An additive Gaussian process regression model for interpretable non-parametric analysis of longitudinal data

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An additive Gaussian process regression model for interpretable non-parametric analysis of longitudinal data. / Cheng, Lu; Ramchandran, Siddharth; Vatanen, Tommi; Lietzén, Niina; Lahesmaa, Riitta; Vehtari, Aki; Lähdesmäki, Harri.

In: Nature Communications, Vol. 10, No. 1, 1798, 17.04.2019, p. 1-11.

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@article{99995bce50034e8a85f2f769a04e0161,
title = "An additive Gaussian process regression model for interpretable non-parametric analysis of longitudinal data",
abstract = "Biomedical research typically involves longitudinal study designs where samples from individuals are measured repeatedly over time and the goal is to identify risk factors (covariates) that are associated with an outcome value. General linear mixed effect models are the standard workhorse for statistical analysis of longitudinal data. However, analysis of longitudinal data can be complicated for reasons such as difficulties in modelling correlated outcome values, functional (time-varying) covariates, nonlinear and non-stationary effects, and model inference. We present LonGP, an additive Gaussian process regression model that is specifically designed for statistical analysis of longitudinal data, which solves these commonly faced challenges. LonGP can model time-varying random effects and non-stationary signals, incorporate multiple kernel learning, and provide interpretable results for the effects of individual covariates and their interactions. We demonstrate LonGP’s performance and accuracy by analysing various simulated and real longitudinal -omics datasets.",
keywords = "OUT CROSS-VALIDATION, INFERENCE",
author = "Lu Cheng and Siddharth Ramchandran and Tommi Vatanen and Niina Lietz{\'e}n and Riitta Lahesmaa and Aki Vehtari and Harri L{\"a}hdesm{\"a}ki",
note = "| openaire: EC/H2020/663830/EU//SIRCIW",
year = "2019",
month = "4",
day = "17",
doi = "10.1038/s41467-019-09785-8",
language = "English",
volume = "10",
pages = "1--11",
journal = "Nature Communications",
issn = "2041-1723",
publisher = "Nature Publishing Group",
number = "1",

}

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TY - JOUR

T1 - An additive Gaussian process regression model for interpretable non-parametric analysis of longitudinal data

AU - Cheng, Lu

AU - Ramchandran, Siddharth

AU - Vatanen, Tommi

AU - Lietzén, Niina

AU - Lahesmaa, Riitta

AU - Vehtari, Aki

AU - Lähdesmäki, Harri

N1 - | openaire: EC/H2020/663830/EU//SIRCIW

PY - 2019/4/17

Y1 - 2019/4/17

N2 - Biomedical research typically involves longitudinal study designs where samples from individuals are measured repeatedly over time and the goal is to identify risk factors (covariates) that are associated with an outcome value. General linear mixed effect models are the standard workhorse for statistical analysis of longitudinal data. However, analysis of longitudinal data can be complicated for reasons such as difficulties in modelling correlated outcome values, functional (time-varying) covariates, nonlinear and non-stationary effects, and model inference. We present LonGP, an additive Gaussian process regression model that is specifically designed for statistical analysis of longitudinal data, which solves these commonly faced challenges. LonGP can model time-varying random effects and non-stationary signals, incorporate multiple kernel learning, and provide interpretable results for the effects of individual covariates and their interactions. We demonstrate LonGP’s performance and accuracy by analysing various simulated and real longitudinal -omics datasets.

AB - Biomedical research typically involves longitudinal study designs where samples from individuals are measured repeatedly over time and the goal is to identify risk factors (covariates) that are associated with an outcome value. General linear mixed effect models are the standard workhorse for statistical analysis of longitudinal data. However, analysis of longitudinal data can be complicated for reasons such as difficulties in modelling correlated outcome values, functional (time-varying) covariates, nonlinear and non-stationary effects, and model inference. We present LonGP, an additive Gaussian process regression model that is specifically designed for statistical analysis of longitudinal data, which solves these commonly faced challenges. LonGP can model time-varying random effects and non-stationary signals, incorporate multiple kernel learning, and provide interpretable results for the effects of individual covariates and their interactions. We demonstrate LonGP’s performance and accuracy by analysing various simulated and real longitudinal -omics datasets.

KW - OUT CROSS-VALIDATION

KW - INFERENCE

UR - http://www.scopus.com/inward/record.url?scp=85064561361&partnerID=8YFLogxK

U2 - 10.1038/s41467-019-09785-8

DO - 10.1038/s41467-019-09785-8

M3 - Article

VL - 10

SP - 1

EP - 11

JO - Nature Communications

JF - Nature Communications

SN - 2041-1723

IS - 1

M1 - 1798

ER -

ID: 33495759