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Human limits in Machine Learning: Prediction of plant phenotypes using soil microbiome data

  • Rosa Aghdam*
  • Xudong Tang*
  • Shan Shan
  • Richard Lankau
  • Claudia Solís-Lemus

This repository provides companion source code to reproduce models results and visualization plots as presented in Aghdam, Tang et al, 2023.

Citation:

@ARTICLE{Aghdam2023-nz,
  title         = "Human Limits in Machine Learning: Prediction of Plant
                   Phenotypes Using Soil Microbiome Data",
  author        = "Aghdam, Rosa and Tang, Xudong and Shan, Shan and Lankau,
                   Richard and Sol{\'\i}s-Lemus, Claudia",
  month         =  jun,
  year          =  2023,
  archivePrefix = "arXiv",
  primaryClass  = "stat.ML",
  eprint        = "2306.11157"
}

Folder Structure

The detailed instruction of setting up and running the codes are provided in the README files of each of the following folder.

Folder Description
julia-code The Julia code for data-augmentation, as well as pre and post process of BNN models.
python-code The Python code for feature selections, comparison to random, Random Forest models, and result visualization.
r-code The R code for normalization of OTUs and feature selection based on NerComi, and a train-test-split for BNN.
shell-script The script for running all BNN models in Software for Flexible Bayesian Modeling and Markov Chain Sampling, and submission files for CHTC system of UW-Madison.
unused-scripts Julia code for models and methods that did not make into the final paper.

Please read the description in julia-code first as an overview of workflow, and then proceed to r-code and python-code. Theshell-script could be went over last, as all the works in other folders need to be done first.

Issues and questions

Issues and questions are encouraged through the GitHub issue tracker. For bugs or errors, please make sure to provide enough details for us to reproduce the error on our end.