Presentation description

Traditional approaches to studying sickness behaviors often rely on subjective observational methods, which can overlook the subtle nuances of these complex responses. To address these limitations, cutting-edge machine learning algorithms capable of both supervised and unsupervised behavioral phenotyping, were employed to detect nuanced and/or complex behaviors in mice that may be specific to sickness.