In order to reproduce the results, the author used code inspired by TeachopenCADD . The code and notebook are provided in the github for reproducibility. For each Uniprot ID, Random Forest (RF) and Artificial Neural Network(ANN) were applied to both imbalanced and balanced data using scikit-learn library. The number of estimators was set equal to 10, and the criterion equal to entropy. For ANN, the hidden layer size was set equal to (30,3). In the case of balanced data, hyperparameter optimization (named as balanced_opt in the supplementary materials) was performed for each number of estimator from 1 to 20, and hidden layer sizes were considered to be one of the following: (5,3), , or (30,3).