Envision2017

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Our code is separated into five Jupyter Notebook files (.ipynb) and one R Markown file.

The Jupyter Notebooks contain the following:

• singleProteinModels.ipynb -- code for tuning hyperparameters and training models using the 8 protein data sets individually.

• envisionTuneTrainPredict.ipynb -- code to tune hyperparameters and train Envision with all eight data sets

• LOPOTuneTrain.ipynb -- train each leave-one-protein-out (LOPO) model to predict the protein data set not used in training.

• LOPO_10xCV.ipynb -- tune using tenfold cross-validation, train each leave-one-protein-out (LOPO) model to predict the protein data set not used in training.

• LOPO_predict_missingFeatureMuts.ipynb -- use each leave-one-protein-out (LOPO) model to predict the protein data set not used in training with missing features.

• LOPO_unnormalized.ipynb -- train each leave-one-protein-out (LOPO) model with unnormalized data and then predict protein data sets not used in training.

• downSamplingAnalysis.ipynb -- code to sample 6, 4,and 2 proteins as training data for model training

• Clinvar_analysis.ipynb -- use Envision to predict Clinvar mutations

The R Markdown contains the following:

• envision_figure_code.Rmd -- code for generating manuscript figures.

Notes:

• All necessary data files can be found in /data directory.

• Graphlab and Python dependencies (e.g. Numpy) are required to successfully run all .ipynb code.

• All code will be deposited in a public GitHub repository upon publication