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• W4315705072 abstract "Imaging mass spectrometry (IMS) is a powerful analytical technique widely used in biology, chemistry, and materials science fields that continue to expand. IMS provides a qualitative compositional analysis and spatial mapping with high chemical specificity. The spatial mapping information can be 2D or 3D depending on the analysis technique employed. Due to the combination of complex mass spectra coupled with spatial information, large high-dimensional datasets (hyperspectral) are often produced. Therefore, the use of automated computational methods for an exploratory analysis is highly beneficial. The fast-paced development of artificial intelligence (AI) and machine learning (ML) tools has received significant attention in recent years. These tools, in principle, can enable the unification of data collection and analysis into a single pipeline to make sampling and analysis decisions on the go. There are various ML approaches that have been applied to IMS data over the last decade. In this review, we discuss recent examples of the common unsupervised (principal component analysis, non-negative matrix factorization, k-means clustering, uniform manifold approximation and projection), supervised (random forest, logistic regression, XGboost, support vector machine), and other methods applied to various IMS datasets in the past five years. The information from this review will be useful for specialists from both IMS and ML fields since it summarizes current and representative studies of computational ML-based exploratory methods for IMS." @default.
• W4315705072 created "2023-01-12" @default.
• W4315705072 creator A5031852572 @default.
• W4315705072 creator A5039044523 @default.
• W4315705072 creator A5053312287 @default.
• W4315705072 creator A5054848814 @default.
• W4315705072 creator A5058362762 @default.
• W4315705072 creator A5059110757 @default.
• W4315705072 creator A5068304057 @default.
• W4315705072 creator A5069640022 @default.
• W4315705072 creator A5091579076 @default.
• W4315705072 date "2023-01-12" @default.
• W4315705072 modified "2023-09-26" @default.
• W4315705072 title "A review on recent machine learning applications for imaging mass spectrometry studies" @default.
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