用于MALDI-TOF质谱稳健跨中心泛化的生物学信息表征学习

中文摘要 AI 辅助

用于MALDI-TOF质谱的机器学习模型在微生物鉴定、抗菌药物耐药性预测等临床微生物学任务中展现出巨大潜力。然而，由于采集特定的变异性常导致模型捕获技术伪影而非可迁移的生物学信息，这类模型在不同医疗机构的部署仍受限于域偏移问题。现有的表征学习方法主要通过统计域对齐解决该问题，却很大程度上忽略了微生物学数据中天然存在的生物学监督信息。我们提出DALMA，这是一种概率表征学习框架，可联合建模采集特定变异性与生物学监督，以学习生物学结构化的潜在表征。通过结合域特定重构与生物学引导的表征学习，DALMA可学习跨异构临床中心泛化的可迁移表征，且推理阶段无需机构特定组件，支持在未见站点的零样本部署。我们在包含三个国家七个数据集的多中心基准上评估DALMA，其在两个保留的临床中心的零样本微生物鉴定中始终达到最优性能，且学习到的表征也能有效迁移至抗菌药物耐药性预测任务。此外，潜在空间新颖性估计可在未见域偏移下实现可靠的选择性预测。这些结果表明，生物学信息表征学习为临床微生物学中稳健且可迁移的机器学习提供了有效策略。

英文摘要

Machine learning models for MALDI-TOF mass spectrometry have shown considerable promise for clinical microbiology tasks such as microbial identification and antimicrobial resistance prediction. However, their deployment across institutions remains limited by domain shift, as acquisition-specific variability often leads models to capture technical artifacts rather than transferable biological information. Existing representation learning approaches primarily address this problem through statistical domain alignment while largely overlooking the biological supervision naturally available in microbiology datasets. We introduce DALMA, a probabilistic representation learning framework that jointly models acquisition-specific variability and biological supervision to learn biologically structured latent representations. By combining domain-specific reconstruction with biologically guided representation learning, DALMA learns transferable representations that generalize across heterogeneous clinical centers without requiring institution-specific components at inference, enabling zero-shot deployment on previously unseen sites. We evaluate DALMA on a multi-center benchmark comprising seven datasets from three countries. DALMA consistently achieves state-of-the-art zero-shot microbial identification across two held-out clinical centers, while the learned representations also transfer effectively to antimicrobial resistance prediction. Furthermore, latent-space novelty estimation enables reliable selective prediction under previously unseen domain shifts. These results demonstrate that biologically informed representation learning provides an effective strategy for robust and transferable ML in clinical microbiology.

发表机构

• Universidad Carlos III de Madrid（卡洛斯三世大学）
• Instituto de Investigación Sanitaria Gregorio Marañón（格雷戈里奥·马拉尼翁卫生研究所）
• Hospital General Universitario Gregorio Marañón（格雷戈里奥·马拉尼翁大学总医院）

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