FORESEE: a tool for the systematic comparison of translational drug response modeling pipelines
- Published
- Accepted
- Subject Areas
- Computational Biology, Genomics, Statistics, Data Mining and Machine Learning
- Keywords
- translational modeling, drug response, R-package, gene expression, prediction pipelines, systematic modeling, anti-cancer compounds
- Copyright
- © 2018 Turnhoff et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2018. FORESEE: a tool for the systematic comparison of translational drug response modeling pipelines. PeerJ Preprints 6:e27256v1 https://doi.org/10.7287/peerj.preprints.27256v1
Abstract
Translational models that utilize omics data generated in in vitro studies to predict the drug efficacy of anti-cancer compounds in patients are highly distinct, which complicates the benchmarking process for new computational approaches. In reaction to this, we introduce the uniFied translatiOnal dRug rESponsE prEdiction platform FORESEE, an open-source R-package. FORESEE not only provides a uniform data format for public cell line and patient data sets, but also establishes a standardized environment for drug response prediction pipelines, incorporating various state-of-the-art preprocessing methods, model training algorithms and validation techniques. The modular implementation of individual elements of the pipeline facilitates a straightforward development of combinatorial models, which can be used to re-evaluate and improve already existing pipelines as well as to develop new ones.
Availability and Implementation: FORESEE is licensed under GNU General Public License v3.0 and available at https://github.com/JRC-COMBINE/FORESEE .
Supplementary Information: Supplementary Files 1 and 2 provide detailed descriptions of the pipeline and the data preparation process, while Supplementary File 3 presents basic use cases of the package.
Contact: schuppert@combine.rwth-aachen.de
Author Comment
This is a preprint submission to PeerJ Preprints and has also been submitted to a peer reviewed journal.