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The pitfalls of using Gaussian Process Regression for normative modeling
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 acr...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8443061/ https://www.ncbi.nlm.nih.gov/pubmed/34525108 http://dx.doi.org/10.1371/journal.pone.0252108 |
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author | Xu, Bohan Kuplicki, Rayus Sen, Sandip Paulus, Martin P. |
author_facet | Xu, Bohan Kuplicki, Rayus Sen, Sandip Paulus, Martin P. |
author_sort | Xu, Bohan |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8443061 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-84430612021-09-16 The pitfalls of using Gaussian Process Regression for normative modeling Xu, Bohan Kuplicki, Rayus Sen, Sandip Paulus, Martin P. PLoS One Research Article 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. Public Library of Science 2021-09-15 /pmc/articles/PMC8443061/ /pubmed/34525108 http://dx.doi.org/10.1371/journal.pone.0252108 Text en © 2021 Xu et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Xu, Bohan Kuplicki, Rayus Sen, Sandip Paulus, Martin P. The pitfalls of using Gaussian Process Regression for normative modeling |
title | The pitfalls of using Gaussian Process Regression for normative modeling |
title_full | The pitfalls of using Gaussian Process Regression for normative modeling |
title_fullStr | The pitfalls of using Gaussian Process Regression for normative modeling |
title_full_unstemmed | The pitfalls of using Gaussian Process Regression for normative modeling |
title_short | The pitfalls of using Gaussian Process Regression for normative modeling |
title_sort | pitfalls of using gaussian process regression for normative modeling |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8443061/ https://www.ncbi.nlm.nih.gov/pubmed/34525108 http://dx.doi.org/10.1371/journal.pone.0252108 |
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