Process the output of model evaluation for data source characterization purposes on the target prediction performance

process_characterization_target_prediction will process output formed by model evaluation to get a data frame containing performance measures in target gene prediction.

Usage

process_characterization_target_prediction(output_characterization)

Arguments

output_characterization

a list of lists containing the results of evaluation of different models (e.g. after execution of evaluate_model. Every sublist should contain at least the following elements: $model_name and $performances_target_prediction.

Value

A data frame containing the target gene prediction performances on every validation dataset for all the models that were evaluated.

Examples

if (FALSE) { # \dontrun{
library(dplyr)
settings <- lapply(expression_settings_validation, convert_expression_settings_evaluation)
weights_settings_loi <- prepare_settings_leave_one_in_characterization(lr_network, sig_network, gr_network, source_weights_df)
weights_settings_loi <- lapply(weights_settings_loi, add_hyperparameters_parameter_settings, lr_sig_hub = 0.25, gr_hub = 0.5, ltf_cutoff = 0, algorithm = "PPR", damping_factor = 0.8, correct_topology = TRUE)
doMC::registerDoMC(cores = 8)
output_characterization <- parallel::mclapply(weights_settings_loi[1:3], evaluate_model, lr_network, sig_network, gr_network, settings, calculate_popularity_bias_target_prediction = TRUE, calculate_popularity_bias_ligand_prediction = TRUE, ncitations, mc.cores = 3)
target_prediction_performances <- process_characterization_target_prediction(output_characterization)
} # }