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@ARTICLE{He:907824,
      author       = {He, Tong and An, Lijun and Chen, Pansheng and Chen,
                      Jianzhong and Feng, Jiashi and Bzdok, Danilo and Holmes,
                      Avram J. and Eickhoff, Simon and Yeo, B. T. Thomas},
      title        = {{M}eta-matching as a simple framework to translate
                      phenotypic predictive models from big to small data},
      journal      = {Nature neuroscience},
      volume       = {25},
      number       = {1},
      issn         = {1097-6256},
      address      = {New York, NY},
      publisher    = {Nature America},
      reportid     = {FZJ-2022-02235},
      pages        = {795-804},
      year         = {2022},
      abstract     = {We propose a simple framework-meta-matching-to translate
                      predictive models from large-scale datasets to new unseen
                      non-brain-imaging phenotypes in small-scale studies. The key
                      consideration is that a unique phenotype from a boutique
                      study likely correlates with (but is not the same as)
                      related phenotypes in some large-scale dataset.
                      Meta-matching exploits these correlations to boost
                      prediction in the boutique study. We apply meta-matching to
                      predict non-brain-imaging phenotypes from resting-state
                      functional connectivity. Using the UK Biobank (N = 36,848)
                      and Human Connectome Project (HCP) (N = 1,019) datasets, we
                      demonstrate that meta-matching can greatly boost the
                      prediction of new phenotypes in small independent datasets
                      in many scenarios. For example, translating a UK Biobank
                      model to 100 HCP participants yields an eight-fold
                      improvement in variance explained with an average absolute
                      gain of $4.0\%$ (minimum = $-0.2\%,$ maximum = $16.0\%)$
                      across 35 phenotypes. With a growing number of large-scale
                      datasets collecting increasingly diverse phenotypes, our
                      results represent a lower bound on the potential of
                      meta-matching.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5254},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:35578132},
      UT           = {WOS:000799439200001},
      doi          = {10.1038/s41593-022-01059-9},
      url          = {https://juser.fz-juelich.de/record/907824},
}