J. Elyse Borgert

J. Elyse Borgert

Research Statistician Developer

Research

You can also find my articles on my Google Scholar profile.

Machine Learning Characterizes Early Gait Biomechanical Phenotypes Linked to Cartilage Composition and Biomarker Profiles at 1 Year Post-ACL Reconstruction

A. N. Buck, J.E. Borgert, H. Lee, C. Lisee, C. B¨ uttner, E. Bjornsen, N. Favoreto, X. Li, L. Arbeeva, L. F. Callahan, et al., “Machine Learning Characterizes Early Gait Biomechanical Phenotypes Linked to Cartilage Composition and Biomarker Profiles at 1 Year Post-ACL Reconstruction,” Osteoarthritis and Cartilage, vol. 34, S157–S158, 2026.

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Elastic Shape Analysis of Movement Data

J.E. Borgert, J. Hannig, J. D. Tucker, L. Arbeeva, A. N. Buck, Y. M. Golightly, S. P. Messier, A. E. Nelson, and J. Marron, “Elastic shape analysis of movement data,” Journal of the American Statistical Association, vol. 121, no. 553, pp. 126–136, 2026.

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A Bernstein-von Mises Theorem for Generalized Fiducial Distributions

J. E. Borgert and J. Hannig, “A Bernstein-von Mises Theorem for Generalized Fiducial Distributions,” Under subsequent review at Bayesian Analysis. [Online]. Available: https://arxiv.org/abs/2401.17961.

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A Vision of How Low-coverage Sequence Data Should Contribute to Genetic Evaluation in the Future

R. M. Thallman, J. E. Borgert, B. N. Engle, J. W. Keele, W. M. Snelling, C. Gondro, and L. A. Kuehn, “A vision of how low-coverage sequence data should contribute to genetic evaluation in the future,” Journal of Animal Science, skaf294, 2025.

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Determining Optimal Diet/Exercise Treatment Assignment for Patients with Symptomatic Knee Osteoarthritis Using Baseline Gait Forces

A. M. Kostic, L. Arbeeva, X. Jiang, Y. M. Golightly, S. P. Messier, R. F. Loeser, J. E. Borgert, J. Marron, M. R. Kosorok, and A. E. Nelson, “Determining Optimal Diet/Exercise Treatment Assignment for Patients with Symptomatic Knee Osteoarthritis Using Baseline Gait Forces,” Osteoarthritis and Cartilage Open, p. 100 691, 2025.

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A Biologically Motivated Nonlinear Latent Variable Genetic Model

B. N. Engle, R. M. Thallman, J. E. Borgert, J. W. Keele, W. M. Snelling, C. Gondro, and L. A. Kuehn, “A Biologically Motivated Nonlinear Latent Variable Genetic Model,” Accepted with revisions at Journal of Animal Science.

Influence of Sociodemographic and Clinical Features on Ground Reaction Force Variability Among Individuals with Symptomatic Knee Osteoarthritis

Y. M. Golightly, J. E. Borgert, S. Xiang, E. Wellsandt, L. Arbeeva, R. F. Loeser, S. P. Messier, A. E. Nelson, and J. Marron, “Influence of Sociodemographic and Clinical Features on Ground Reaction Force Variability Among Individuals with Symptomatic Knee Osteoarthritis,” R&R at Osteoarthritis and Cartilage Open.

Vertical Ground Reaction Force Variability is Associated with Clinical Features in Individuals with Knee OA and Overweight/Obesity: A Novel Machine Learning Analysis of the IDEA Trial

A. N. Buck, J. E. Borgert, H. Lee, L. Arbeeva, Y. M. Golightly, R. F. Loeser, S. P. Messier, B. Pietrosimone, A. E. Nelson, and J. Marron, “Vertical Ground Reaction Force Variability is Associated with Clinical Features in Individuals with Knee OA and Overweight/Obesity: A Novel Machine Learning Analysis of the IDEA Trial,” Osteoarthritis and Cartilage, vol. 33, S160–S161, 2025.

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Comments on: Shape-based functional data analysis

J. E. Borgert and J. S. Marron, “Comments on: Shape-based functional data analysis,” TEST, 2024. [Online]. Available: https://doi.org/10.1007/s11749-023-00914-6

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A machine learning approach to identify patterns of variation among collagen biomarkers and clinical features in a community-based cohort

L. Arbeeva, E. Borgert, T. Keefe, A.-C. Bay-Jensen, R. Loeser, Y. Golightly, J. Marron, and A. Nelson, “A machine learning approach to identify patterns of variation among collagen biomarkers and clinical features in a community-based cohort,” Osteoarthritis and Cartilage, vol. 31, no. 5, pp. 677–678, 2023.

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Persistent Topology of Protein Space

W. Hamilton, J. E. Borgert, T. Hamelryck, and J. Marron, “Persistent topology of protein space,” Research in Computational Topology 2, p. 223, 2022.

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Testcrosses are an efficient strategy for identifying cis-regulatory variation: Bayesian analysis of allele-specific expression (BayesASE)

B. R. Miller, A. M. Morse, J. E. Borgert, Z. Liu, K. Sinclair, G. Gamble, F. Zou, J. R. Newman, L. G. Leon-Novelo, F. Marroni et al., “Testcrosses are an efficient strategy for identifying cis-regulatory variation: Bayesian analysis of allele-specific expression (BayesASE),” G3, vol. 11, no. 5, 2021.

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