These are machine learning classifiers that classify the average fold change in Crisper-cas9 dataset into four type of classes, where each class represent a quartile of the average fold change range. These models include:
Support vector machine (SVR)
k-nearest neighbors (KNN)
Random forest (RF)
Decision tree (DT)
Logistic regression (LR)
Naive bayes (NB)
Convolutional neural network (CNN)
All the classifers are implemented in python using Scikit-learn library except CNN is implemented in Keras with tensorflow back end. All classifiers except CNN are implemented in clf.py, clf_validate.py, clf_validate_onehot.py, where each script uses a different set of features exracted from the crisper-cas9 dataset for training the classifer. CNN is implemented in cnn_validate.py, cnn_embedding.py, cnn_validate_onehot.py, where each CNN script uses a different set of features exracted from the crisper-cas9 dataset for training the CNN.