Integrating supervised and unsupervised machine learning for behavior segmentation reveals latent frailty signatures and improves aging clocks in isogenic and outbred mice
This study demonstrates that combining supervised expert-defined features with unsupervised behavioral signatures discovered by Keypoint-MoSeq significantly improves the prediction of age and frailty in mice, while revealing that these behavioral aging markers are highly strain-specific and do not generalize across different mouse populations.