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C-HMOSHSSA: Gene selection for cancer classification using multi-objective meta-heuristic and machine learning

Aman Sharma¹, Rinkle Rani¹

¹Computer Science and Engineering Department, Thapar Institute of Engineering & Technology, Patiala, Punjab, India.

Computer Methods and Programs in Biomedicine

|August 17, 2019

Summary

This summary is machine-generated.

A new hybrid gene selection framework, C-HMOSHSSA, enhances cancer detection accuracy by identifying minimal, biologically relevant gene subsets. This method outperforms existing techniques for microarray data classification.

Keywords:

Cancer classification Feature selection Gene expression Gene selection Machine learning Multi-objective optimization Salp swarm algorithm Spotted hyena optimizer

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

DNA microarray technology is crucial for early cancer detection and understanding disease microenvironments.
Classifying microarray data is vital due to the availability of numerous gene expression assays.
A key challenge is identifying minimal gene subsets that maximize classification accuracy.

Purpose of the Study:

To develop a hybrid gene selection framework (C-HMOSHSSA) for improved microarray data classification.
To leverage the strengths of the multi-objective spotted hyena optimizer (MOSHO) and Salp Swarm Algorithm (SSA).
To enhance both convergence and diversity in multi-objective optimization for gene selection.

Main Methods:

Proposed a hybrid framework (C-HMOSHSSA) combining MOSHO and SSA for gene selection.
Utilized MOSHO for efficient information maintenance and SSA for diversity maintenance.
Developed a hybrid approach to balance exploration and exploitation capabilities in optimization.

Main Results:

Trained four classifiers on seven high-dimensional datasets using the proposed hybrid gene selection algorithm.
Demonstrated that the C-HMOSHSSA technique significantly outperforms existing state-of-the-art methods.
Identified novel sets of informative and biologically relevant genes.

Conclusions:

The proposed C-HMOSHSSA technique successfully identifies informative and biologically relevant gene sets.
This approach offers significant improvements in gene selection for cancer detection.
The methodology is applicable to other domains requiring effective feature selection.