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A consensus multi-view multi-objective gene selection approach for improved sample classification.

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Summary
This summary is machine-generated.

This study introduces a new feature selection method using multi-view clustering to improve gene expression analysis. The Consensus Multi-View Multi-objective Clustering (CMVMC) algorithm effectively reduces genes while enhancing sample classification accuracy.

Keywords:
Feature selectionGene ontology (GO)Multi-objective optimizationMulti-view clusteringProtein protein interaction networkSample classification

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Genomics

Background:

  • Gene expression data analysis is crucial for biological insights but suffers from high dimensionality due to irrelevant genes.
  • Existing feature selection methods often overlook multi-modal biological data, potentially missing critical information.
  • Integrating diverse 'omics' data can enhance the efficiency of feature selection.

Purpose of the Study:

  • To develop an advanced feature selection algorithm that effectively utilizes multi-modal biological data.
  • To improve the accuracy and efficiency of sample classification in gene expression analysis.
  • To address the limitations of existing methods by incorporating multiple data views.

Main Methods:

  • Proposed a novel Consensus Multi-View Multi-objective Clustering-based feature selection algorithm (CMVMC).
  • Integrated gene expression, Gene Ontology (GO), and protein-protein interaction network (PPIN) data to create two distinct views.
  • Applied multi-objective consensus clustering to leverage information from both incorporated views.

Main Results:

  • CMVMC successfully identified a reduced set of relevant and non-redundant genes from complex datasets.
  • Demonstrated significant reduction in gene-space for both Multiple Tissues (5565 to 41 genes) and Yeast (2884 to 10 genes) datasets.
  • Achieved high sample classification accuracies: 92.73% for Multiple Tissues and 95.84% for Yeast.

Conclusions:

  • The proposed CMVMC method significantly enhances sample classification accuracy.
  • CMVMC effectively reduces the number of genes, simplifying downstream analysis.
  • Results were validated using internal and external cluster validity indices and biological significance tests.