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A Granular Self-Organizing Map for Clustering and Gene Selection in Microarray Data.

Shubhra Sankar Ray, Avatharam Ganivada, Sankar K Pal

IEEE Transactions on Neural Networks and Learning Systems

|August 19, 2015

Summary

This summary is machine-generated.

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A novel granular self-organizing map (GSOM) enhances clustering and gene selection. This fuzzy rough set-based approach improves classification accuracy and statistical significance in microarray data analysis.

Area of Science:

Computational biology
Machine learning
Data mining

Background:

Self-organizing maps (SOMs) are effective for data clustering.
Fuzzy rough set theory offers robust methods for handling uncertainty and imprecision.
Gene selection from microarray data is crucial for accurate disease diagnosis and treatment.

Purpose of the Study:

To develop a new granular self-organizing map (GSOM) by integrating fuzzy rough set concepts with SOM.
To introduce an unsupervised fuzzy rough feature selection (UFRFS) method for gene selection in microarray data.
To evaluate the performance of the proposed GSOM and UFRFS methods against existing approaches.

Main Methods:

A modified learning procedure updates neuron weights in the GSOM.

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A novel neighborhood definition using fuzzy rough sets is employed.

Clusters generated by GSOM are used to create a decision table for gene selection.

Main Results:

GSOM demonstrated superior clustering performance, validated by β-index, DB-index, Dunn-index, and fuzzy rough entropy.
UFRFS achieved higher classification accuracy and feature evaluation index compared to related unsupervised methods.
Selected genes by UFRFS showed greater statistical significance.

Conclusions:

The proposed GSOM effectively integrates fuzzy rough set theory for improved clustering.
The UFRFS method provides a statistically robust and accurate approach for gene selection in microarray data.
The developed methods offer significant advantages for bioinformatics and machine learning applications.