深度学习在质谱成像数据分析中的应用研究进展OA北大核心CSTPCDMEDLINE

Mass spectrometry imaging(MSI)is a promising method for characterizing the spa-tial distribution of compounds.Given the diversified development of acquisition methods and continuous improvements in the sensitivity of this technology,both the total amount of genera-ted data and complexity of analysis have exponentially increased,rendering increasing challen-ges of data postprocessing,such as large amounts of noise,background signal interferences,as well as image registration deviations caused by sample position changes and scan deviations,and etc.Deep learning(DL)is a powerful tool widely used in data analysis and image recon-struction.This tool enables the automatic feature extraction of data by building and training a neural network model,and achieves comprehensive and in-depth analysis of target data through transfer learning,which has great potential for MSI data analysis.This paper reviews the cur-rent research status,application progress and challenges of DL in MSI data analysis,focusing on four core stages:data preprocessing,image reconstruction,cluster analysis,and multimo-dal fusion.The application of a combination of DL and mass spectrometry imaging in the study of tumor diagnosis and subtype classification is also illustrated.This review also discusses trends of development in the future,aiming to promote a better combination of artificial intelli-gence and mass spectrometry technology.