# nf-core/drugresponseeval: Citations

## [DrugResponseEval](https://github.com/nf-core/drugresponseeval/)

> Bernett, J., Iversen, P., Picciani, M., Wilhelm, M., Baum, K., & List, M. **From Hype to Health Check: Critical Evaluation of Drug Response Prediction Models with DrEval.** > [10.1101/2025.05.26.655288](https://doi.org/10.1101/2025.05.26.655288) > _bioRxiv_, 2025-05.

## [nf-core](https://pubmed.ncbi.nlm.nih.gov/32055031/)

> Ewels PA, Peltzer A, Fillinger S, Patel H, Alneberg J, Wilm A, Garcia MU, Di Tommaso P, Nahnsen S. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol. 2020 Mar;38(3):276-278. doi: 10.1038/s41587-020-0439-x. PubMed PMID: 32055031.

## [Nextflow](https://pubmed.ncbi.nlm.nih.gov/28398311/)

> Di Tommaso P, Chatzou M, Floden EW, Barja PP, Palumbo E, Notredame C. Nextflow enables reproducible computational workflows. Nat Biotechnol. 2017 Apr 11;35(4):316-319. doi: 10.1038/nbt.3820. PubMed PMID: 28398311.

## Pipeline tools

- [DrEvalPy](https://github.com/daisybio/drevalpy): The pipeline mostly automates the individual steps of the DrEvalPy PyPI package.

  > Bernett, J, Iversen, P, Picciani, M, Wilhelm, M, Baum, K, List, M. Will be published soon.

- [CurveCurator](https://www.nature.com/articles/s41467-023-43696-z): For custom curve fitting on custom datasets. We also used it to re-process the response curves of GDSC1, GDSC2, CCLE, and CTRP.

  > Bayer, F.P., Gander, M., Kuster, B., The, M. CurveCurator: a recalibrated F-statistic to assess, classify, and explore significance of dose–response curves. Nature Communications. 2023 Nov;14(7902).

- [DIPK](https://doi.org/10.1093/bib/bbae153): Implemented model in the pipeline.

  > Li P, Jiang Z, Liu T, Liu X, Qiao H, Yao X. Improving drug response prediction via integrating gene relationships with deep learning. Briefings in Bioinformatics. 2024 May;25(3):bbae153.

- [MOLI](https://doi.org/10.1093/bioinformatics/btz318): Implemented model in the pipeline.

  > Sharifi-Noghabi H, Zolotareva O, Collins CC, Ester M. MOLI: multi-omics late integration with deep neural networks for drug response prediction. Bioinformatics. 2019 Jul;35(14):i501-9.

- [SRMF](https://doi.org/10.1186/s12885-017-3500-5): Implemented model in the pipeline.

  > Wang L, Li X, Zhang L, Gao Q. Improved anticancer drug response prediction in cell lines using matrix factorization with similarity regularization. BMC cancer. 2017 Dec;17:1-2.

- [SuperFELT](https://doi.org/10.1186/s12859-021-04146-z): Implemented model in the pipeline.

  > Park S, Soh J, Lee H. Super. FELT: supervised feature extraction learning using triplet loss for drug response prediction with multi-omics data. BMC bioinformatics. 2021 May 25;22(1):269.

## Software packaging/containerisation tools

- [Anaconda](https://anaconda.com)

  > Anaconda Software Distribution. Computer software. Vers. 2-2.4.0. Anaconda, Nov. 2016. Web.

- [Bioconda](https://pubmed.ncbi.nlm.nih.gov/29967506/)

  > Grüning B, Dale R, Sjödin A, Chapman BA, Rowe J, Tomkins-Tinch CH, Valieris R, Köster J; Bioconda Team. Bioconda: sustainable and comprehensive software distribution for the life sciences. Nat Methods. 2018 Jul;15(7):475-476. doi: 10.1038/s41592-018-0046-7. PubMed PMID: 29967506.

- [BioContainers](https://pubmed.ncbi.nlm.nih.gov/28379341/)

  > da Veiga Leprevost F, Grüning B, Aflitos SA, Röst HL, Uszkoreit J, Barsnes H, Vaudel M, Moreno P, Gatto L, Weber J, Bai M, Jimenez RC, Sachsenberg T, Pfeuffer J, Alvarez RV, Griss J, Nesvizhskii AI, Perez-Riverol Y. BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics. 2017 Aug 15;33(16):2580-2582. doi: 10.1093/bioinformatics/btx192. PubMed PMID: 28379341; PubMed Central PMCID: PMC5870671.

- [Docker](https://dl.acm.org/doi/10.5555/2600239.2600241)

  > Merkel, D. (2014). Docker: lightweight linux containers for consistent development and deployment. Linux Journal, 2014(239), 2. doi: 10.5555/2600239.2600241.

- [Singularity](https://pubmed.ncbi.nlm.nih.gov/28494014/)

  > Kurtzer GM, Sochat V, Bauer MW. Singularity: Scientific containers for mobility of compute. PLoS One. 2017 May 11;12(5):e0177459. doi: 10.1371/journal.pone.0177459. eCollection 2017. PubMed PMID: 28494014; PubMed Central PMCID: PMC5426675.
