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k- Strong Inference Algorithm: A Hybrid Information Theory Based Gene Network Inference Algorithm.

Mustafa Özgür Cingiz1

  • 1Computer Engineering Department, Faculty of Engineering and Natural Sciences, Bursa Technical University, Mimar Sinan Campus, Yildirim, 16310, Bursa, Turkey. mustafa.cingiz@btu.edu.tr.

Molecular Biotechnology
|November 11, 2023
PubMed
Summary

The k-Strong Inference Algorithm (ksia) infers gene networks using integrated correlation scores and elimination steps. This hybrid approach enhances reliability and robustness for omics data analysis, improving gene expression dataset predictions.

Keywords:
Association estimatorsGene co-expression networksGene network inference algorithmsGene regulatory networksOverlap analysis

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Gene networks are crucial for understanding disease-gene mechanisms, reducing experimental costs.
  • Existing gene network inference (GNI) algorithms vary in accuracy and robustness.
  • Omics datasets provide valuable data for inferring gene interactions.

Purpose of the Study:

  • To introduce a novel hybrid GNI algorithm, k-Strong Inference Algorithm (ksia).
  • To infer more reliable and robust gene networks from omics data.
  • To improve the prediction accuracy of gene interactions.

Main Methods:

  • ksia integrates Pearson correlation coefficient (PCC) and Spearman rank correlation coefficient (SCC) for mutual information scoring.
  • Three distinct elimination steps are applied to remove redundant and spurious gene relations.
  • The algorithm's performance was benchmarked against ARACNE, C3NET, CLR, and MRNET using microbe microarrays.

Main Results:

  • ksia infers fewer relations due to stringent elimination, enhancing specificity.
  • The algorithm demonstrated superior performance on Escherichia coli (E.coli) and Saccharomyces cerevisiae (yeast) datasets.
  • Improved F-measure and precision values indicate enhanced gene network accuracy.

Conclusions:

  • The hybrid approach of integrating association estimators and elimination stages enhances GNI performance.
  • ksia offers a reliable method for inferring robust gene networks from omics data.
  • The ksia R package is available for public use at https://github.com/ozgurcingiz/ksia.