Mixed effect gradient boosting for high-dimensional longitudinal data

High-dimensional longitudinal data present significant analytical challenges due to intricate within-subject correlations and an overwhelming ratio of predictors to observations. To address these challenges, we introduce Mixed-Effect Gradient Boosting (MEGB), a novel R package that synergises gradie...

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Published inScientific reports Vol. 15; no. 1; pp. 30927 - 24
Main Authors Olaniran, Oyebayo Ridwan, Olaniran, Saidat Fehintola, Allohibi, Jeza, Alharbi, Abdulmajeed Atiah, Alharbi, Nada MohammedSaeed
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 22.08.2025
Nature Publishing Group
Nature Portfolio
Subjects
639/705/531
639/705/794
Accuracy
Algorithms
Biomarkers
Biomedical research
Boosting Machine Learning Algorithms
Computer Simulation
Data analysis
Datasets
Decision trees
Efficiency
Feature selection
Female
Fetuses
Flexibility
Genomics
Gradient Boosting
High-dimensional Data
Humanities and Social Sciences
Humans
Longitudinal Data
Longitudinal Studies
Machine learning
Mixed Effect Model
multidisciplinary
Placenta - metabolism
Pregnancy
Proteomics
RNA - metabolism
Science
Science (multidisciplinary)
Sparsity
Trends
High-dimensional Data
Gradient Boosting
Longitudinal Data
Mixed Effect Model
Online AccessGet full text
ISSN2045-2322
2045-2322
DOI10.1038/s41598-025-16526-z

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Abstract High-dimensional longitudinal data present significant analytical challenges due to intricate within-subject correlations and an overwhelming ratio of predictors to observations. To address these challenges, we introduce Mixed-Effect Gradient Boosting (MEGB), a novel R package that synergises gradient boosting with mixed-effects modelling to simultaneously account for population-level fixed effects and subject-specific random variability. MEGB provides a unified framework for analysing repeated measures data that accommodates complex covariance structures while harnessing gradient boosting’s inherent regularisation for robust feature selection and prediction. In comprehensive simulations spanning linear and nonlinear data-generating processes, MEGB achieved 35-76% lower mean squared error (MSE) compared to state-of-the-art alternatives like Mixed-Effect Random Forests (MERF) and REEMForest, while maintaining 55-70% true positive rates for variable selection in ultra-high-dimensional regimes ( p = 2000 ) . Demonstrating practical utility, we applied MEGB to maternal cell-free plasma RNA data ( n = 12 subjects, p = 33 , 297 transcripts), where it identified 9 key placental transcripts driving fetal RNA dynamics across pregnancy trimesters.
AbstractList High-dimensional longitudinal data present significant analytical challenges due to intricate within-subject correlations and an overwhelming ratio of predictors to observations. To address these challenges, we introduce Mixed-Effect Gradient Boosting (MEGB), a novel R package that synergises gradient boosting with mixed-effects modelling to simultaneously account for population-level fixed effects and subject-specific random variability. MEGB provides a unified framework for analysing repeated measures data that accommodates complex covariance structures while harnessing gradient boosting’s inherent regularisation for robust feature selection and prediction. In comprehensive simulations spanning linear and nonlinear data-generating processes, MEGB achieved 35-76% lower mean squared error (MSE) compared to state-of-the-art alternatives like Mixed-Effect Random Forests (MERF) and REEMForest, while maintaining 55-70% true positive rates for variable selection in ultra-high-dimensional regimes (p=2000) . Demonstrating practical utility, we applied MEGB to maternal cell-free plasma RNA data (n=12 subjects, p=33,297 transcripts), where it identified 9 key placental transcripts driving fetal RNA dynamics across pregnancy trimesters.
Abstract High-dimensional longitudinal data present significant analytical challenges due to intricate within-subject correlations and an overwhelming ratio of predictors to observations. To address these challenges, we introduce Mixed-Effect Gradient Boosting (MEGB), a novel R package that synergises gradient boosting with mixed-effects modelling to simultaneously account for population-level fixed effects and subject-specific random variability. MEGB provides a unified framework for analysing repeated measures data that accommodates complex covariance structures while harnessing gradient boosting’s inherent regularisation for robust feature selection and prediction. In comprehensive simulations spanning linear and nonlinear data-generating processes, MEGB achieved 35-76% lower mean squared error (MSE) compared to state-of-the-art alternatives like Mixed-Effect Random Forests (MERF) and REEMForest, while maintaining 55-70% true positive rates for variable selection in ultra-high-dimensional regimes $$(p=2000)$$ ( p = 2000 ) . Demonstrating practical utility, we applied MEGB to maternal cell-free plasma RNA data $$(n=12$$ ( n = 12 subjects, $$p=33,297$$ p = 33 , 297 transcripts), where it identified 9 key placental transcripts driving fetal RNA dynamics across pregnancy trimesters.
High-dimensional longitudinal data present significant analytical challenges due to intricate within-subject correlations and an overwhelming ratio of predictors to observations. To address these challenges, we introduce Mixed-Effect Gradient Boosting (MEGB), a novel R package that synergises gradient boosting with mixed-effects modelling to simultaneously account for population-level fixed effects and subject-specific random variability. MEGB provides a unified framework for analysing repeated measures data that accommodates complex covariance structures while harnessing gradient boosting's inherent regularisation for robust feature selection and prediction. In comprehensive simulations spanning linear and nonlinear data-generating processes, MEGB achieved 35-76% lower mean squared error (MSE) compared to state-of-the-art alternatives like Mixed-Effect Random Forests (MERF) and REEMForest, while maintaining 55-70% true positive rates for variable selection in ultra-high-dimensional regimes . Demonstrating practical utility, we applied MEGB to maternal cell-free plasma RNA data subjects, transcripts), where it identified 9 key placental transcripts driving fetal RNA dynamics across pregnancy trimesters.
High-dimensional longitudinal data present significant analytical challenges due to intricate within-subject correlations and an overwhelming ratio of predictors to observations. To address these challenges, we introduce Mixed-Effect Gradient Boosting (MEGB), a novel R package that synergises gradient boosting with mixed-effects modelling to simultaneously account for population-level fixed effects and subject-specific random variability. MEGB provides a unified framework for analysing repeated measures data that accommodates complex covariance structures while harnessing gradient boosting’s inherent regularisation for robust feature selection and prediction. In comprehensive simulations spanning linear and nonlinear data-generating processes, MEGB achieved 35-76% lower mean squared error (MSE) compared to state-of-the-art alternatives like Mixed-Effect Random Forests (MERF) and REEMForest, while maintaining 55-70% true positive rates for variable selection in ultra-high-dimensional regimes \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(p=2000)$$\end{document} ( p = 2000 ) . Demonstrating practical utility, we applied MEGB to maternal cell-free plasma RNA data \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(n=12$$\end{document} ( n = 12 subjects, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p=33,297$$\end{document} p = 33 , 297 transcripts), where it identified 9 key placental transcripts driving fetal RNA dynamics across pregnancy trimesters.
High-dimensional longitudinal data present significant analytical challenges due to intricate within-subject correlations and an overwhelming ratio of predictors to observations. To address these challenges, we introduce Mixed-Effect Gradient Boosting (MEGB), a novel R package that synergises gradient boosting with mixed-effects modelling to simultaneously account for population-level fixed effects and subject-specific random variability. MEGB provides a unified framework for analysing repeated measures data that accommodates complex covariance structures while harnessing gradient boosting’s inherent regularisation for robust feature selection and prediction. In comprehensive simulations spanning linear and nonlinear data-generating processes, MEGB achieved 35-76% lower mean squared error (MSE) compared to state-of-the-art alternatives like Mixed-Effect Random Forests (MERF) and REEMForest, while maintaining 55-70% true positive rates for variable selection in ultra-high-dimensional regimes ( p = 2000 ) . Demonstrating practical utility, we applied MEGB to maternal cell-free plasma RNA data ( n = 12 subjects, p = 33 , 297 transcripts), where it identified 9 key placental transcripts driving fetal RNA dynamics across pregnancy trimesters.
High-dimensional longitudinal data present significant analytical challenges due to intricate within-subject correlations and an overwhelming ratio of predictors to observations. To address these challenges, we introduce Mixed-Effect Gradient Boosting (MEGB), a novel R package that synergises gradient boosting with mixed-effects modelling to simultaneously account for population-level fixed effects and subject-specific random variability. MEGB provides a unified framework for analysing repeated measures data that accommodates complex covariance structures while harnessing gradient boosting's inherent regularisation for robust feature selection and prediction. In comprehensive simulations spanning linear and nonlinear data-generating processes, MEGB achieved 35-76% lower mean squared error (MSE) compared to state-of-the-art alternatives like Mixed-Effect Random Forests (MERF) and REEMForest, while maintaining 55-70% true positive rates for variable selection in ultra-high-dimensional regimes ( p = 2000 ) . Demonstrating practical utility, we applied MEGB to maternal cell-free plasma RNA data ( n = 12 subjects, p = 33 , 297 transcripts), where it identified 9 key placental transcripts driving fetal RNA dynamics across pregnancy trimesters.High-dimensional longitudinal data present significant analytical challenges due to intricate within-subject correlations and an overwhelming ratio of predictors to observations. To address these challenges, we introduce Mixed-Effect Gradient Boosting (MEGB), a novel R package that synergises gradient boosting with mixed-effects modelling to simultaneously account for population-level fixed effects and subject-specific random variability. MEGB provides a unified framework for analysing repeated measures data that accommodates complex covariance structures while harnessing gradient boosting's inherent regularisation for robust feature selection and prediction. In comprehensive simulations spanning linear and nonlinear data-generating processes, MEGB achieved 35-76% lower mean squared error (MSE) compared to state-of-the-art alternatives like Mixed-Effect Random Forests (MERF) and REEMForest, while maintaining 55-70% true positive rates for variable selection in ultra-high-dimensional regimes ( p = 2000 ) . Demonstrating practical utility, we applied MEGB to maternal cell-free plasma RNA data ( n = 12 subjects, p = 33 , 297 transcripts), where it identified 9 key placental transcripts driving fetal RNA dynamics across pregnancy trimesters.
ArticleNumber 30927
Author Olaniran, Saidat Fehintola
Alharbi, Abdulmajeed Atiah
Alharbi, Nada MohammedSaeed
Allohibi, Jeza
Olaniran, Oyebayo Ridwan
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Keywords High-dimensional Data
Gradient Boosting
Longitudinal Data
Mixed Effect Model
Language English
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Snippet High-dimensional longitudinal data present significant analytical challenges due to intricate within-subject correlations and an overwhelming ratio of...
Abstract High-dimensional longitudinal data present significant analytical challenges due to intricate within-subject correlations and an overwhelming ratio of...
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SubjectTerms 639/705/531
639/705/794
Accuracy
Algorithms
Biomarkers
Biomedical research
Boosting Machine Learning Algorithms
Computer Simulation
Data analysis
Datasets
Decision trees
Efficiency
Feature selection
Female
Fetuses
Flexibility
Genomics
Gradient Boosting
High-dimensional Data
Humanities and Social Sciences
Humans
Longitudinal Data
Longitudinal Studies
Machine learning
Mixed Effect Model
multidisciplinary
Placenta - metabolism
Pregnancy
Proteomics
RNA - metabolism
Science
Science (multidisciplinary)
Sparsity
Trends
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Title Mixed effect gradient boosting for high-dimensional longitudinal data
URI https://link.springer.com/article/10.1038/s41598-025-16526-z
https://www.ncbi.nlm.nih.gov/pubmed/40847064
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Volume 15
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