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

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Cheng, Lu
dc.contributor.author Ramchandran, Siddharth
dc.contributor.author Vatanen, Tommi
dc.contributor.author Lietzén, Niina
dc.contributor.author Lahesmaa, Riitta
dc.contributor.author Vehtari, Aki
dc.contributor.author Lähdesmäki, Harri
dc.date.accessioned 2019-05-06T09:20:08Z
dc.date.available 2019-05-06T09:20:08Z
dc.date.issued 2019-04-17
dc.identifier.citation Cheng , L , Ramchandran , S , Vatanen , T , Lietzén , N , Lahesmaa , R , Vehtari , A & Lähdesmäki , H 2019 , ' An additive Gaussian process regression model for interpretable non-parametric analysis of longitudinal data ' Nature Communications , vol. 10 , no. 1 , 1798 , pp. 1-11 . https://doi.org/10.1038/s41467-019-09785-8 en
dc.identifier.issn 2041-1723
dc.identifier.other PURE UUID: 99995bce-5003-4e8a-85f2-f769a04e0161
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/an-additive-gaussian-process-regression-model-for-interpretable-nonparametric-analysis-of-longitudinal-data(99995bce-5003-4e8a-85f2-f769a04e0161).html
dc.identifier.other PURE LINK: http://www.scopus.com/inward/record.url?scp=85064561361&partnerID=8YFLogxK
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/33495762/s41467_019_09785_8.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/37729
dc.description | openaire: EC/H2020/663830/EU//SIRCIW
dc.description.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. en
dc.format.extent 1-11
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher NATURE PUBLISHING GROUP
dc.relation info:eu-repo/grantAgreement/EC/H2020/663830/EU//SIRCIW
dc.relation.ispartofseries Nature Communications en
dc.relation.ispartofseries Volume 10, issue 1 en
dc.rights openAccess en
dc.subject.other Chemistry(all) en
dc.subject.other Biochemistry, Genetics and Molecular Biology(all) en
dc.subject.other Physics and Astronomy(all) en
dc.subject.other 113 Computer and information sciences en
dc.title An additive Gaussian process regression model for interpretable non-parametric analysis of longitudinal data en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Department of Computer Science
dc.contributor.department The University of Auckland
dc.contributor.department Åbo Akademi University
dc.contributor.department Probabilistic Machine Learning
dc.contributor.department Helsinki Institute for Information Technology HIIT
dc.subject.keyword OUT CROSS-VALIDATION
dc.subject.keyword INFERENCE
dc.subject.keyword Chemistry(all)
dc.subject.keyword Biochemistry, Genetics and Molecular Biology(all)
dc.subject.keyword Physics and Astronomy(all)
dc.subject.keyword 113 Computer and information sciences
dc.identifier.urn URN:NBN:fi:aalto-201905062847
dc.identifier.doi 10.1038/s41467-019-09785-8
dc.type.version publishedVersion


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