Application of Machine Learning in Spatial Proteomics,Journal of Chemical Information and Modeling

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Application of Machine Learning in Spatial Proteomics
Journal of Chemical Information and Modeling ( IF 5.6 ) Pub Date : 2022-11-15 , DOI: 10.1021/acs.jcim.2c01161
Minjie Mou ₁ , Ziqi Pan ₁ , Mingkun Lu ₁ , Huaicheng Sun ₁ , Yunxia Wang ₁ , Yongchao Luo ₁ , Feng Zhu ₁

Affiliation

Spatial proteomics is an interdisciplinary field that investigates the localization and dynamics of proteins, and it has gained extensive attention in recent years, especially the subcellular proteomics. Numerous evidence indicate that the subcellular localization of proteins is associated with various cellular processes and disease progression. Mass spectrometry (MS)-based and imaging-based experimental approaches have been developed to acquire large-scale spatial proteomic data. To allow the reliable analysis of increasingly complex spatial proteomics data, machine learning (ML) methods have been widely used in both MS-based and imaging-based spatial proteomic data analysis pipelines. Here, we comprehensively survey the applications of ML in spatial proteomics from following aspects: (1) data resources for spatial proteome are comprehensively introduced; (2) the roles of different ML algorithms in data analysis pipelines are elaborated; (3) successful applications of spatial proteomics and several analytical tools integrating ML methods are presented; (4) challenges existing in modern ML-based spatial proteomics studies are discussed. This review provides guidelines for researchers seeking to apply ML methods to analyze spatial proteomic data and can facilitate insightful understanding of cell biology as well as the future research in medical and drug discovery communities.

中文翻译：

机器学习在空间蛋白质组学中的应用

空间蛋白质组学是研究蛋白质定位和动力学的交叉学科，近年来受到广泛关注，尤其是亚细胞蛋白质组学。大量证据表明蛋白质的亚细胞定位与各种细胞过程和疾病进展有关。已经开发了基于质谱 (MS) 和基于成像的实验方法来获取大规模空间蛋白质组学数据。为了能够对日益复杂的空间蛋白质组学数据进行可靠的分析，机器学习 (ML) 方法已广泛用于基于 MS 和基于成像的空间蛋白质组学数据分析管道。在这里，我们从以下几个方面全面概述了ML在空间蛋白质组学中的应用：(1) 空间蛋白质组数据资源全面介绍；(2) 阐述了不同ML算法在数据分析流水线中的作用；(3) 介绍了空间蛋白质组学的成功应用和几种集成机器学习方法的分析工具；(4) 讨论了现代基于 ML 的空间蛋白质组学研究中存在的挑战。这篇综述为寻求应用 ML 方法分析空间蛋白质组数据的研究人员提供了指南，并可以促进对细胞生物学以及医学和药物发现社区未来研究的深刻理解。(4) 讨论了现代基于 ML 的空间蛋白质组学研究中存在的挑战。这篇综述为寻求应用 ML 方法分析空间蛋白质组数据的研究人员提供了指南，并可以促进对细胞生物学以及医学和药物发现社区未来研究的深刻理解。(4) 讨论了现代基于 ML 的空间蛋白质组学研究中存在的挑战。这篇综述为寻求应用 ML 方法分析空间蛋白质组数据的研究人员提供了指南，并可以促进对细胞生物学以及医学和药物发现社区未来研究的深刻理解。

更新日期：2022-11-15

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