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Unsupervised Gene Network Inference with Decision Trees and Random Forests.

Vân Anh Huynh-Thu1, Pierre Geurts2

  • 1Department of Electrical Engineering and Computer Science, University of Liège, Liège, Belgium. vahuynh@uliege.be.

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|December 15, 2018
PubMed
Summary
This summary is machine-generated.

Decision trees are powerful machine learning tools for inferring gene regulatory networks (GRNs). This chapter details the GENIE3 algorithm, a leading method for understanding gene interactions.

Keywords:
Decision treesMachine learningRandom forestRegression treesTree ensembles

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Area of Science:

  • Computational Biology
  • Machine Learning
  • Systems Biology

Background:

  • Gene regulatory networks (GRNs) control gene expression.
  • Inferring GRNs is crucial for understanding biological systems.
  • Traditional methods face challenges with complex interactions.

Purpose of the Study:

  • Introduce decision trees as a machine learning approach for GRN inference.
  • Review decision tree-based methods for GRN inference.
  • Detail the GENIE3 algorithm for state-of-the-art GRN inference.

Main Methods:

  • Utilizing decision tree algorithms for biological data analysis.
  • Applying non-parametric, scalable methods with minimal parameters.
  • Implementing the GENIE3 algorithm for gene regulatory network inference.

Main Results:

  • Decision trees effectively detect multivariate interacting effects.
  • The GENIE3 algorithm demonstrates state-of-the-art performance in GRN inference.
  • Decision tree methods offer good scalability and parameter efficiency.

Conclusions:

  • Decision trees are well-suited for inferring gene regulatory networks.
  • The GENIE3 algorithm is a powerful tool for GRN inference.
  • Machine learning approaches like decision trees advance systems biology research.