aaanalysis 0.1.2

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).

If you are looking to make publication-ready plots with a view lines of code, see our Plotting Prelude.

Install

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

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

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 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]