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Technique of Gene Expression Profiles Extraction Based on the Complex Use of Clustering and Classification Methods.

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Summary

This study introduces a stepwise method to identify key gene expression profiles for understanding disease subtypes and patient health. The approach utilizes clustering, machine learning classifiers, and fuzzy logic to improve gene regulatory network reconstruction.

Keywords:
ML-based binary classifiersROC analysisSOTA clustering algorithmclassificationclusteringclustering quality criteriafuzzy inference systemgene expression profileslung cancer

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • High-dimensional gene expression data presents challenges in identifying disease-relevant profiles.
  • Understanding gene regulatory networks is crucial for disease subtyping and patient health assessment.

Purpose of the Study:

  • To develop and validate a stepwise procedure for extracting informative gene expression profiles.
  • To enhance the reconstruction and simulation of gene regulatory networks by focusing on key genes.

Main Methods:

  • Gene expression data preprocessing including statistical criteria and Shannon entropy for feature reduction.
  • Stepwise hierarchical clustering using Self-Organizing Tree Algorithm (SOTA) with correlation distance.
  • Classification using logistic regression, support-vector machine, decision trees, and random forest, evaluated with Receiver Operating Characteristic (ROC) analysis.
  • Fuzzy inference system for final selection of informative gene expression profiles based on classifier outputs.

Main Results:

  • A robust stepwise procedure was implemented to identify informative gene expression profiles.
  • The method effectively integrates clustering, multiple machine learning classifiers, and fuzzy logic.
  • The extracted gene profiles are suitable for reconstructing and simulating gene regulatory networks.

Conclusions:

  • The proposed stepwise procedure enhances the extraction of informative gene expression profiles.
  • This method facilitates improved gene regulatory network reconstruction and simulation, considering disease subtypes and patient health.
  • The approach offers a valuable tool for advancing personalized medicine and understanding complex diseases.