Deep learning (DL), an emerging area of investigation in the fields of machine learning and artificial intelligence, has markedly advanced over the past years. DL techniques are being applied to assist medical professionals and researchers in improving clinical diagnosis, disease prediction and drug discovery. It is expected that DL will help to provide actionable knowledge from a variety of ‘big data’, including metabolomics data. In this review, we discuss the applicability of DL to metabolomics, while presenting and discussing several examples from recent research. We emphasize the use of DL in tackling bottlenecks in metabolomics data acquisition, processing, metabolite identification, as well as in metabolic phenotyping and biomarker discovery. Finally, we discuss how DL is used in genome-scale metabolic modelling and in interpretation of metabolomics data. The DL-based approaches discussed here may assist computational biologists with the integration, prediction and drawing of statistical inference about biological outcomes, based on metabolomics data.

中文翻译：

---

深度学习遇上代谢组学：方法论视角

深度学习 (DL) 是机器学习和人工智能领域的一个新兴研究领域，在过去几年中取得了显着进展。DL 技术正被用于协助医疗专业人员和研究人员改进临床诊断、疾病预测和药物发现。预计深度学习将有助于从包括代谢组学数据在内的各种“大数据”中提供可操作的知识。在这篇综述中，我们讨论了 DL 对代谢组学的适用性，同时展示和讨论了最近研究中的几个例子。我们强调使用 DL 解决代谢组学数据采集、处理、代谢物鉴定以及代谢表型和生物标志物发现中的瓶颈。最后，我们讨论了 DL 如何用于基因组规模的代谢建模和代谢组学数据的解释。此处讨论的基于 DL 的方法可以帮助计算生物学家基于代谢组学数据对生物结果的统计推断进行整合、预测和绘制。