These are regression predictors that predict the average fold change of sgRNAs in Crisper-cas9 experiments. Those models include:
Support vector regression (SVR)
neural network regression (NNR)
Random forest regression (RFR)
linear regression (LR)
All the predictors are implemented in python using Scikit-learn library and Keras with tensorflow back end for implementing neural network regression model. Each model is trained with a set of features exracted from the crisper-cas9 dataset including:
Efficiency, Specificity, BS_Length, and Distance_exon_BS (D)