ArticleNature communications2019
An additive Gaussian process regression model for interpretable non-parametric analysis of longitudinal data.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 113 citations in OpenAlex.
- LGTM: Gaussian process modulated neural topic modeling for longitudinal microbiome.Gut microbes · 2026Article
- Modelling non-linear personality change surrounding transitions: A review of statistical approaches.European journal of personality · 2026Article
- Refined trajectory smoothing and deep learning classification of human sperm motility.Human reproduction (Oxford, England) · 2026Article
- Foliar Distribution of Seed-Applied Fluxapyroxad in Barley.Journal of agricultural and food chemistry · 2026Article
- Longitudinal omics data analysis: approaches and applications.Computational and structural biotechnology journal · 2026Review
- cytoGPNet: Enhancing clinical outcome prediction accuracy using longitudinal cytometry data in small cohort studies.Patterns (New York, N.Y.) · 2025Article
- Identifiability for Gaussian Processes with Holomorphic Kernels.... International Conference on Learning Representations · 2025Article
- Covariance Assisted Multivariate Penalized Additive Regression (CoMPAdRe).Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2025Article
- Forecasting SARS-CoV-2 spike protein evolution from small data by deep learning and regression.Frontiers in systems biology · 2024Article
- The prevalence of tuberculosis infection among foreign-born Canadians: a modelling study.CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne · 2023Article
- On the Identifiability and Interpretability of Gaussian Process Models.Advances in neural information processing systems · 2023Article
- Serum APOC1 levels are decreased in young autoantibody positive children who rapidly progress to type 1 diabetes.Scientific reports · 2023Article
- Machine Learning and Hybrid Methods for Metabolic Pathway Modeling.Methods in molecular biology (Clifton, N.J.) · 2023Review
- Benchmarking tools for detecting longitudinal differential expression in proteomics data allows establishing a robust reproducibility optimization regression approach.Nature communications · 2022Article
- Neighboring mutation-mediated enhancement of dengue virus infectivity and spread.EMBO reports · 2022Article
- Adaptive Weighted Neighbors Method for Sensitivity Analysis.Interdisciplinary sciences, computational life sciences · 2022Article
- Impacts of a delayed and slow-paced vaccination on cases and deaths during the COVID-19 pandemic: a modelling study.Journal of the Royal Society, Interface · 2022Article
- Machine learning for optimizing daily COVID-19 vaccine dissemination to combat the pandemic.Health and technology · 2022Article
- Effect of Environmental Exposures on the Gut Microbiota from Early Infancy to Two Years of Age.Microorganisms · 2021Article
- Microbial trend analysis for common dynamic trend, group comparison, and classification in longitudinal microbiome study.BMC genomics · 2021Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.