Welcome to the AAanalysis documentation!

Package
Testing

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 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.

You can find the official documentation at Read the Docs.

Install

AAanalysis can be installed from PyPi:

pip install aaanalysis

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

pip install aaanalysis[pro]

Contributing

We appreciate bug reports, feature requests, or updates on documentation and code. For details, please refer to Contributing Guidelines. 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.

AAontology:

Breimann et al. (2024b), AAontology: An ontology of amino acid scales for interpretable machine learning, bioRxiv.

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