Machine learning-based automated phenotyping of inflammatory nocifensive behavior in mice.

ABSTRACT: The discovery and development of new and potentially nonaddictive pain therapeutics requires rapid, yet clinically relevant assays of nociception in preclinical models. A reliable and scalable automated scoring system for nocifensive behavior of mice in the formalin assay would dramatically lower the time and labor costs associated with experiments and reduce experimental variability. Here, we present a method that exploits machine learning techniques for video recordings that consists of three components: key point detection, per frame feature extraction using these key points, and classification of behavior using the GentleBoost algorithm. This approach to automation is flexible as different model classifiers or key points can be used with only small losses in accuracy. The adopted syste

SUBMITTER: Wotton JM 

PROVIDER: S-EPMC7509709 | biostudies-literature | 2020 Jan-Dec

REPOSITORIES: biostudies-literature