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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
Selecting a minimal number of relevant genes from microarray data to design accurate tissue classifiers
Hui-Ling Huang1, Chong-Cheng Lee, Shinn-Ying Ho
1Department of Information Management, Jin-Wen Institute of Technology, Hsin-Tien 231, Taiwan.
Bio Systems
|February 13, 2007
Summary
This study introduces GeneSelect, an intelligent genetic algorithm (IGA) for efficient gene selection from microarray data. It improves diagnostic test development by optimizing gene selection and classification accuracy, outperforming existing methods.
Area of Science:
- Bioinformatics
- Computational Biology
- Machine Learning in Genomics
Background:
- Developing inexpensive diagnostic tests requires selecting minimal, relevant genes from microarray data while maximizing classification accuracy.
- Simultaneously optimizing gene selection and classification accuracy presents a significant computational challenge due to the large parameter space.
- Existing methods like the genetic algorithm/maximum likelihood classification (GA/MLHD) approach aim to address this but can be improved.
Purpose of the Study:
- To propose an efficient evolutionary approach, GeneSelect, for optimizing gene selection and multiclass classification accuracy from microarray data.
- To develop an intelligent genetic algorithm (IGA) integrated into GeneSelect for enhanced performance in gene selection.
- To compare the performance of the proposed intelligent genetic algorithm/maximum likelihood classification (IGA/MLHD) against the existing GA/MLHD.
Main Methods:
- GeneSelect employs a three-part cooperative strategy: an efficient encoding scheme for candidate solutions, a generalized fitness function, and an intelligent genetic algorithm (IGA).
- The IGA was combined with maximum likelihood classification (IGA/MLHD) for evaluating gene selection performance.
- The IGA/MLHD method was applied to 11 human cancer-related gene expression datasets for validation.
Main Results:
- The IGA/MLHD method demonstrated superior performance compared to the GA/MLHD approach across all evaluated datasets.
- IGA/MLHD achieved a reduction in the number of selected genes while enhancing classification accuracy.
- The selected genes and accuracy using IGA/MLHD exhibited greater robustness.
Conclusions:
- The proposed GeneSelect method, particularly the IGA/MLHD implementation, offers an efficient and effective solution for gene selection in microarray data analysis.
- This approach holds significant promise for the development of more accurate and cost-effective diagnostic tools.
- The enhanced performance in terms of gene count, accuracy, and robustness validates the utility of intelligent genetic algorithms in bioinformatics.
