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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Selecting high-dimensional mixed graphical models using minimal AIC or BIC forests.

David Edwards1, Gabriel C G de Abreu, Rodrigo Labouriau

  • 1Institute of Genetics and Biotechnology, Faculty of Agricultural Sciences, Aarhus University, Aarhus, Denmark. David.Edwards@agrsci.dk

BMC Bioinformatics
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Summary
This summary is machine-generated.

This study extends Chow and Liu's method to construct networks from high-dimensional data, optimizing penalized likelihood criteria and handling mixed discrete and Gaussian variables for biological network inference.

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Area of Science:

  • Computational Biology
  • Statistical Genetics
  • Bioinformatics

Background:

  • Chow and Liu's method uses maximum weight spanning tree algorithms for discrete data.
  • This approach is efficient for high-dimensional problems.

Purpose of the Study:

  • Extend Chow and Liu's method for penalized likelihood criteria (AIC, BIC).
  • Adapt the method for datasets with both discrete and Gaussian variables.
  • Apply the extended method to gene expression and genetics of gene expression studies.

Main Methods:

  • Utilized maximum weight spanning tree algorithms.
  • Incorporated penalized likelihood criteria like AIC and BIC.
  • Developed an approach for mixed-type data (discrete and Gaussian).

Main Results:

  • Identified minimal BIC forests for differentially expressed genes, supplementing differential expression analysis.
  • Constructed networks approximating joint distributions in genetics of gene expression studies.
  • Demonstrated applicability to gene expression datasets.

Conclusions:

  • The extended approach is valuable for preliminary analysis of high-dimensional data dependence structures.
  • Identified distinct connected components for dimension reduction.
  • Provided initial models for complex network inference and identified key biological network features like hub nodes.