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            "description": "<h1>\n  <picture>\n    <source media=\"(prefers-color-scheme: dark)\" srcset=\"docs/images/nf-core-drugresponseeval_logo_dark.png\">\n    <img alt=\"nf-core/drugresponseeval\" src=\"docs/images/nf-core-drugresponseeval_logo_light.png\">\n  </picture>\n</h1>\n\n[![Open in GitHub Codespaces](https://img.shields.io/badge/Open_In_GitHub_Codespaces-black?labelColor=grey&logo=github)](https://github.com/codespaces/new/nf-core/drugresponseeval)\n[![GitHub Actions CI Status](https://github.com/nf-core/drugresponseeval/actions/workflows/nf-test.yml/badge.svg)](https://github.com/nf-core/drugresponseeval/actions/workflows/nf-test.yml)\n[![GitHub Actions Linting Status](https://github.com/nf-core/drugresponseeval/actions/workflows/linting.yml/badge.svg)](https://github.com/nf-core/drugresponseeval/actions/workflows/linting.yml)[![AWS CI](https://img.shields.io/badge/CI%20tests-full%20size-FF9900?labelColor=000000&logo=Amazon%20AWS)](https://nf-co.re/drugresponseeval/results)[![Cite with Zenodo](http://img.shields.io/badge/DOI-10.5281/zenodo.14779984-1073c8?labelColor=000000)](https://doi.org/10.5281/zenodo.14779984)\n\n[![nf-test](https://img.shields.io/badge/unit_tests-nf--test-337ab7.svg)](https://www.nf-test.com)\n\n[![Nextflow](https://img.shields.io/badge/version-%E2%89%A525.10.4-green?style=flat&logo=nextflow&logoColor=white&color=%230DC09D&link=https%3A%2F%2Fnextflow.io)](https://www.nextflow.io/)\n[![nf-core template version](https://img.shields.io/badge/nf--core_template-4.0.2-green?style=flat&logo=nfcore&logoColor=white&color=%2324B064&link=https%3A%2F%2Fnf-co.re)](https://github.com/nf-core/tools/releases/tag/4.0.2)\n[![run with conda](http://img.shields.io/badge/run%20with-conda-3EB049?labelColor=000000&logo=anaconda)](https://docs.conda.io/en/latest/)\n[![run with docker](https://img.shields.io/badge/run%20with-docker-0db7ed?labelColor=000000&logo=docker)](https://www.docker.com/)\n[![run with singularity](https://img.shields.io/badge/run%20with-singularity-1d355c.svg?labelColor=000000)](https://sylabs.io/docs/)\n[![Launch on Seqera Platform](https://img.shields.io/badge/Launch%20%F0%9F%9A%80-Seqera%20Platform-%234256e7)](https://cloud.seqera.io/launch?pipeline=https://github.com/nf-core/drugresponseeval)\n\n[![Get help on Slack](http://img.shields.io/badge/slack-nf--core%20%23drugresponseeval-4A154B?labelColor=000000&logo=slack)](https://nfcore.slack.com/channels/drugresponseeval)[![Follow on Bluesky](https://img.shields.io/badge/bluesky-%40nf__core-1185fe?labelColor=000000&logo=bluesky)](https://bsky.app/profile/nf-co.re)[![Follow on Mastodon](https://img.shields.io/badge/mastodon-nf__core-6364ff?labelColor=FFFFFF&logo=mastodon)](https://mstdn.science/@nf_core)[![Watch on YouTube](http://img.shields.io/badge/youtube-nf--core-FF0000?labelColor=000000&logo=youtube)](https://www.youtube.com/c/nf-core)\n\n![HiRSE Code Promo Badge](https://img.shields.io/badge/Promo-8db427?label=HiRSE&labelColor=005aa0&link=https%3A%2F%2Fgo.fzj.de%2FCodePromo)\n\n## Introduction\n\n# ![drevalpy_summary](assets/dreval_summary.svg)\n\n**DrEval** is a bioinformatics framework that includes a PyPI package (drevalpy) and a Nextflow\npipeline (this repo). DrEval ensures that evaluations are statistically sound, biologically\nmeaningful, and reproducible. DrEval simplifies the implementation of drug response prediction\nmodels, allowing researchers to focus on advancing their modeling innovations by automating\nstandardized evaluation protocols and preprocessing workflows. With DrEval, hyperparameter\ntuning is fair and consistent. With its flexible model interface, DrEval supports any model type,\nranging from statistical models to complex neural networks. By contributing your model to the\nDrEval catalog, you can increase your work's exposure, reusability, and transferability.\n\n1. The response data is loaded\n2. All models are trained and evaluated in a cross-validation setting\n3. For each CV split, the best hyperparameters are determined using a grid search per model\n4. The model is trained on the full training set (train & validation) with the best\n   hyperparameters to predict the test set\n5. If randomization tests are enabled, the model is trained on the full training set with the best\n   hyperparameters to predict the randomized test set\n6. If robustness tests are enabled, the model is trained N times on the full training set with the\n   best hyperparameters\n7. Plots are created summarizing the results\n\nFor baseline models, no randomization or robustness tests are performed.\n\n## Usage\n\n> [!NOTE]\n> If you are new to Nextflow and nf-core, please refer to [this page](https://nf-co.re/docs/get_started/environment_setup/overview) on how to set-up Nextflow. Make sure to [test your setup](https://nf-co.re/docs/get_started/run-your-first-pipeline) with `-profile test` before running the workflow on actual data.\n\nNow, you can run the pipeline using:\n\n```bash\nnextflow run nf-core/drugresponseeval \\\n   -profile <docker/singularity/.../institute> \\\n   --models <RandomForest,model2,...> \\\n   --baselines <NaiveMeanEffectsPredictor,baseline2,...> \\\n   --dataset_name <CTRPv2|CTRPv1|CCLE|GDSC1|GDSC2|BeatAML2|PDX_Bruna|TOYv1|TOYv2|custom_dataset>\n```\n\n> [!WARNING]\n> Please provide pipeline parameters via the CLI or Nextflow `-params-file` option. Custom config files including those provided by the `-c` Nextflow option can be used to provide any configuration _**except for parameters**_; see [docs](https://nf-co.re/docs/running/run-pipelines#using-parameter-files).\n\nFor more details and further functionality, please refer to the [usage documentation](https://nf-co.re/drugresponseeval/usage) and the [parameter documentation](https://nf-co.re/drugresponseeval/parameters).\n\n## Pipeline output\n\nTo see the results of an example test run with a full size dataset refer to the [results](https://nf-co.re/drugresponseeval/results) tab on the nf-core website pipeline page.\nFor more details about the output files and reports, please refer to the\n[output documentation](https://nf-co.re/drugresponseeval/output).\n\n## Credits\n\nnf-core/drugresponseeval was originally written by Judith Bernett (TUM) and Pascal Iversen (FU\nBerlin).\n\nWe thank the following people for their extensive assistance in the development of this pipeline:\n\n## Contributions and Support\n\nContributors to nf-core/drugresponseeval and the drevalpy PyPI package:\n\n- [Judith Bernett](https://github.com/JudithBernett) (TUM)\n- [Pascal Iversen](https://github.com/PascalIversen) (FU Berlin)\n- [Mario Picciani](https://github.com/picciama) (TUM)\n\nIf you would like to contribute to this pipeline, please see the [contributing guidelines](.github/CONTRIBUTING.md).\n\nFor further information or help, don't hesitate to get in touch on the [Slack `#drugresponseeval` channel](https://nfcore.slack.com/channels/drugresponseeval) (you can join with [this invite](https://nf-co.re/join/slack)).\n\n## Citations\n\nIf you use nf-core/drugresponseeval for your analysis, please cite it using the following doi: [10.5281/zenodo.14779984](https://doi.org/10.5281/zenodo.14779984)\n\n> Our corresponding publication is at doi [10.1101/2025.05.26.655288](doi.org/10.1101/2025.05.26.655288)\n>\n> 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.**\n>\n> _bioRxiv_, 2025-05.\n\nThe underlying data is available at doi: [10.5281/zenodo.12633909](https://doi.org/10.5281/zenodo.12633909).\n\nThe underlying python package is drevalpy, availably on [PyPI](https://pypi.org/project/drevalpy/) as standalone, for which we also have an extensive [ReadTheDocs Documentation](https://drevalpy.readthedocs.io/en/latest/).\n\nAn extensive list of references for the tools used by the pipeline can be found in the [`CITATIONS.md`](CITATIONS.md) file.\n\nYou can cite the `nf-core` publication as follows:\n\n> **The nf-core framework for community-curated bioinformatics pipelines.**\n>\n> Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.\n>\n> _Nat Biotechnol._ 2020 Feb 13. doi: [10.1038/s41587-020-0439-x](https://dx.doi.org/10.1038/s41587-020-0439-x).\n",
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