Recent advances and prospects of computational methods for metabolite identification: a review with emphasis on machine learning approaches.
Recent advances and prospects of computational methods for metabolite identification: a review with emphasis on machine learning approaches.
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DOI：
10.1093/bib/bby066
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发表时间：
2019-11-27
影响因子：
9.5
通讯作者：
Mamitsuka H
中科院分区：
文献类型：
--
作者：
Nguyen DH;Nguyen CH;Mamitsuka H
动机：代谢组学涉及大量代谢物的研究，这些代谢物是生物系统中存在的小分子。它们发挥许多重要的功能，例如能量传输、信号传导、细胞构建块和抑制/催化。了解代谢物的生化特征是代谢组学的重要组成部分，可以扩大生物系统的知识。它也是生物技术、生物医学或制药等许多应用和领域发展的关键。然而，代谢物的鉴定仍然是代谢组学中的一项具有挑战性的任务，有大量潜在有趣但未知的代谢物。鉴定代谢物的标准方法是基于质谱 (MS)，然后采用分离技术。几十年来，针对基于质谱的代谢物识别任务，人们提出了许多不同方法的技术，这些技术可分为以下四组：质谱数据库、计算机碎片、碎片树和机器学习。在这篇综述论文中，我们全面调查了当前可用的代谢物识别工具，重点是计算机碎裂和基于机器学习的方法。我们还对先进的机器学习方法进行了深入的讨论，这可以导致这项任务的进一步改进。
Motivation: Metabolomics involves studies of a great number of metabolites, which are small molecules present in biological systems. They play a lot of important functions such as energy transport, signaling, building block of cells and inhibition/catalysis. Understanding biochemical characteristics of the metabolites is an essential and significant part of metabolomics to enlarge the knowledge of biological systems. It is also the key to the development of many applications and areas such as biotechnology, biomedicine or pharmaceuticals. However, the identification of the metabolites remains a challenging task in metabolomics with a huge number of potentially interesting but unknown metabolites. The standard method for identifying metabolites is based on the mass spectrometry (MS) preceded by a separation technique. Over many decades, many techniques with different approaches have been proposed for MS-based metabolite identification task, which can be divided into the following four groups: mass spectra database, in silico fragmentation, fragmentation tree and machine learning. In this review paper, we thoroughly survey currently available tools for metabolite identification with the focus on in silico fragmentation, and machine learning-based approaches. We also give an intensive discussion on advanced machine learning methods, which can lead to further improvement on this task.
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