BlackBoxFilter: Train a Black Box Model for Drug Efficacy Prediction

In JRC-COMBINE/FORESEE: A Tool for the Systematic Comparison of Translational Drug Response Modeling Pipelines

Description

The BlackBoxFilter applies a machine learning algorithm to the feature matrix that was created from molecular data characterizing the samples of the TrainObject to create a model that is predictive of the drug response.

Usage

BlackBoxFilter(TrainObject, BlackBox = "ridge", nfoldCrossvalidation = 1,
  ...)

Arguments

TrainObject	Object that contains all data needed to train a model, including molecular data (such as gene expression, mutation, copy number variation, methylation, cancer type, etc. ) and drug response data
BlackBox	Modeling algorithm for training: The function 'linear' fits a linear regression model to the training data, The function 'ridge' fits a linear ridge regression model by Cule et al. (2012) to the training data, The function 'lasso' fits a lasso regression model from the glmnet package by Friedman et al. (2008) to the training data, The function 'elasticnet' fits an elastic net regression model from the glmnet package by Friedman et al. (2008) to the training data, The function 'svm' fits a support vector regression model from the e1071 package by Meyer and Chih-Chung (2017) to the training data, The function 'rf' fits a random forest regression model by Breiman (2001) to the training data The function 'rf_ranger' fits a fast random forest regression model by Marvin N. Wright (2018) to the training data The function 'tandem' fits a two-stage regression model by Nanne Aben (2017) to the training data. The function 'listInputOptions("BlackBoxFilter")' returns a list of the possible options. Instead of choosing one of the implemented options, a user-defined function can be used as an input.
nfoldCrossvalidation	# folds to use for crossvalidation while training the model. If put to one, the complete data of the TrainObject is used for training.

Value

ForeseeModel	A black box model trained on the TrainObject features that can be applied to new test data.
TrainObject	The TrainObject that was used to train the model.

JRC-COMBINE/FORESEE documentation built on Jan. 24, 2020, 1:19 a.m.