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Table of Contents

  • Installation Instructions
  • Modeling Tutorials
    • Tutorial 1: Built-in demonstration scripts
      • Evaluating a GE regression model
      • Visualizing an MPA regression model
      • Training a GE regression model
      • References
    • Tutorial 2: Protein DMS modeling using additive G-P maps
      • Training
      • Visualization
      • References
    • Tutorial 3: Splicing MPRA modeling using multiple built-in G-P maps
      • Training multiple models
      • Visualizing model performance
      • Visualizing pairwise model parameters
      • References
    • Tutorial 4: Protein DMS modeling using a biophysical G-P map
      • Defining a custom G-P map
      • Training a model with a custom G-P map
      • Visualizing models with custom G-P maps
      • References
    • Tutorial 5: Biophsyical modeling of the E. coli lac promoter using Sort-seq MPRA data
      • Training
      • References
  • Built-In Datasets
    • Overview of built-in datasets
    • gb1 dataset
      • Summary
      • Preprocessing
    • nisthal dataset
      • Summary
      • Preprocessing
    • amyloid dataset
      • Summary
      • Preprocessing
    • tdp43 dataset
      • Summary
      • Preprocessing
    • mpsa datasets
      • Summary
      • Preprocessing
    • sortseq dataset
      • Summary
      • Preprocessing
  • Underlying Mathematics
    • Notation
    • Genotype-phenotype (G-P) maps
      • Additive G-P maps
      • Neighbor G-P maps
      • Pairwise G-P maps
      • Black box G-P maps
    • MPA measurement processes
    • GE nonlinearities
    • GE noise models
      • Gaussian nose models
      • Cauchy noise models
      • Skewed-t noise models
    • Gauge modes and diffemorphic modes
      • Fixing diffeomorphic modes
      • Fixing the gauge
    • Metrics
      • Loss function
      • Variational information
      • Predictive information
      • Uncertainties in kNN estimates
    • References
  • API Reference
    • Tests
      • run_tests()
    • Examples
      • load_example_dataset()
      • load_example_model()
      • run_demo()
      • list_tutorials()
    • Load
      • load()
    • Visualization
      • heatmap()
      • heatmap_pairwise()
    • Models
      • Model
mavenn
  • Modeling Tutorials
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Modeling Tutorials

MAVE-NN comes with a variety of pre-trained models that users can load and apply. Models similar to these can be trained using the following notebooks

  • Tutorial 1: Built-in demonstration scripts
  • Tutorial 2: Protein DMS modeling using additive G-P maps
  • Tutorial 3: Splicing MPRA modeling using multiple built-in G-P maps
  • Tutorial 4: Protein DMS modeling using a biophysical G-P map
  • Tutorial 5: Biophsyical modeling of the E. coli lac promoter using Sort-seq MPRA data

© Copyright 2021, Ammar Tareen, Mahdi Kooshkbaghi, Justin B. Kinney.

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