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Machine learning based prioritisation of genes associated with osteoarthritis joint damage in animals

Lookup NU author(s): Professor David YoungORCiD

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Abstract

© 2026Objectives: To systematically prioritise unstudied genes for their potential to modulate joint damage phenotypes in animal models of osteoarthritis (OA). Methods: We curated 567 known protein-coding OA-associated genes from the literature and integrated them with 1139 uniformly processed skeletal transcriptomic response datasets and a human protein-protein interaction network to prioritise unstudied candidate OA genes. Three machine learning models, XGBoost, SVM, and Random Forest, were trained using repeated 5-fold cross-validation and evaluated on an independent test set of the latest literature-reported associations. Model interpretability was assessed using SHAP values analysis. Results: The XGBoost model achieved the highest performance on the held-out test dataset, with an AUROC of 0.84 (95% CI: 0.81–0.87). As expected, key predictive features included transcriptomic responses from both animal models and human OA tissues, as well as protein-protein interaction network features. Notably, SHAP value analysis highlighted specific biological pathways including oxidative phosphorylation and complement factor pathways as influential in the model. The top-prioritised genes included regulators of the NF-κB pathway, such as IER3, SOCS3, and NFKBIA. Incorporation of OpenTargets druggability data highlighted putative clinically tractable genes for further investigation, including MMP2 and MAP3K8. Conclusions: Our trained machine learning model effectively prioritised newly reported OA-associated genes, demonstrating its potential as a systematic gene prioritisation tool. An accompanying SkeletalVis R package enables researchers to explore over 1000 transcriptomic responses and the trained model predictions for their own studies.

Publication metadata

Author(s): Soul J, Young DA

Publication type: Article

Publication status: Published

Journal: Osteoarthritis and Cartilage

Year: 2026

Pages: epub ahead of print

Online publication date: 14/01/2026

Acceptance date: 07/01/2026

ISSN (print): 1063-4584

ISSN (electronic): 1522-9653

Publisher: Elsevier

URL: https://doi.org/10.1016/j.joca.2026.01.002

DOI: 10.1016/j.joca.2026.01.002

PubMed id: 41544746

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