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Machine Learning in Mass Spectrometric Analysis of DIA Data
Author(s) -
Xu Leon L.,
Young Adamo,
Zhou Audrina,
Röst Hannes L.
Publication year - 2020
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.201900352
Subject(s) - computer science , data acquisition , pipeline (software) , proteomics , scalability , pace , mass spectrometry , machine learning , data mining , artificial intelligence , data science , chemistry , chromatography , database , biochemistry , geodesy , gene , programming language , geography , operating system
Liquid Chromatography coupled to Tandem Mass Spectrometry (LC‐MS/MS) based methods are currently the top choice for high‐throughput, quantitative measurements of the proteome. While traditional proteomics LC‐MS/MS methods can suffer from issues such as low reproducibility and quantitative accuracy due to its stochastic nature, recent improvements in acquisition protocols have resulted in methods that can overcome these challenges. Data‐independent acquisition (DIA) is a novel mass spectrometric method that does so by using a deterministic acquisition strategy. These new approaches will allow researchers to apply MS on more complex samples, however, existing heuristic and expert‐knowledge based methods will struggle with keeping pace of the increasing complexity of the resulting data. Deep learning (DL) based methods have been shown to be more adept at handling large amounts of complex data than traditional methods in many other fields, such as computer vision and natural language processing. Proteomics is also entering a phase where the size and complexity of the data will require us to look towards scalable and data‐driven DL pipelines.