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A graph-theoretic approach for identifying non-redundant and relevant gene markers from microarray data using

Monalisa Mandal1, Anirban Mukhopadhyay1

  • 1Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India.

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This study introduces a novel graph-theoretic approach for feature selection, mapping it to the densest subgraph problem. A multiobjective particle swarm optimization algorithm effectively identifies relevant and non-redundant disease genes from gene expression data.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Machine Learning

Background:

  • Feature selection is crucial for identifying informative variables in datasets.
  • Traditional methods may struggle with high-dimensional biological data.
  • Graph theory offers a novel framework for organizing feature selection problems.

Purpose of the Study:

  • To develop a graph-theoretic method for feature selection.
  • To identify relevant and non-redundant features, specifically disease-related genes.
  • To propose a multiobjective optimization algorithm for this task.

Main Methods:

  • Representing features and their dissimilarities as a weighted graph.
  • Mapping the feature selection problem to the densest subgraph problem.
  • Employing a multiobjective particle swarm optimization (PSO) algorithm to optimize subgraph properties.

Main Results:

  • The proposed PSO-based algorithm successfully identified relevant and non-redundant disease-related genes.
  • The method demonstrated competitive performance against existing feature selection techniques.
  • Validation was performed on multiple real-life microarray gene expression datasets.

Conclusions:

  • The graph-theoretic approach combined with PSO is effective for gene feature selection.
  • This method offers a robust solution for identifying disease-related genes from complex biological data.
  • The approach provides a new perspective on tackling feature selection challenges in bioinformatics.