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Published on: October 11, 2018
Fuzzy-rough sets for information measures and selection of relevant genes from microarray data.
1Machine Intelligence Unit, Indian Statistical Institute, Kolkata 700108, India. pmaji@isical.ac.in
Summary
This study introduces a novel fuzzy-rough set approach to effectively select relevant, nonredundant genes from continuous microarray data. The method accurately approximates gene expression distributions for improved gene selection.
Area of Science:
- Bioinformatics
- Computational Biology
- Machine Learning
Background:
- Information measures like entropy are useful for gene selection from microarray data.
- Computing these measures is challenging for continuous gene expression values due to difficulties in density estimation.
Purpose of the Study:
- To present a novel approach for gene selection using fuzzy equivalence partition matrices.
- To approximate continuous gene expression distributions for accurate information measure computation.
Main Methods:
- Utilized fuzzy-rough set theory to construct fuzzy equivalence partition matrices.
- Approximated marginal and joint distributions of continuous gene expression values.
- Evaluated performance using class separability index and support vector machine accuracy.
Main Results:
- The proposed fuzzy equivalence partition matrix method effectively selects relevant and nonredundant genes.
- Demonstrated superior performance compared to existing methods for continuous gene expression data.
Conclusions:
- The fuzzy equivalence partition matrix offers a robust solution for gene selection in high-dimensional microarray datasets.
- This approach overcomes limitations of traditional information measures with continuous data.
