timma (version 1.2.1)

timmaModel: 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 one.sided TIMMA model

Usage

timmaModel(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 matrix

error

the prediction errors

prediction

predicted drug sensitivity

The difference between timmaModel and timmaBinary is timmaModel returns the predicted efficacy matrix of all possible target combinations while timmaBinary not.

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<-timmaModel(tyner_interaction_binary[, 1:6], tyner_sensitivity[,1])