A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

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

Julkaisun tekijät: Lu Cheng, Siddharth Ramchandran, Tommi Vatanen, Niina Lietzén, Riitta Lahesmaa, Aki Vehtari, Harri Lähdesmäki

Julkaisuvuosi: 2019

Journal: Nature Communications

Lehden akronyymi: Nat Commun

Volyymi: 10

Sivujen määrä: 11

ISSN: 2041-1723

eISSN: 2041-1723

DOI: http://dx.doi.org/10.1038/s41467-019-09785-8

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


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 2021-24-06 at 10:05