timma (version 1.2.0)

timma-package: Target Inhibition inference using Maximization and Minimization Averaging

Description

Due to the exponentially increasing number of potential drug and target combinations, it is meaningful to select the most promising combinations based on computational models. The TIMMA model was proposed to utilize drug-target interaction data and drug sensitivity data to infer the effects of drug combinations. This R package TIMMA is the implementation of the TIMMA model. It consists of the following components: (a) model selection using the sffs algorithm; (b) model construction using the maximization and minimization averaging rules; (c) ranking of drug combinations according to their synergy scores and a target inhibition network.

Details

ll{ Package: TIMMA Type: Package Version: 0.99.0 Date: 2014-10-07 License: Artistic License 2.0 }

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)
median_sensitivity<-tyner_sensitivity[, 1]
results<-timma(tyner_interaction_binary, median_sensitivity)