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METASPACE-ML: Context-specific metabolite annotation for imaging mass spectrometry using machine learning

ABSTRACT: Imaging mass spectrometry is a powerful technology enabling spatial metabolomics, yet metabolites can be assigned only to a fraction of the data generated. METASPACE-ML is a machine learning-based approach addressing this challenge which incorporates new scores and computationally-efficient False Discovery Rate estimation. For training and evaluation, we use a comprehensive set of 1,710 datasets from 159 researchers from 47 labs encompassing both animal and plant-based datasets representing multiple spatial metabolomics contexts derived from the METASPACE knowledge base. Here we show that, METASPACE-ML outperforms its rule-based predecessor, exhibiting higher precision, increased throughput, and enhanced capability in identifying low-intensity and biologically-relevant metabolites.

ORGANISM(S): Homo sapiens (human) Sorghum bicolor Mus musculus (mouse) Populus trichocarpa (poplar tree)

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PROVIDER: S-BIAD1283 | biostudies-other |

REPOSITORIES: biostudies-other

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