aaanalysis 1.0.2
Python framework for interpretable protein prediction
License: BSD-3-Clause
Requires Python: <3.13,>=3.9
Keywords: protein, prediction, bioinformatics, machine learning, interpretable AI

Welcome to the AAanalysis documentation!
========================================
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   * - **Package**
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   * - **Testing**
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**AAanalysis** (Amino Acid analysis) is a Python framework for interpretable sequence-based protein prediction.
Its foundation are the following algorithms:

- **CPP**: Comparative Physicochemical Profiling, a feature engineering algorithm comparing two sets of protein
  sequences to identify the set of most distinctive features.
- **dPULearn**: deterministic Positive-Unlabeled (PU) Learning algorithm to enable training on
  unbalanced and small datasets.
- **AAclust**: k-optimized clustering wrapper framework to select redundancy-reduced sets of numerical scales
  (e.g., amino acid scales).

In addition, AAanalysis provide functions for loading various protein benchmark datasets, amino acid scales,
and their two-level classification (**AAontology**). We combined **CPP** with the explainable
AI  `SHAP <https://shap.readthedocs.io/en/latest/index.html>`_ framework to explain sample level predictions with
single-residue resolution.

If you are looking to make publication-ready plots with a view lines of code, see our
`Plotting Prelude <https://aaanalysis.readthedocs.io/en/latest/generated/plotting_prelude.html>`_.


You can find the official documentation at `Read the Docs <https://aaanalysis.readthedocs.io/en/latest/>`_.

Install
=======
**AAanalysis** can be installed from `PyPi <https://pypi.org/project/aaanalysis>`_:

.. code-block:: bash

   pip install aaanalysis

For extended features, including our explainable AI module, please use the 'professional' version:

.. code-block:: bash

   pip install aaanalysis[pro]

Contributing
============
We appreciate bug reports, feature requests, or updates on documentation and code. For details, please refer to
`Contributing Guidelines <CONTRIBUTING.rst>`_. These include specifics about AAanalysis and also notes on Test
Guided Development (TGD) using ChatGPT. For further questions or suggestions, please email stephanbreimann@gmail.com.

Citations
=========
If you use AAanalysis in your work, please cite the respective publication as follows:

**AAclust**:
   Breimann and Frishman (2024a),
   *AAclust: k-optimized clustering for selecting redundancy-reduced sets of amino acid scales*,
   `Bioinformatics Advances <https://academic.oup.com/bioinformaticsadvances/article/4/1/vbae165/7852846>`__.

**AAontology**:
   Breimann *et al.* (2024b),
   *AAontology: An ontology of amino acid scales for interpretable machine learning*,
   `Journal of Molecular Biology <https://www.sciencedirect.com/science/article/pii/S0022283624003267>`__.

**CPP**:
   Breimann and Kamp *et al.* (2025),
   *Charting γ-secretase substrates by explainable AI*,
   **Nature Communications** (link if available)

**dPULearn**:
   Breimann and Kamp *et al.* (2025),
   *Charting γ-secretase substrates by explainable AI*,
   **Nature Communications** (link if available)