timma (version 1.2.1)

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])