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 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 source code of AAanalysis at GitHub.
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]
Indices and tables
Citation
If you use AAanalysis in your work, please cite the respective publication as follows:
- AAclust:
[Breimann24a] Breimann and Frishman (2024a), AAclust: k-optimized clustering for selecting redundancy-reduced sets of amino acid scales, bioRxiv.
- AAontology:
[Breimann24b] Breimann et al. (2024b), AAontology: An ontology of amino acid scales for interpretable machine learning, bioRxiv.
- CPP:
[Breimann24c] Breimann and Kamp et al. (2024c), Charting γ-secretase substrates by explainable AI, .. # Link if available
- dPULearn:
[Breimann24c] Breimann and Kamp et al. (2024c), Charting γ-secretase substrates by explainable AI, .. # Link if available