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@MISC{Breimann:279931,
      author       = {Breimann, Stephan},
      title        = {{S}oftware: {AA}analysis, v1.0.0},
      address      = {Zenodo},
      reportid     = {DZNE-2025-00862},
      year         = {2025},
      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.},
      cin          = {AG Steiner},
      cid          = {I:(DE-2719)1110000-1},
      pnm          = {352 - Disease Mechanisms (POF4-352)},
      pid          = {G:(DE-HGF)POF4-352},
      typ          = {PUB:(DE-HGF)33},
      doi          = {10.5281/ZENODO.15320204},
      url          = {https://pub.dzne.de/record/279931},
}