Software

DZNE-2025-00862

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Software: AAanalysis, v1.0.0

Breimann, S.DZNE*

2025
Zenodo

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Please use a persistent id in citations: doi:10.5281/ZENODO.15320204

Abstract: First stable release of AAanalysis (Amino Acid analysis), a Python framework for interpretable sequence-based protein prediction. This version includes the foundational algorithms used in the publication 'Charting γ-secretase substrates by explainable AI' (Breimann & Kamp et al., Nature Communications, 2025): CPP (Comparative Physicochemical Profiling), a feature engineering method that identifies the most distinctive physicochemical properties between two sets of protein sequences, and dPULearn, a deterministic positive-unlabeled (PU) learning algorithm enabling robust classification from imbalanced and small datasets.

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Contributing Institute(s):

1. Biochemistry of γ-Secretase (AG Steiner)

Research Program(s):

1. 352 - Disease Mechanisms (POF4-352) (POF4-352)

Appears in the scientific report 2025

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Linked articles:

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Journal Article Breimann, S. (First author)DZNE* ; Kamp, F. ; Steiner, H.DZNE* ; Frishman, D.
AAontology: An Ontology of Amino Acid Scales for Interpretable Machine Learning
Journal of molecular biology 436(19), 168717 (2024) [10.1016/j.jmb.2024.168717]2024   Files BibTeX | EndNote: XML, Text | RIS

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Journal Article Breimann, S. (First author)DZNE* ; Kamp, F. ; Basset, G. ; Abou-Ajram, C. ; Güner, G.DZNE* ; Yanagida, K. ; Okochi, M. ; Müller, S. A.DZNE* ; Lichtenthaler, S. F.DZNE* ; Langosch, D. ; Frishman, D. ; Steiner, H. (Last author)DZNE*
Charting γ-secretase substrates by explainable AI.
Nature Communications 16(1), 5428 (2025) [10.1038/s41467-025-60638-z]2025   Files  Fulltext by Pubmed Central BibTeX | EndNote: XML, Text | RIS

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