timmaModel: Predicting drug sensitivity with binary drug-target...

In timma: Target Inhibition Interaction using Maximization and Minimization Averaging

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.

Author(s)

Liye He [e-mail]

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])

timma documentation built on May 2, 2019, 1:10 p.m.