计算机科学
文艺复兴
人工智能
领域(数学)
人工神经网络
机器学习
深度学习
数据科学
数学
艺术
纯数学
艺术史
作者
Armen G. Beck,Matthew Muhoberac,Caitlin E. Randolph,Connor H. Beveridge,Prageeth R. Wijewardhane,Hilkka I. Kenttämaa,Gaurav Chopra
出处
期刊:ACS Measurement Au
[American Chemical Society]
日期:2024-02-21
卷期号:4 (3): 233-246
被引量:11
标识
DOI:10.1021/acsmeasuresciau.3c00060
摘要
Statistical analysis and modeling of mass spectrometry (MS) data have a long and rich history with several modern MS-based applications using statistical and chemometric methods. Recently, machine learning (ML) has experienced a renaissance due to advents in computational hardware and the development of new algorithms for artificial neural networks (ANN) and deep learning architectures. Moreover, recent successes of new ANN and deep learning architectures in several areas of science, engineering, and society have further strengthened the ML field. Importantly, modern ML methods and architectures have enabled new approaches for tasks related to MS that are now widely adopted in several popular MS-based subdisciplines, such as mass spectrometry imaging and proteomics. Herein, we aim to provide an introductory summary of the practical aspects of ML methodology relevant to MS. Additionally, we seek to provide an up-to-date review of the most recent developments in ML integration with MS-based techniques while also providing critical insights into the future direction of the field.
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