A multi-objective gene selection for cancer diagnosis using particle swarm optimization and mutual information

Gene expression profiling for cancer diagnosis requires the identification of optimal and non-redundant gene subsets from microarray data. We present a multi-objective particle swarm optimization (PSO) approach that balances gene-class relevancy and inter-gene redundancy by integrating mutual inform...

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Bibliographic Details
Published inJournal of ambient intelligence and humanized computing Vol. 15; no. 11; pp. 3777 - 3793
Main Authors Rafie, Azar, Moradi, Parham
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2024
Springer Nature B.V
Subjects
Accuracy
Algorithms
Artificial Intelligence
Cancer
Computational Intelligence
Convergence
Datasets
Diagnosis
Engineering
Entropy
Feature selection
Gene expression
Genes
Heuristic
Lattice theory
Literature reviews
Machine learning
Medical diagnosis
Methods
Multiple objective analysis
Optimization techniques
Original Research
Pareto optimum
Particle swarm optimization
Random variables
Redundancy
Robotics and Automation
Search methods
User Interfaces and Human Computer Interaction
Gene selection
Multi-objective
Medical diagnosis
Particle swarm optimization
Cancer classification
Information theory
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ISSN1868-5137
1868-5145
DOI10.1007/s12652-024-04853-4

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Summary:Gene expression profiling for cancer diagnosis requires the identification of optimal and non-redundant gene subsets from microarray data. We present a multi-objective particle swarm optimization (PSO) approach that balances gene-class relevancy and inter-gene redundancy by integrating mutual information. Our method employs a dual-phase search strategy: an initial PSO search followed by a local search to accelerate convergence, and a subsequent Pareto front selection to extract the non-dominated gene subsets. Experiments on cancer microarray benchmark datasets demonstrate that our approach significantly enhances feature selection and diagnosis accuracy compared to existing methods. Notably, our approach incorporates a novel dual-evaluation framework and an improved particle representation scheme, which collectively enhance robustness and prevent premature convergence. These innovations ensure a comprehensive and effective gene selection process for cancer diagnosis.
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ISSN:1868-5137
1868-5145
DOI:10.1007/s12652-024-04853-4