Untargeted metabolomic approaches hold a great promise as a diagnostic tool for inborn errors of metabolisms (IEMs) in the near future. However, the complexity of the involved data makes its application difficult and time consuming. Computational approaches, such as metabolic network simulations and machine learning, could significantly help to exploit metabolomic data to aid the diagnostic process. While the former suffers from limited predictive accuracy, the latter is normally able to generalize only to IEMs for which sufficient data are available. Here, we propose a hybrid approach that exploits the best of both worlds by building a mapping between simulated and real metabolic data through a novel method based on Siamese neural networks (SNN).

中文翻译：

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通过整合真实数据和模拟数据来确定代谢紊乱优先级的暹罗神经网络模型

无目标代谢组学方法有望在不久的将来作为诊断先天性代谢错误（IEM）的诊断工具。但是，所涉及数据的复杂性使其应用变得困难且耗时。诸如代谢网络模拟和机器学习之类的计算方法可以极大地帮助开发代谢组学数据，以帮助诊断过程。尽管前者的预测准确性有限，但后者通常只能将其推广到具有足够数据的IEM。在这里，我们提出一种混合方法，通过基于暹罗神经网络（SNN）的新方法在模拟和真实代谢数据之间建立映射，从而充分利用两全其美的优势。