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SEMtree: tree-based structure learning methods with structural equation models.

Mario Grassi1, Barbara Tarantino1

  • 1Department of Brain and Behavioral Sciences, University of Pavia, Pavia 27100, Italy.

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Summary
This summary is machine-generated.

SEMtree() identifies functional modules in protein-protein interaction networks by analyzing expression data. This R package helps uncover biologically relevant subnetworks for disease state analysis.

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Area of Science:

  • Bioinformatics
  • Systems Biology
  • Network Analysis

Background:

  • Exponential growth in expression and protein-protein interaction (PPI) data necessitates methods to identify functional modules.
  • Identifying condition-specific subnetworks is crucial for understanding cellular and disease states.

Purpose of the Study:

  • To propose SEMtree(), a novel set of tree-based algorithms for discovering functional modules in PPI networks.
  • To provide a user-friendly R package for analyzing network regions with high reliability scores.

Main Methods:

  • SEMtree() integrates graph theory and statistically interpretable parameters within a structural equation modeling framework.
  • It employs statistical testing for differential expression and gene-gene co-expression.
  • Active subnetworks are detected and converted into directed trees using causal additive trees based on the Chu-Liu-Edmonds algorithm.

Main Results:

  • SEMtree() was applied to COVID-19 RNA-seq data (GSE172114) and simulated datasets.
  • The method effectively captures biologically relevant subnetworks with clear visualization of directed paths.
  • SEMtree() demonstrates good perturbation extraction and classifier performance compared to existing methods.

Conclusions:

  • SEMtree() offers an efficient technique for identifying functional modules in PPI networks.
  • The R package SEMgraph provides a user-friendly tool for researchers.
  • This approach aids in revealing process-specific information correlated with cellular or disease states.