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

Titel: TIMMA-R : an R package for predicting synergistic multi-targeted drug combinations in cancer cell lines or patient-derived samples
Författare: He, Liye; Wennerberg, Krister; Aittokallio, Tero; Tang, Jing
Upphovmannens organisation: Institute for Molecular Medicine Finland
Krister Wennerberg / Principal Investigator
Tero Aittokallio / Principal Investigator
Bioinformatics
Datum: 2015-06-01
Språk: eng
Sidantal: 3
Tillhör serie: Bioinformatics
ISSN: 1367-4803
DOI: https://doi.org/10.1093/bioinformatics/btv067
Permanenta länken (URI): http://hdl.handle.net/10138/225114
Abstrakt: 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
Referentgranskad: Ja
Licens: cc_by_nc
Användningsbegränsning: openAccess
Parallelpublicerad version: publishedVersion


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