The pitfalls of using Gaussian Process Regression for normative modeling.
Journal
PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081
Informations de publication
Date de publication:
2021
2021
Historique:
received:
06
05
2021
accepted:
27
08
2021
entrez:
15
9
2021
pubmed:
16
9
2021
medline:
16
11
2021
Statut:
epublish
Résumé
Normative modeling, a group of methods used to quantify an individual's deviation from some expected trajectory relative to observed variability around that trajectory, has been used to characterize subject heterogeneity. Gaussian Processes Regression includes an estimate of variable uncertainty across the input domain, which at face value makes it an attractive method to normalize the cohort heterogeneity where the deviation between predicted value and true observation is divided by the derived uncertainty directly from Gaussian Processes Regression. However, we show that the uncertainty directly from Gaussian Processes Regression is irrelevant to the cohort heterogeneity in general.
Identifiants
pubmed: 34525108
doi: 10.1371/journal.pone.0252108
pii: PONE-D-21-15070
pmc: PMC8443061
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e0252108Subventions
Organisme : NIGMS NIH HHS
ID : P20 GM121312
Pays : United States
Déclaration de conflit d'intérêts
The authors have declared that no competing interests exist.
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