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Table of Contents

  • Installation Instructions
  • Modeling Tutorials
    • Tutorial 1: Built in demonstration scripts
      • Load data and split the data into training and validation
      • Construct your model and train your data
      • Show the learning loss during training
      • Prediction on validation data
      • Visualization on landscape
      • Use pretrained model
      • Visualization of obtained landscape
    • Tutorial 2: Test on simulated data
      • Load data
      • Split data into 70% for training and 30% for testing
      • Construct DLIM model and API
      • Predict on validation data
      • Visualization
      • Relationship between predicted phenotype and real
      • Use pretrained model
    • Reproducibility 1: figure 2
      • Load the packages
      • Generate data, define the path to save model
      • Split data into training, testing and validation
      • With spectral initialization
      • Without spectralization
      • Visualization of the results
      • Repeat for geometric model with Gaussian and regulatory Cascade model
      • Title Gaussian
      • Biomechanistic model
    • Reproducibility 2: figure 3 & SI3
      • Load packages
      • Test on mechanistic model
      • Test on exponential model
    • Reproducibility 3: figure 4 & 6
      • Import packages
      • Prediction accuracy on epistasis data
      • Get full prediction accuracy on en1
      • Get full prediction on env2
      • Get full prediction accuracy on epistasis
      • Compare dlim model to mechanistic model
    • Reproducibility 4: figure 5
      • Import packages
    • Reproducibility 5: figure 7
      • Import packages
      • Data Preprocessing
      • Load data for D-LIM
      • Visualization of the results
    • Reproducibility 6: figure 8
      • Import package
      • Subtle environment
      • Shorten the name of environment, making plot easier to show text
      • Strong environment
      • Comparison between infered phenotype from DLIM vs SVD method
D-LIM
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