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Colon cancer data analysis by chameleon algorithm.

Juanying Xie¹, Yuchen Wang¹, Zhaozhong Wu¹

¹School of Computer Science, Shaanxi Normal University, Xi'an, People's Republic of China.

Health Information Science and Systems

|October 29, 2019

Summary

This study introduces a novel gene selection algorithm for colon cancer detection. The chameleon algorithm effectively identifies key differential genes, achieving 85.48% accuracy in distinguishing cancer patients from normal individuals.

Keywords:

Chameleon algorithm Clustering Colon cancer Fisher function Gene subset selection Information index to classification

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Accurate detection of differential genes is crucial for distinguishing colon cancer patients from healthy individuals.
Existing gene selection methods may not fully capture complex gene expression patterns in cancer.

Purpose of the Study:

To propose a novel gene selection algorithm for colon cancer detection using the chameleon algorithm.
To enhance the accuracy of classifying colon cancer patients and normal individuals based on selected differential genes.

Main Methods:

A three-step gene selection process utilizing the chameleon algorithm based on Euclidean distance and information index.
Candidate gene selection using Fisher function values.
Clustering of colon cancer data using the selected gene subset with the chameleon algorithm.

Main Results:

The proposed chameleon algorithm-based gene selection achieved a final clustering accuracy of 85.48%.
The algorithm effectively identified key differential genes specific to colon cancer.
Comparative analysis with related studies confirmed the algorithm's effectiveness.

Conclusions:

The developed chameleon algorithm-based approach is effective for identifying differential genes in colon cancer.
This method offers a promising tool for improving the diagnostic accuracy of colon cancer detection.