Welcome to the AAanalysis documentation!

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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 [email protected].

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, bioRxiv <https://www.biorxiv.org/content/10.1101/2024.02.04.578800v1>__.

AAontology: Breimann et al. (2024b), AAontology: An ontology of amino acid scales for interpretable machine learning, bioRxiv <https://www.biorxiv.org/content/10.1101/2023.08.03.551768v1>__.

CPP: Breimann and Kamp et al. (2024c), Charting γ-secretase substrates by explainable AI, .. # Link if available

dPULearn: Breimann and Kamp et al. (2024c), Charting γ-secretase substrates by explainable AI, .. # Link if available