Code repository supporting manuscript titled "Development of Chemical Categories for Per- and Polyfluoroalkyl Substances (PFAS) and the Proof-of-Concept Approach to the Identification of Potential Candidates for Tiered Toxicological Testing and Human Health Assessment".
Please cite: Patlewicz G., Judson R., Williams A. J., Butler T., Barone Jr. S ., Carstens K. E., Cowden J., Dawson J. L., Degitz S. J., Fay K., Henry T. R., Lowit A., Padilla S., Paul Friedman K., Phillips M. B., Turk D., Wambaugh J. F., Wetmore B. A., Thomas R. S. Development of Chemical Categories for Per- and Polyfluoroalkyl Substances (PFAS) and the Proof-of-Concept Approach to the Identification of Potential Candidates for Tiered Toxicological Testing and Human Health Assessment. Computational Toxicology 2024 https://doi.org/10.1016/j.comtox.2024.100327
Supplementary information referenced in the manuscript are available as a compressed tar file. Raw data files underpinning the analysis are available as a compressed tar file.
These files can be accessed at doi.org/10.23645/epacomptox.26524327
A prototype shiny-app to browse the landscape is available at patlewig.shinyapps.io/pfas_poc/ on a limited basis.
Code repository is provided on an "as is" basis and the user assumes responsibility for its use.
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to download or generate data
│ │ └── make_dataset.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── predict_model.py
│ │ └── train_model.py
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
│
└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io
Project based on the cookiecutter data science project template. #cookiecutterdatascience