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Immunotherapy is a treatment that boosts or manipulates the immune system to fight diseases, including cancer. For instance, by stimulating an immune response through vaccinations against viruses that cause cancers, like hepatitis B virus and human papillomavirus, these diseases can be prevented. Nonetheless, some cancer cells can avoid the immune system due to their rapid mutation and division. The immune response to many cancers involves three phases: elimination, equilibrium, and escape.

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Updated: Mar 21, 2026

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Published on: November 5, 2019

Gene selection approach based on improved swarm intelligent optimisation algorithm for tumour classification.

Cong Jin¹, Shu-Wei Jin²

¹School of Computer, Central China Normal University, Wuhan 430079, People's Republic of China. jincong@mail.ccnu.ed.cn.

IET Systems Biology

|May 18, 2016

Summary

This summary is machine-generated.

This study introduces an optimized gene selection method for tumor classification using gene expression profiles (GEP). The approach effectively identifies key genes and builds accurate tumor classifiers, demonstrating significant improvements.

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Last Updated: Mar 21, 2026

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Published on: November 5, 2019

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Gene selection from gene expression profiles (GEP) is crucial for accurate tumor classification.
Existing methods often require manual determination of the number of genes to select.

Purpose of the Study:

To present an improved swarm intelligent optimization algorithm for gene selection in tumor classification.
To develop a method that automatically determines the optimal number of genes for classification.

Main Methods:

An enhanced swarm intelligent optimization algorithm was developed to maintain population diversity during gene selection.
Tumor classifiers, including ensemble methods, were constructed based on the selected genes.
The approach was evaluated using four distinct gene expression datasets.

Main Results:

The proposed gene selection method effectively identified informative genes from large datasets.
Tumor classifiers built using the selected genes demonstrated high accuracy.
The algorithm successfully automated the determination of the number of selected genes.

Conclusions:

The improved swarm intelligent optimization algorithm provides an effective and automated approach for gene selection in tumor classification.
The developed tumor classifiers show promising performance, highlighting the utility of the proposed gene selection strategy.