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| Hauptseite > Publikationsdatenbank > Meta-matching as a simple framework to translate phenotypic predictive models from big to small data > print |
| Hauptseite > Publikationsdatenbank > Meta-matching as a simple framework to translate phenotypic predictive models from big to small data > print |
| 001 | 907824 | ||
| 005 | 20230123110622.0 | ||
| 024 | 7 | _ | |a 10.1038/s41593-022-01059-9 |2 doi |
| 024 | 7 | _ | |a 1097-6256 |2 ISSN |
| 024 | 7 | _ | |a 1546-1726 |2 ISSN |
| 024 | 7 | _ | |a 2128/31560 |2 Handle |
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| 024 | 7 | _ | |a pmid:35578132 |2 pmid |
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| 037 | _ | _ | |a FZJ-2022-02235 |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a He, Tong |0 P:(DE-HGF)0 |b 0 |
| 245 | _ | _ | |a Meta-matching as a simple framework to translate phenotypic predictive models from big to small data |
| 260 | _ | _ | |a New York, NY |c 2022 |b Nature America |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1658897823_15482 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
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| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a 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. |
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| 588 | _ | _ | |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de |
| 700 | 1 | _ | |a An, Lijun |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Chen, Pansheng |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Chen, Jianzhong |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Feng, Jiashi |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Bzdok, Danilo |0 P:(DE-Juel1)136848 |b 5 |
| 700 | 1 | _ | |a Holmes, Avram J. |0 P:(DE-HGF)0 |b 6 |
| 700 | 1 | _ | |a Eickhoff, Simon |0 P:(DE-Juel1)131678 |b 7 |
| 700 | 1 | _ | |a Yeo, B. T. Thomas |0 P:(DE-HGF)0 |b 8 |e Corresponding author |
| 773 | _ | _ | |a 10.1038/s41593-022-01059-9 |0 PERI:(DE-600)1494955-6 |n 1 |p 795-804 |t Nature neuroscience |v 25 |y 2022 |x 1097-6256 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/907824/files/s41593-022-01059-9.pdf |
| 856 | 4 | _ | |y OpenAccess |u https://juser.fz-juelich.de/record/907824/files/HeTong_preprint.pdf |
| 909 | C | O | |o oai:juser.fz-juelich.de:907824 |p openaire |p open_access |p VDB |p driver |p dnbdelivery |
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