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Standards Development
This technical report (TS) provides guidelines of machine learning application to mass spectrometry data including spectra and mass images. Machine learning includes numerical analysis methods using computers such as multivariate analysis, artificial neural network-based methods and combination of multiple learning methods, and is generally classified into three categories, unsupervised learning, supervised learning, and reinforcement learning. This TS gives explanations of basic terms regarding machine learning and shows suitable methods depending on the analysis purposes and necessary procedures to data preparation for machine learning. This TS does not strictly regulate learning methods for mass spectrometry data because new learning methods and modified methods are being developed.
There are so many types of machine learning methods that the selection of suitable methods for individual mass spectrometry data sets is generally difficult especially for beginners of machine learning. Therefore, guidelines that shows what learning methods are suitable for what data and analysis purposes and examples of machine learning applications are required. To catch up the latest machine learning trends, understanding issues by conventional methods is important. Therefore, this TS summarizes the history of machine learning application to SIMS and mass imaging data, the specific features of machine learning that cannot be achieved by conventional multivariate analysis, the features of unsupervised learning and supervised learning methods, and the possibility of reinforcement learning methods. Moreover, the preparation of a data format including data preprocessing and the annotation of samples is important to obtain appropriate results from the machine learning analysis. The purpose of this TS is to provide useful directions of machine learning application to mass spectra and images.
Justification
ISO TC201/SC6 approved the usefulness of it at 30th ISO/TC201/SC6 meeting (N 416, 2022-10-27), and it has been studied as VAMAS Projects TWA2 A26 and A31 to develop ISO
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