Emerging Computational Methods in Mass Spectrometry Imaging. Issue 34 (17th October 2022)

• Record Type: Journal Article
• Authors: Hu, Hang; Laskin, Julia
• Is Part Of: Advanced science. Volume 9:Issue 34(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-10-17
• Subjects: artificial intelligence -- computational methods -- data‐driven experiments -- data mining -- mass spectrometry imaging; Science -- Periodicals; 505
• DOI: 10.1002/advs.202203339
• Languages: English
• ISSNs: 2198-3844

Abstract

Abstract: Mass spectrometry imaging (MSI) is a powerful analytical technique that generates maps of hundreds of molecules in biological samples with high sensitivity and molecular specificity. Advanced MSI platforms with capability of high‐spatial resolution and high‐throughput acquisition generate vast amount of data, which necessitates the development of computational tools for MSI data analysis. In addition, computation‐driven MSI experiments have recently emerged as enabling technologies for further improving the MSI capabilities with little or no hardware modification. This review provides a critical summary of computational methods and resources developed for MSI data analysis and interpretation along with computational approaches for improving throughput and molecular coverage in MSI experiments. This review is focused on the recently developed artificial intelligence methods and provides an outlook for a future paradigm shift in MSI with transformative computational methods. Abstract : Computational methods play a key role in mass spectrometry imaging (MSI) by enabling both efficient data analysis and data driven MSI experiments. A concise overview of emerging artificial intelligence methods in MSI provided herein describes challenges associated with the development of these approaches and provides an outlook for a future paradigm shift in MSI enabled by transformative computational methods.