In multi-modal biomedical research, integrating high-dimensional genomic data with clinical baselines is essential for precision medicine. However, standard deep neural network approaches often entangle these modalities, obscuring the specific predictive impact of genetic features and leading to possibly suboptimal predictive performance. Motivated by the landmark METABRIC cohort primary breast tumors study, we propose the Stein-Encoder, a white-box supervised framework designed to isolate the genetic signal driving clinical outcomes conditional on nuisance covariates. By leveraging Stein's method and residualization techniques, our approach constructs an interpretable single index that summarizes relevant biological heterogeneity while flexibly incorporating clinical factors and can be used to improve downstream prediction. We establish theoretical guarantees for identification, consistency and efficiency improvement. Applied to the METABRIC cohort, the Stein-Encoder outperforms unsupervised benchmarks in predictive accuracy. Crucially, it achieves structural disentanglement by revealing response-specific biological mechanisms: we find that tumor size is driven primarily by mitotic networks, whereas prognostic indices rely on a distinct proliferation-versus-immune axis. This work contributes a unified, computationally efficient framework that bridges statistical rigor with the representational power of neural networks, enabling interpretable, task-specific and efficient compression of multi-modal health data for a wide range of precision medicine applications, beyond biomarker discovery.

翻译：在多模态生物医学研究中，将高维基因组数据与临床基线指标相结合对于精准医学至关重要。然而，标准深度神经网络方法往往会纠缠这些模态信息，掩盖遗传特征的特异性预测影响，并可能导致次优的预测性能。受标志性METABRIC队列原发性乳腺癌研究的启发，我们提出了Stein-Encoder，这是一种白盒监督框架，旨在隔离在协变量干扰条件下驱动临床结局的遗传信号。通过利用Stein方法与残差化技术，我们的方法构建了一个可解释的单一指标，在灵活整合临床因素的同时总结相关的生物学异质性，并可应用于改进下游预测任务。我们建立了识别性、一致性和效率提升的理论保证。应用于METABRIC队列时，Stein-Encoder在预测准确性上优于无监督基准方法。至关重要的是，它通过揭示响应特异性生物学机制实现了结构解耦：我们发现肿瘤大小主要由有丝分裂网络驱动，而预后指标则依赖于独特的增殖-免疫轴。本研究提供了一个统一且计算高效的框架，该框架在统计严谨性与神经网络的表征能力之间架起桥梁，能够为超越生物标志物发现之外更广泛的精准医学应用，实现对多模态健康数据的可解释、任务特异性和高效压缩。