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001     279931
005     20250722101137.0
024 7 _ |a 10.5281/ZENODO.15320204
|2 doi
037 _ _ |a DZNE-2025-00862
100 1 _ |a Breimann, Stephan
|0 P:(DE-2719)9001161
|b 0
245 _ _ |a Software: AAanalysis, v1.0.0
260 _ _ |a Zenodo
|c 2025
336 7 _ |a Software
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336 7 _ |a Software
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336 7 _ |a MISC
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336 7 _ |a Computer Program
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336 7 _ |a OTHER
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336 7 _ |a Software
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520 _ _ |a 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.
536 _ _ |a 352 - Disease Mechanisms (POF4-352)
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588 _ _ |a Dataset connected to DataCite
773 _ _ |a 10.5281/ZENODO.15320204
909 C O |o oai:pub.dzne.de:279931
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910 1 _ |a Deutsches Zentrum für Neurodegenerative Erkrankungen
|0 I:(DE-588)1065079516
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913 1 _ |a DE-HGF
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|l Neurodegenerative Diseases
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914 1 _ |y 2025
920 1 _ |0 I:(DE-2719)1110000-1
|k AG Steiner
|l Biochemistry of γ-Secretase
|x 0
980 _ _ |a sware
980 _ _ |a VDB
980 _ _ |a I:(DE-2719)1110000-1
980 _ _ |a UNRESTRICTED

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