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Biomarker Prioritisation and Power Estimation Using Ensemble Gene Regulatory Network Inference.

Furqan Aziz1,2, Animesh Acharjee1,2,3, John A Williams1,2,4

  • 1Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, Birmingham B15 2TT, UK.

International Journal of Molecular Sciences
|October 29, 2020
PubMed
Summary

This study introduces a framework for inferring gene regulatory networks (GRNs) using MIDER and PLSNET methods. An ensemble approach combining these methods enhances accuracy in identifying key gene regulators from expression data.

Keywords:
causal modellingexperimental designgene regulatory networkomics integration

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Area of Science:

  • Systems Biology
  • Bioinformatics
  • Computational Biology

Background:

  • Understanding gene regulatory networks (GRNs) is crucial for deciphering gene regulation mechanisms.
  • Inferring GRN topology from gene expression data presents challenges due to noisy data and limited samples.
  • Discrepancies among existing inference algorithms hinder meaningful comparison and validation.

Purpose of the Study:

  • To develop and validate computational methods for inferring GRN structure directly from gene expression data.
  • To identify key gene regulators in Inflammatory Bowel Disease (IBD), Pancreatic Ductal Adenocarcinoma (PDAC), and Acute Myeloid Leukaemia (AML).
  • To assess the accuracy of an ensemble approach combining multiple inference algorithms and estimate sample size requirements for validation.

Main Methods:

  • Application of Mutual Information Distance and Entropy Reduction (MIDER) and Partial Least Square based Feature Selection (PLSNET) methods.
  • Inference of GRN topology using gene expression datasets from IBD, PDAC, and AML studies.
  • Development of an ensemble-based approach combining MIDER and PLSNET outputs for improved accuracy.

Main Results:

  • Successful identification of key gene regulators across different disease datasets, including UGT1A family genes in IBD and SULF1/THBS2 in PDAC.
  • Demonstration that the ensemble approach significantly enhances GRN inference accuracy compared to individual methods.
  • Estimation of the required number of samples for future validation studies, providing a practical framework for experimental design.

Conclusions:

  • The proposed analysis framework effectively infers GRN structure and predicts candidate regulator genes from expression data.
  • An ensemble strategy integrating MIDER and PLSNET offers a robust and accurate method for GRN topology inference.
  • The framework provides valuable insights for experimental validation by estimating necessary sample sizes.