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@ARTICLE{Makarious:163706,
      author       = {Makarious, Mary B. and Leonard, Hampton L. and Vitale, Dan
                      and Iwaki, Hirotaka and Sargent, Lana and Dadu, Anant and
                      Violich, Ivo and Hutchins, Elizabeth and Saffo, David and
                      Bandres-Ciga, Sara and Kim, Jonggeol Jeff and Song, Yeajin
                      and Maleknia, Melina and Bookman, Matt and Nojopranoto,
                      Willy and Campbell, Roy H. and Hashemi, Sayed Hadi and
                      Botia, Juan A. and Carter, John F. and Craig, David W. and
                      Van Keuren-Jensen, Kendall and Morris, Huw R. and Hardy,
                      John A. and Blauwendraat, Cornelis and Singleton, Andrew B.
                      and Faghri, Faraz and Nalls, Mike A.},
      title        = {{M}ulti-modality machine learning predicting
                      {P}arkinson’s disease},
      journal      = {npj Parkinson's Disease},
      volume       = {8},
      number       = {1},
      issn         = {2373-8057},
      address      = {London [u.a.]},
      publisher    = {Nature Publ. Group},
      reportid     = {DZNE-2022-00445},
      pages        = {35},
      year         = {2022},
      abstract     = {Personalized medicine promises individualized disease
                      prediction and treatment. The convergence of machine
                      learning (ML) and available multimodal data is key moving
                      forward. We build upon previous work to deliver multimodal
                      predictions of Parkinson’s disease (PD) risk and
                      systematically develop a model using GenoML, an automated ML
                      package, to make improved multi-omic predictions of PD,
                      validated in an external cohort. We investigated top
                      features, constructed hypothesis-free disease-relevant
                      networks, and investigated drug–gene interactions. We
                      performed automated ML on multimodal data from the
                      Parkinson’s progression marker initiative (PPMI). After
                      selecting the best performing algorithm, all PPMI data was
                      used to tune the selected model. The model was validated in
                      the Parkinson’s Disease Biomarker Program (PDBP) dataset.
                      Our initial model showed an area under the curve (AUC) of
                      $89.72\%$ for the diagnosis of PD. The tuned model was then
                      tested for validation on external data (PDBP, AUC
                      $85.03\%).$ Optimizing thresholds for classification
                      increased the diagnosis prediction accuracy and other
                      metrics. Finally, networks were built to identify gene
                      communities specific to PD. Combining data modalities
                      outperforms the single biomarker paradigm. UPSIT and PRS
                      contributed most to the predictive power of the model, but
                      the accuracy of these are supplemented by many smaller
                      effect transcripts and risk SNPs. Our model is best suited
                      to identifying large groups of individuals to monitor within
                      a health registry or biobank to prioritize for further
                      testing. This approach allows complex predictive models to
                      be reproducible and accessible to the community, with the
                      package, code, and results publicly available.},
      cin          = {Tübingen common / AG Gasser},
      ddc          = {610},
      cid          = {I:(DE-2719)6000018 / I:(DE-2719)1210000},
      pnm          = {353 - Clinical and Health Care Research (POF4-353)},
      pid          = {G:(DE-HGF)POF4-353},
      typ          = {PUB:(DE-HGF)16},
      pmc          = {pmc:PMC8975993},
      pubmed       = {pmid:35365675},
      doi          = {10.1038/s41531-022-00288-w},
      url          = {https://pub.dzne.de/record/163706},
}