A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä

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




TekijätLu Cheng, Siddharth Ramchandran, Tommi Vatanen, Niina Lietzén, Riitta Lahesmaa, Aki Vehtari, Harri Lähdesmäki

Julkaisuvuosi2019

JournalNature Communications

Lehden akronyymiNat Commun

Artikkelin numero1798

Vuosikerta10

Aloitussivu1798

Sivujen määrä11

ISSN2041-1723

eISSN2041-1723

DOIhttps://doi.org/10.1038/s41467-019-09785-8

Verkko-osoitehttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6470127/pdf/41467_2019_Article_9785.pdf

Rinnakkaistallenteen osoitehttps://research.utu.fi/converis/portal/detail/Publication/40092706


Tiivistelmä

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.


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Last updated on 2024-26-11 at 23:10