TIMMA-R : an R package for predicting synergistic multi-targeted drug combinations in cancer cell lines or patient-derived samples

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http://hdl.handle.net/10138/225114

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He , L , Wennerberg , K , Aittokallio , T & Tang , J 2015 , ' TIMMA-R : an R package for predicting synergistic multi-targeted drug combinations in cancer cell lines or patient-derived samples ' , Bioinformatics , vol. 31 , no. 11 , pp. 1866-1868 . https://doi.org/10.1093/bioinformatics/btv067

Title: TIMMA-R : an R package for predicting synergistic multi-targeted drug combinations in cancer cell lines or patient-derived samples
Author: He, Liye; Wennerberg, Krister; Aittokallio, Tero; Tang, Jing
Contributor: University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Institute for Molecular Medicine Finland
Date: 2015-06-01
Language: eng
Number of pages: 3
Belongs to series: Bioinformatics
ISSN: 1367-4803
URI: http://hdl.handle.net/10138/225114
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.
Subject: DISCOVERY
SENSITIVITY
RESOURCE
3122 Cancers
1182 Biochemistry, cell and molecular biology
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