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TIMMA-R: An R Package for Predicting Synergistic Multi-Targeted Drug Combinations in Cancer Cell Lines or Patient-Derived Samples

Liye He, Krister Wennerberg, Tero Aittokallio, Jing Tang, TIMMA-R: An R Package for Predicting Synergistic Multi-Targeted Drug Combinations in Cancer Cell Lines or Patient-Derived Samples. Bioinformatics 31(11), 1866–8, 2015.

http://dx.doi.org/10.1093/bioinformatics/btv067

Abstract:

Network pharmacology-based prediction of multi-targeted drug combinations is becoming a promising strategy to improve anticancer efficacy and safety. We developed a logic-based network algorithm, called Target Inhibition Interaction using Maximization and Minimization Averaging (TIMMA), which predicts the effects of drug combinations based on their binary drug-target interactions and single-drug sensitivity profiles in a given cancer sample. Here, we report the R implementation of the algorithm (TIMMA-R), which is much faster than the original MATLAB code. The major extensions include modeling of multiclass drug-target profiles and network visualization. We also show that the TIMMA-R predictions are robust to the intrinsic noise in the experimental data, thus making it a promising high-throughput tool to prioritize drug combinations in various cancer types for follow-up experimentation or clinical applications.

BibTeX entry:

@ARTICLE{jHeWeAiTa15a,
  title = {TIMMA-R: An R Package for Predicting Synergistic Multi-Targeted Drug Combinations in Cancer Cell Lines or Patient-Derived Samples},
  author = {He, Liye and Wennerberg, Krister and Aittokallio, Tero and Tang, Jing},
  journal = {Bioinformatics},
  volume = {31},
  number = {11},
  pages = {1866–8},
  year = {2015},
}

Belongs to TUCS Research Unit(s): Biomathematics Research Unit (BIOMATH)

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