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A New Algorithm for Cancer Biomarker Gene Detection Using Harris Hawks Optimization.

Halah AlMazrua¹, Hala AlShamlan¹

¹Information Technology Department, College of Computer and Information Sciences, King Saud University (KSU), Riyadh 11451, Saudi Arabia.

Sensors (Basel, Switzerland)

|October 14, 2022

Summary

This summary is machine-generated.

Two new swarm intelligence algorithms, HHO-SVM and HHO-KNN, efficiently select optimal gene subsets for high-accuracy sample classification. These methods improve upon existing techniques for high-dimensional data analysis.

Keywords:

Harris Hawks Optimization bio-inspired algorithms bioinformatics cancer classification evolutionary algorithm feature selection gene expression k-nearest neighbor support vector machine

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Area of Science:

Bioinformatics
Computational Biology
Machine Learning

Background:

High-dimensional biological data presents challenges for accurate sample classification.
Effective gene selection is crucial for reducing data complexity and improving model performance.
Swarm intelligence offers a promising approach for optimizing complex feature selection tasks.

Purpose of the Study:

To introduce two novel swarm intelligence algorithms, HHO-SVM and HHO-KNN, for effective gene selection.
To enhance classification accuracy by identifying minimal yet informative gene subsets.
To address the challenges posed by high-dimensional microarray data.

Main Methods:

Development of two algorithms: Harris Hawks Optimization with Support Vector Machines (HHO-SVM) and Harris Hawks Optimization with k-Nearest Neighbors (HHO-KNN).
A two-phase approach involving redundancy analysis, relevance calculation, and gene selection using SVM and k-NN with leave-one-out cross-validation.
Performance evaluation on six diverse microarray datasets.

Main Results:

Both HHO-SVM and HHO-KNN demonstrated strong performance in gene selection.
The proposed algorithms achieved high classification accuracy.
A significant reduction in the number of selected genes was observed compared to existing methods.
Comparative analysis showed competitive or superior results against established algorithms.

Conclusions:

HHO-SVM and HHO-KNN are effective swarm intelligence algorithms for gene selection in high-dimensional data.
These algorithms offer a robust solution for improving classification accuracy while minimizing feature sets.
The proposed methods show significant potential for applications in bioinformatics and computational biology.