Proteomics NLP Exploration

Exploring classification of proteomics literature and repository metadata using NLP.

Start at the notebook to view the results of the project or use the "Open in Colab" button above to start in interactive view.

Project Organization

├── LICENSE
├── build.py           <- luigi workflow with classes like 'Train' or 'Data' as build targets
├── README.md          <- The top-level README for developers using this project.
├── notebook.ipynb     <- The primary Jupyter notebook for documenting the analysis.
├── 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          <- Supplemental 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`
│
├── start_notebook.sh  <- builds and launches the dockerized notebook server for reproducible analysis
├── 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
│   │
│   └── test           <- Unit tests for source modules
│
└── tox.ini            <- tox file with settings for running tox; see tox.testrun.org

Running the Project

First, install the Docker client for your system.

Then, in a terminal, change to the project directory (the one containing this file) and:

• Test the installation using docker info
• Run python build.py to download data and run any preprocessing steps
• Start the notebook container by running sh start_notebook.sh from this directory

Now your notebook server is running! Open a browser and point to http://localhost. Next,

• Enter the password token displayed on the terminal
• Click on notebook.ipynb to open
• If you're accessing a finished notebook, you can browse, edit the code, and execute the cells to reproduce or alter the figures.
• If you're starting a new notebook, read the project guidelines in the notebook and start coding!

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