MLDAAPP: machine learning data acquisition for assessing population phenotypes.

ABSTRACT: Collecting phenotypic data from many individuals is critical to answering fundamental biological questions, particularly in genetics. Yet, whole organismal phenotypic data are still often collected manually; limiting the scale of data generation, precluding reproducible workflows, and creating potential bias. Computer vision can ameliorate these issues, but currently available packages only operate with specific inputs and have limited scalability. We present Machine Learning Data Acquisition for Assessing Population Phenotypes (MLDAAPP), a package of tools built on YOLOv8 for collecting phenotypic data from groups of individuals that is flexible for generating counts (e.g. Drosophila fecundity, census), movement (e.g. activity, aversive behavior), and many other potential phenotypes. MLDA

SUBMITTER: Gabidulin AR 

PROVIDER: S-EPMC12506647 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature