aaanalysis 0.1.0

Welcome to the AAanalysis documentation

AAanalysis (Amino Acid analysis) is a Python framework for interpretable sequence-based protein prediction, providing the following algorithms:

• AAclust: k-optimized clustering wrapper framework to select redundancy-reduced sets of numerical scales (e.g., amino acid scales)

• 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.

Moreover, AAanalysis provides functions for loading protein benchmark datasets (load_data), amino acid scale sets (load_scales), and their in-depth two-level classification (AAontology).

Install

AAanalysis can be installed either from PyPi or conda-forge:

pip install -u aaanalysis
or
conda install -c conda-forge aaanalysis

You can also use the GitHub repository and install dependencies using poetry:

git clone https://github.com/breimanntools/aaanalysis
cd aaanalysis
poetry install

Contributions

We welcome bug reports, feature requests, and code contributions.

• Issues: Found a bug or have a feature idea? Please open an issue on our GitHub Repository.

• Code: Want to contribute code? Submit a Pull Request.

• Docs: See an error or room for improvement? Contributions to documentation are welcome.

For questions or suggestions, email stephanbreimann@gmail.com.

Citations

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

AAclust:

[Citation details and link if available]

AAontology:

Breimann, Kamp, Steiner, Frishman (2023), AAontology: An ontology of amino acid scales for interpretable machine learning, bioRxiv.

CPP:

[Citation details and link if available]

dPULearn:

[Citation details and link if available]