High-throughput behavioral screening in Caenorhabditis elegans using machine learning for drug repurposing.

ABSTRACT: Caenorhabditis elegans is a widely used animal model for researching new disease treatments. In recent years, automated methods have been developed to extract mobility phenotypes and analyse, using statistical methods, whether there are differences between control strains and disease model strains. However, these methods present certain limitations in detecting subtle and non-linear patterns. In this study, we propose a high-throughput screening method based on machine learning, using classifiers that provide a recovery percentage as a measure of treatment effect. We evaluate two main approaches: traditional machine learning models based on behavioral features extracted from the worm's skeleton using Tierpsy Tracker, and deep neural networks that directly analyse video sequences. The resul

SUBMITTER: Garcia-Garvi A 

PROVIDER: S-EPMC12274433 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature