Prediction of ligand activity prediction by a model trained on ligand importance scores.

model_based_ligand_activity_prediction Predict the activity state of a ligand based on a classification model that was trained to predict ligand activity state based on ligand importance scores.

Usage

model_based_ligand_activity_prediction(importances, model, normalization)

Arguments

importances

A data frame containing at least folowing variables: $setting, $test_ligand, $ligand and one or more feature importance scores. $test_ligand denotes the name of a possibly active ligand, $ligand the name of the truely active ligand.

model

A model object of a classification object as e.g. generated via caret.

normalization

Way of normalization of the importance measures: "mean" (classifcal z-score) or "median" (modified z-score)

Value

A data frame containing the ligand importance scores and the probabilities that according to the trained model, the ligands are active based on their importance scores.

Examples

if (FALSE) { # \dontrun{
settings <- lapply(expression_settings_validation[1:5], convert_expression_settings_evaluation)
settings_ligand_pred <- convert_settings_ligand_prediction(settings, all_ligands = unlist(extract_ligands_from_settings(settings, combination = FALSE)), validation = TRUE, single = TRUE)

weighted_networks <- construct_weighted_networks(lr_network, sig_network, gr_network, source_weights_df)
ligands <- extract_ligands_from_settings(settings_ligand_pred, combination = FALSE)
ligand_target_matrix <- construct_ligand_target_matrix(weighted_networks, lr_network, ligands)
ligand_importances <- dplyr::bind_rows(lapply(settings_ligand_pred, get_single_ligand_importances, ligand_target_matrix))
evaluation <- evaluate_importances_ligand_prediction(ligand_importances, "median", "lda")

settings <- lapply(expression_settings_validation[5:10], convert_expression_settings_evaluation)
settings_ligand_pred <- convert_settings_ligand_prediction(settings, all_ligands = unlist(extract_ligands_from_settings(settings, combination = FALSE)), validation = FALSE, single = TRUE)
ligands <- extract_ligands_from_settings(settings_ligand_pred, combination = FALSE)
ligand_target_matrix <- construct_ligand_target_matrix(weighted_networks, lr_network, ligands)
ligand_importances <- dplyr::bind_rows(lapply(settings_ligand_pred, get_single_ligand_importances, ligand_target_matrix, known = FALSE))
activity_predictions <- model_based_ligand_activity_prediction(ligand_importances, evaluation$model, "median")
print(head(activity_predictions))
} # }