Supervised machine learning in the mass spectrometry laboratory: A tutorial.

ABSTRACT: As the demand for laboratory testing by mass spectrometry increases, so does the need for automated methods for data analysis. Clinical mass spectrometry (MS) data is particularly well-suited for machine learning (ML) methods, which deal nicely with structured and discrete data elements. The alignment of these two fields offers a promising synergy that can be used to optimize workflows, improve result quality, and enhance our understanding of high-dimensional datasets and their inherent relationship with disease. In recent years, there has been an increasing number of publications that examine the capabilities of ML-based software in the context of chromatography and MS. However, given the historically distant nature between the fields of clinical chemistry and computer science, there is a

SUBMITTER: Lee ES 

PROVIDER: S-EPMC8692990 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature