用3D姿态数据直接学习行为表型，无需人工特征设计
GEESE: Genotype-aware End-to-End Spatio-temporal Embedding for Behavioral Phenotyping
- 基于预训练时序模型，从动作序列中端到端学习行为表征
- 在3种自闭症相关基因模型上，分类与基因预测均优于传统方法
- 支持非编程人员通过自然语言操作，适合生物实验研究者
当前遗传动物模型的行为表型分析依赖费力的手工特征工程，制约了可重复性和可扩展性。我们提出GEESE，一种端到端深度学习框架，直接从3D姿态动态中学习行为表征，无需手工特征。利用预训练时序基础模型，将运动序列编码为支持行为分类与基因型预测的行为流形。在三种自闭症相关基因模型（CNTNAP2、CHD8、FMR1）上评估，该方法在两项任务中均超越手工特征基线，揭示学习到的表征能捕捉基因型特异性行为特征。框架具有跨基因背景泛化能力，全队列模型仅凭运动模式即可识别基因背景与基因型。我们进一步提供HONK工具，使无编程经验的研究人员可通过自然语言交互完成姿态数据的行为表型分析。
原文摘要 · Abstract (English)
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.
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