GEESE: Genotype-aware End-to-End Spatio-temporal Embedding for Behavioral Phenotyping
For researchers studying genetic animal models, GEESE provides a scalable and reproducible method for behavioral phenotyping without manual feature engineering.
GEESE is an end-to-end deep learning framework that learns behavioral representations from 3D pose dynamics, surpassing hand-crafted feature baselines in behavior classification and genotype prediction across three autism-associated genetic models.
Behavioral phenotyping of genetic animal models currently requires labor-intensive manual feature engineering that limits reproducibility and scalability. We present GEESE, an end-to-end deep learning framework that learns behavioral representations directly from 3D pose dynamics without hand-crafted features. Using a pretrained time series foundation model, we encode movement sequences into a behavioral manifold that supports both behavior classification and genotype prediction. Evaluated across three autism-associated genetic models (CNTNAP2, CHD8, FMR1), our deep learning approach surpasses hand-crafted feature baselines in both tasks, revealing that learned representations capture genotype-specific behavioral signatures. The framework generalizes across genetic backgrounds, and an all-cohort model identifies both genetic background and genotype from movement patterns alone. We further provide HONK, an interactive intelligent tool enabling researchers without programming expertise to perform behavioral phenotyping from pose data through natural language interaction.