An implementation of a framework for drug sensitivity prediction from various genetic characterizations using ensemble approaches. Random Forests or Multivariate Random Forest predictive models can be generated from each genetic characterization that are then combined using a Least Square Regression approach. It also provides options for the use of different error estimation approaches of Leave-one-out, Bootstrap, N-fold cross validation and 0.632+Bootstrap along with generation of prediction confidence interval using Jackknife-after-Bootstrap approach.
|Author||Raziur Rahman, Ranadip Pal|
|Date of publication||2016-08-10 21:28:56|
|Maintainer||Raziur Rahman <email@example.com>|
build_forest_predict: Prediction using Random Forest or Multivariate Random Forest
build_single_tree: Model of a single tree of Random Forest or Multivariate...
Combination: Weights for combination of predictions from different data...
CombPredict: Integrated Prediction of Testing samples using Combination...
CombPredictSpecific: Prediction for testing samples using specific combination...
CrossValidation: Generate training and testing samples for cross validation
Dream_Dataset: NCI-Dream Drug Sensitivity Prediction Challenge Dataset
error_calculation: Error calculation for integrated model
Imputation: Imputation of a numerical vector
IntegratedPrediction: Integrated Prediction of Testing samples from integrated RF...
Node_cost: Information Gain
predicting: Prediction of testing sample in a node
single_tree_prediction: Prediction of Testing Samples for single tree
split_node: Splitting Criteria of all the nodes of the tree
splitt: Split of the Parent node