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Fuzzy logic based approaches for gene regulatory network inference.

Khalid Raza1

  • 1Department of Computer Science, Jamia Millia Islamia, New Delhi, India.

Artificial Intelligence in Medicine
|December 22, 2018
PubMed
Summary

This review consolidates fuzzy logic and hybrid methods for gene regulatory network inference (GRNI) from high-throughput biological data. These computational approaches are crucial for understanding gene regulation and disease mechanisms.

Keywords:
Fuzzy clusteringFuzzy inference systemFuzzy logicGene regulatory networkNetwork inferenceSystems biology

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • High-throughput techniques like microarrays and next-generation sequencing generate vast biological data.
  • Biological databases are growing exponentially, necessitating advanced computational analysis.
  • Gene regulatory networks (GRNs) are key to understanding gene regulation and disease mechanisms.

Purpose of the Study:

  • To provide a consolidated review of fuzzy logic and its hybrid approaches for gene regulatory network inference (GRNI).
  • To cover advancements in GRNI computational algorithms over the last two decades.

Main Methods:

  • Review of computational approaches for GRNI.
  • Focus on statistical techniques, information theory, regression, probabilistic methods, neural networks, and fuzzy logic.
  • Detailed examination of fuzzy logic and its hybridizations for GRNI.

Main Results:

  • Fuzzy logic and its hybrid approaches are well-studied for GRNI due to their advantages.
  • Various computational methods exist for inferring GRNs from gene expression data.
  • The paper consolidates research on fuzzy logic-based GRNI over the past 20 years.

Conclusions:

  • Fuzzy logic offers significant advantages for gene regulatory network inference.
  • Hybrid intelligent approaches enhance GRNI capabilities.
  • This review provides a comprehensive overview of fuzzy logic applications in GRNI.