GEESE: Genotype-aware End-to-End Spatio-temporal Embedding for Behavioral Phenotyping

arXiv:2605.24370v1 Announce Type: new Abstract: 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, C

Source: arXiv cs.LG — read the full report at the original publisher.

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