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Technology of Informative Feature Selection for Immunosignature Analysis
A A Koshechkin1, O V Romanovich2, D Stamate3
1Assistant, Department of Theoretical Foundations of Informatics; National Research Tomsk State University, 36 Lenin Avenue, Tomsk, 634050, Russia.
Sovremennye Tekhnologii V Meditsine
|November 19, 2021
Summary
This study introduces a new technology to reduce the dimensionality of immunosignature data, improving classification accuracy by effectively selecting informative features and simplifying analysis.
Area of Science:
- Biomedical data analysis
- Bioinformatics
- Machine learning in healthcare
Background:
- Immunosignature analysis generates high-dimensional data with many uninformative features.
- Effective data reduction is crucial for accurate classification and practical application.
Purpose of the Study:
- To develop and validate a technology for reducing immunosignature data dimensionality.
- To achieve high-quality classification by effectively selecting informative features.
Main Methods:
- A three-step feature selection process: 'one vs all' strategy, median-based screening of false-informative features, and ranking of informative features.
- Utilized normalized immunosignature datasets from a public biomedical repository.
- Employed a Support Vector Machine (SVM) classifier to assess feature selection effectiveness.
Main Results:
- The proposed technology significantly reduces the feature space, eliminating approximately 50% of features in the initial screening step.
- Achieved high classification accuracy (macro-average F1-score of 98.9%) with only 15 features for the GSE52581 dataset.
- Demonstrated robust performance with 91.3% accuracy using 266 features for the same dataset.
Conclusions:
- The developed technology offers a promising approach for effective dimensionality reduction in immunosignature data.
- This method enhances classification quality while simplifying data analysis for practical applications.
