An additive Gaussian process regression model for interpretable non-parametric analysis of longitudinal data
Research output: Contribution to journal › Article › Scientific › peer-review
- Department of Computer Science
- Centre of Excellence in Molecular Systems Immunology and Physiology Research Group, SyMMys
- Professorship Lähdesmäki H.
- Centre of Excellence in Computational Inference, COIN
- Probabilistic Machine Learning
- Helsinki Institute for Information Technology HIIT
- Professorship Vehtari A.
- Cardiff University
- Åbo Akademi University
- The University of Auckland
- Broad Institute
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.
|Number of pages||11|
|Publication status||Published - 17 Apr 2019|
|MoE publication type||A1 Journal article-refereed|
- OUT CROSS-VALIDATION, INFERENCE