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Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

Published on: November 2, 2013

Systematic identification of multiple tumor types in microarray data based on hybrid differential evolution

Chun-Liang Lu^1,2, Tsan-Cheng Su³, Tsun-Chen Lin⁴

¹Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan.

Technology and Health Care : Official Journal of the European Society for Engineering and Medicine

|December 20, 2015

Summary

This summary is machine-generated.

This study introduces a hybrid binary differential evolution (DE) algorithm with silhouette filters for improved microarray analysis. The method effectively classifies breast and leukemia cancer subtypes, aiding in biomarker discovery for cancer diagnosis.

Keywords:

Gene selection cancer classification hybrid differential evolution silhouette statistics

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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

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Published on: May 17, 2019

Area of Science:

Bioinformatics
Computational Biology
Machine Learning in Oncology

Background:

Accurate tumor cell classification is crucial for developing diagnostic systems using microarray data.
Standard differential evolution (DE) algorithms operate in continuous space, limiting their direct application to binary optimization problems common in gene selection.

Purpose of the Study:

To propose a hybrid framework combining a binary DE algorithm and silhouette filter for enhanced searching capabilities in microarray analysis.
To utilize this hybrid DE algorithm for gene selection and silhouette statistics for classifying multiple tumor types, specifically breast and leukemia cancers.
To explore distance metrics on silhouette statistics to improve classification accuracy.

Main Methods:

Development of a hybrid framework integrating a binary differential evolution (DE) algorithm with a silhouette filter.
Application of the hybrid DE algorithm for effective gene selection from microarray data.
Utilizing silhouette statistics as a discriminant function for tumor type classification.
Investigating various distance metrics on silhouette statistics to optimize classification performance.

Main Results:

The hybrid DE-silhouette filter method demonstrated effectiveness in discriminating between breast and leukemia cancer subtypes using microarray data.
The approach successfully identified potential biomarkers crucial for accurate cancer diagnosis.
The proposed method shows improved searching ability for binary optimization problems in bioinformatics.

Conclusions:

The hybrid binary DE and silhouette filter framework offers a robust approach for classifying cancer subtypes from microarray data.
This method facilitates biomarker discovery, contributing to the advancement of diagnostic systems for breast and leukemia cancers.
The study highlights the potential of tailored optimization algorithms for complex biological data analysis.