DocTOR models and cross-validation dataset

Dataset necessary for DocTOR utility.

DocTOR (Direct fOreCast Target On Reaction), is a utility written in python3.9 (using the conda workframe) that allows the user to upload a list of Uniprot IDs and Adverse reactions (from the available models) in order to study the relationship between the two.

On output the program will assign a positive or negative class to the protein, assessing its possible involvement in the selected ADRs onset.

DocTOR exploits the data coming from T-ARDIS [https://doi.org/10.1093/database/baab068] to train different Machine Learning approaches (SVM, RF, NN) using network topological measurements as features.

The prediction coming from the single trained models are combined in a meta-predictor exploiting three different voting systems.

The results of the meta-predictor together with the ones from the single ML method will be available in the output log file (named "predictions_community" or "predictions_curated" based on the database type).

The DocTOR utility is avaiable at https://github.com/cristian931/DocTOR