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@ARTICLE{Tsai:284365,
      author       = {Tsai, Wei-Yun and Giesbertz, Pieter and Breimann, Stephan
                      and Lichtenthaler, Stefan and Frishman, Dmitrij},
      title        = {{M}ulti-platform integration of brain and {CSF} proteomes
                      reveals biomarker panels for {A}lzheimer's disease.},
      journal      = {Briefings in bioinformatics},
      volume       = {27},
      number       = {1},
      issn         = {1467-5463},
      address      = {Oxford [u.a.]},
      publisher    = {Oxford University Press},
      reportid     = {DZNE-2026-00133},
      pages        = {bbag012},
      year         = {2026},
      abstract     = {Alzheimer's disease (AD) is the leading cause of dementia
                      and represents a progressive, irreversible neurodegenerative
                      disorder. Given the complexity and heterogeneity of AD,
                      which involves numerous interrelated molecular pathways,
                      large-scale proteomics datasets are essential for robust
                      biomarker discovery. Comprehensive proteomic profiling
                      enables the unbiased identification of novel biomarkers
                      across diverse biological processes, thereby increasing the
                      likelihood of finding sensitive and specific candidates for
                      early diagnosis and therapeutic targeting. In this study, we
                      analyzed 28 large-scale proteomics datasets obtained from
                      the AD Knowledge Portal and published studies. The data
                      comprise tandem mass tag, label-free quantification, and
                      proximity extension assay measurements from brain tissue and
                      cerebrospinal fluid. To enhance analytical power, we
                      integrated these proteomic profiles with corresponding
                      clinical information to construct comprehensive feature sets
                      for subsequent machine learning analysis. Using Random
                      Forest and Logistic Regression models, we identified a panel
                      of proteins capable of distinguishing AD patients from
                      healthy controls. Several of these biomarkers have been
                      previously validated in the context of AD, while others
                      represent novel candidates not yet reported as
                      AD-associated. These newly identified biomarkers warrant
                      further experimental validation and hold promise for
                      improving early diagnosis as well as guiding the development
                      of targeted therapies for AD.},
      keywords     = {Alzheimer Disease: metabolism / Alzheimer Disease:
                      cerebrospinal fluid / Alzheimer Disease: diagnosis / Humans
                      / Biomarkers: cerebrospinal fluid / Biomarkers: metabolism /
                      Proteome: metabolism / Brain: metabolism / Proteomics:
                      methods / Machine Learning / CSF (Other) / biomarkers
                      (Other) / brain tissue (Other) / data integration (Other) /
                      machine learning (Other) / proteomics (Other) / Biomarkers
                      (NLM Chemicals) / Proteome (NLM Chemicals)},
      cin          = {AG Lichtenthaler / AG Steiner},
      ddc          = {004},
      cid          = {I:(DE-2719)1110006 / I:(DE-2719)1110000-1},
      pnm          = {352 - Disease Mechanisms (POF4-352)},
      pid          = {G:(DE-HGF)POF4-352},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:41608987},
      doi          = {10.1093/bib/bbag012},
      url          = {https://pub.dzne.de/record/284365},
}