Journal Article

FZJ-2022-02235

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Meta-matching as a simple framework to translate phenotypic predictive models from big to small data

He, T. ; An, L. ; Chen, P. ; Chen, J. ; Feng, J. ; Bzdok, D. ; Holmes, A. J.Extern* ; Eickhoff, S.FZJ* ; Yeo, B. T. T. (Corresponding author)

2022
Nature America New York, NY

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Please use a persistent id in citations: http://hdl.handle.net/2128/31560  doi:10.1038/s41593-022-01059-9

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.

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Contributing Institute(s):

1. Gehirn & Verhalten (INM-7)

Research Program(s):

1. 5254 - Neuroscientific Data Analytics and AI (POF4-525) (POF4-525)

Appears in the scientific report 2022

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Database coverage:
; ; BIOSIS Previews ; Biological Abstracts ; Clarivate Analytics Master Journal List ; Current Contents - Life Sciences ; Ebsco Academic Search ; Essential Science Indicators ; IF >= 25 ; JCR ; Nationallizenz ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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