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A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions
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PPIGCF: A Protein-Protein Interaction-Based Gene Correlation Filter for Optimal Gene Selection.

Soumen Kumar Pati1, Manan Kumar Gupta1, Ayan Banerjee2

  • 1Department of Bioinformatics, Maulana Abul Kalam Azad University of Technology, Haringhata 741249, West Bengal, India.

Genes
|May 27, 2023
PubMed
Summary

This study introduces a new method, protein-protein interaction-based gene correlation filtration (PPIGCF), for analyzing complex biological data. PPIGCF efficiently identifies key gene markers for cancer classification using less data.

Keywords:
Pearson’s correlationdimension reductiongene ontologyinformation contentprotein–protein interaction

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Omics data complexity necessitates advanced computational methods for identifying significant biological markers.
  • Existing techniques may struggle with the scale and complexity of gene expression datasets.

Purpose of the Study:

  • To introduce a novel dimension reduction technique, protein-protein interaction-based gene correlation filtration (PPIGCF).
  • To enhance the efficiency of biomarker discovery from microarray gene expression data.

Main Methods:

  • PPIGCF utilizes Gene Ontology (GO) and protein-protein interaction (PPI) structures.
  • It classifies genes based on GO annotations (Biological Process and Cellular Component) to build PPI networks.
  • A gene correlation filter and Information Content (IC) are applied to prioritize significant genes.

Main Results:

  • PPIGCF effectively reduces dimensionality in complex biological datasets.
  • The method requires fewer genes to achieve high accuracy (~99%) in cancer classification compared to current methods.
  • Demonstrated efficiency in reducing computational and time complexity for biomarker discovery.

Conclusions:

  • PPIGCF is an efficient technique for prioritizing significant genes and discovering biomarkers.
  • The method offers improved computational and time efficiency for analyzing omics data.
  • PPIGCF shows promise for accurate cancer classification with reduced data requirements.