timmaBinary: 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 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

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

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