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@ARTICLE{Breimann:271147,
      author       = {Breimann, Stephan and Kamp, Frits and Steiner, Harald and
                      Frishman, Dmitrij},
      title        = {{AA}ontology: {A}n {O}ntology of {A}mino {A}cid {S}cales
                      for {I}nterpretable {M}achine {L}earning},
      journal      = {Journal of molecular biology},
      volume       = {436},
      number       = {19},
      issn         = {0022-2836},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier},
      reportid     = {DZNE-2024-01015},
      pages        = {168717},
      year         = {2024},
      abstract     = {Amino acid scales are crucial for protein prediction tasks,
                      many of them being curated in the AAindex database. Despite
                      various clustering attempts to organize them and to better
                      understand their relationships, these approaches lack the
                      fine-grained classification necessary for satisfactory
                      interpretability in many protein prediction problems. To
                      address this issue, we developed AAontology-a two-level
                      classification for 586 amino acid scales (mainly from
                      AAindex) together with an in-depth analysis of their
                      relations-using bag-of-word-based classification,
                      clustering, and manual refinement over multiple iterations.
                      AAontology organizes physicochemical scales into 8
                      categories and 67 subcategories, enhancing the
                      interpretability of scale-based machine learning methods in
                      protein bioinformatics. Thereby it enables researchers to
                      gain a deeper biological insight. We anticipate that
                      AAontology will be a building block to link amino acid
                      properties with protein function and dysfunctions as well as
                      aid informed decision-making in mutation analysis or protein
                      drug design.},
      keywords     = {Machine Learning / Amino Acids: chemistry / Computational
                      Biology: methods / Proteins: chemistry / Proteins:
                      metabolism / Databases, Protein / Cluster Analysis},
      cin          = {AG Haass / AG Steiner},
      ddc          = {610},
      cid          = {I:(DE-2719)1110007 / I:(DE-2719)1110000-1},
      pnm          = {352 - Disease Mechanisms (POF4-352)},
      pid          = {G:(DE-HGF)POF4-352},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:39053689},
      doi          = {10.1016/j.jmb.2024.168717},
      url          = {https://pub.dzne.de/record/271147},
}