GEESE: Genotype-aware End-to-End Spatio-temporal Embedding for Behavioral Phenotyping
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866910250958848000 |
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| author | Ding, Yiran Gao, Yuen Qian, Chunqi Cui, Zijun |
| author_facet | Ding, Yiran Gao, Yuen Qian, Chunqi Cui, Zijun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_24370 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GEESE: Genotype-aware End-to-End Spatio-temporal Embedding for Behavioral Phenotyping Ding, Yiran Gao, Yuen Qian, Chunqi Cui, Zijun Machine Learning Quantitative Methods I.2.6; J.3; I.5.2 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. |
| title | GEESE: Genotype-aware End-to-End Spatio-temporal Embedding for Behavioral Phenotyping |
| topic | Machine Learning Quantitative Methods I.2.6; J.3; I.5.2 |
| url | https://arxiv.org/abs/2605.24370 |