timma (version 1.2.0)

timmaBinary: Predicting drug sensitivity with binary drug-target interaction data

Description

A function to predict the drug sensitivity with binary drug-target interaction data using the original maximization and minimization rules

Usage

timmaBinary(drug_target_profile, sens, loo = TRUE)

Arguments

drug_target_profile

the drug-target interaction data. See timma.

sens

a drug sensitivity vector.

loo

a logical value indicating whether to use the leave-one-out cross-validation in the model selection process. By default, loo = TRUE.

Value

• A list containing the following components:
• dummy
  the predicted efficacy for target combinations that can be found from the training data
• error
  the prediction errors
• prediction
  predicted drug sensitivity

References

Tang J, Karhinen L, Xu T, Szwajda A, Yadav B, Wennerberg K, Aittokallio T. Target inhibition networks: predicting selective combinations of druggable targets to block cancer survival pathways. PLOS Computational Biology 2013; 9: e1003226.

Examples

data(tyner_interaction_binary)
data(tyner_sensitivity)
results<-timmaBinary(tyner_interaction_binary[, 1:6], tyner_sensitivity[,1])