General Information
| Description | An R package for predicting synergistic multi-targeted drug combinations in cancer cell lines or patient-derived samples |
| Homepage | http://cran.r-project.org/web/packages/timma/ |
Publications
TIMMA-R: an R package for predicting synergistic multi-targeted drug combinations in cancer cell lines or patient-derived samples
Summary:
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.
Availability and implementation:
TIMMA-R source code is freely available at http://cran.r-project.org/web/packages/timma/ .
Contact:
jing.tang@helsinki.fi
Supplementary information:
Supplementary data are available at Bioinformatics online.
Related Tools
SynergyFinder
Published April 2017
A web application for analyzing drug combination dose–response matrix data
LRSSL
Published January 2017
Predict and interpret drug–disease associations based on data integration using sparse subspace learning
IntegratedMRF
Published February 2017
Random forest-based framework for integrating prediction from different data types
FAIR Evaluation
Sorry, you must be signed in to evaluate this object.
