  • To address the challenges of high dimensionality and limited samples in gene expression data analysis.
  • To utilize multiobjective optimization for balancing gene subset size and classification accuracy.

Main Methods:

  • Formulated gene subset identification as a multiobjective optimization problem.

Related Experiment Videos

  • Employed the Non-dominated Sorting Genetic Algorithm II (NSGA-II), a multiobjective evolutionary algorithm.
  • Incorporated multiple training sets for robust classifier evaluation.
  • Introduced a prediction strength threshold for enhanced classification confidence.

    • Main Results:

    • Discovered small gene subsets (4-5 genes) achieving near 100% classification accuracy for leukemia, lymphoma, and colon cancer.
    • Identified 352 distinct three-gene combinations for 100% accurate leukemia classification.
    • Demonstrated consistent gene subset identification across different disease samples.
    • Validated the efficacy of NSGA-II in gene subset identification.

    Conclusions:

    • Multiobjective evolutionary algorithms, specifically NSGA-II, are highly effective for gene subset identification in bioinformatics.
    • The proposed method enables discovery of smaller, more accurate gene signatures for disease classification.
    • The approach offers flexibility and reliability for complex genomic data analysis.