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Future Blog Post
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Blog Post number 4
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Blog Post number 3
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Blog Post number 2
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Blog Post number 1
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portfolio
Portfolio item number 1
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publications
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.
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.
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.
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
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.
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.
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.
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.
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.
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.
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.
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.
talks
Talk 1 on Relevant Topic in Your Field
This is a description of your talk, which is a markdown file that can be all markdown-ified like any other post. Yay markdown!
Tutorial 1 on Relevant Topic in Your Field
Talk 2 on Relevant Topic in Your Field
Conference Proceeding talk 3 on Relevant Topic in Your Field
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
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Teaching experience 2
This is a description of a teaching experience. You can use markdown like any other post.