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An efficient gene selection method for microarray data based on LASSO and BPSO.

Ying Xiong^1,2,3, Qing-Hua Ling⁴, Fei Han^1,2

¹School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, 212013, China.

BMC Bioinformatics

|January 1, 2020

Summary

This summary is machine-generated.

This study introduces a novel gene selection method using least absolute shrinkage and selection operator (LASSO) and improved binary particle swarm optimization (BPSO). The approach effectively identifies optimal gene subsets for accurate cancer classification from microarray data.

Keywords:

Binary particle swarm optimization Extreme learning machine Gene selection LASSO

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

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Published on: June 21, 2018

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Accurate gene selection from microarray data is crucial for improving cancer diagnosis and treatment.
Identifying compact and predictive gene subsets remains a significant challenge in cancer research.

Purpose of the Study:

To develop an advanced gene selection method for enhanced accuracy in cancer classification.
To identify the most predictive gene subsets without discarding critical genes.

Main Methods:

A novel approach combining least absolute shrinkage and selection operator (LASSO) with an improved binary particle swarm optimization (BPSO).
Utilizing clustering to prevent LASSO overfitting and a double filter strategy to create a refined gene pool.
Implementing a new mapping function within BPSO to guide particle updates for selecting highly predictive genes.

Main Results:

The proposed method successfully selects optimal gene subsets with high probability.
Experimental validation on public microarray datasets demonstrated superior performance compared to existing methods.

Conclusions:

The improved BPSO, coupled with double filter strategies, effectively identifies optimal gene subsets.
The method shows significant promise for improving the accuracy of cancer classification using gene expression data.