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Related Experiment Video

Updated: Oct 3, 2025

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Inferring Causation in Yeast Gene Association Networks With Kernel Logistic Regression.

Amira Al-Aamri1, Kamal Taha2, Maher Maalouf3

  • 1Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, UAE.

Evolutionary Bioinformatics Online
|February 17, 2022
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Summary

This study introduces a machine learning approach to build causal gene networks by predicting gene associations. The method accurately identifies gene relationships and transcription factors using microarray data from Saccharomyces cerevisiae.

Keywords:
Bioinformaticsgene co-expression networkpredictive modeltranscription factor

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Area of Science:

  • Bioinformatics
  • Systems Biology
  • Computational Biology

Background:

  • Inferring gene-gene associations is crucial in bioinformatics.
  • Existing tools often lack directionality and reaction type information.
  • Causal gene networks require methods that consider these relationships.

Purpose of the Study:

  • To construct a causal gene co-expression network.
  • To identify transcription factors within gene pairs.
  • To improve prediction accuracy using machine learning.

Main Methods:

  • Utilized microarray expression data from Saccharomyces cerevisiae.
  • Employed a machine learning technique based on logistic regression.
  • Classified gene pairs into connected or nonconnected based on correlation.

Main Results:

  • Successfully constructed a causal gene co-expression network.
  • Achieved high performance in predicting gene relationships.
  • Demonstrated effectiveness in identifying transcription factors.

Conclusions:

  • The logistic regression model effectively addresses network sparsity.
  • The proposed system enhances the accuracy of predicting gene associations.
  • This method provides a robust framework for yeast regulatory network analysis.