TIMMA-R: an R package for predicting synergistic multi-targeted drug combinations in cancer cell lines or patient-derived samples
Research output: Contribution to journal › Journal article › Research › peer-review
UNLABELLED: 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/.
Original language | English |
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Journal | Bioinformatics (Online) |
Volume | 31 |
Issue number | 11 |
Pages (from-to) | 1866-8 |
Number of pages | 3 |
ISSN | 1367-4811 |
DOIs | |
Publication status | Published - 1 Jun 2015 |
Externally published | Yes |
- Algorithms, Antineoplastic Combined Chemotherapy Protocols/pharmacology, Cell Line, Tumor, Drug Discovery, Drug Synergism, Humans, Neoplasms/drug therapy, Software
Research areas
ID: 199429384