The metabolome of an organism depends on environmental factors and intracellular
regulation and provides information about the physiological conditions. Metabolomics helps …

Metabolomics research has recently gained popularity because it enables the study of
biological traits at the biochemical level and, as a result, can directly reveal what occurs in a …

Introduction Metabolomics is increasingly being used in the clinical setting for disease
diagnosis, prognosis and risk prediction. Machine learning algorithms are particularly …

As the global population becomes older, understanding the impact of aging on health and
disease becomes paramount. Recent advancements in multiomic technology have allowed …

Metabolomics is the science of studying small molecules (metabolites) in biological systems
with the aim of getting insight into cells, biofluids and organisms. Chemometric methods are …

In the past few years, deep learning has been successfully applied to various omics data.
However, the applications of deep learning in metabolomics are still relatively low compared …

Deep learning (DL), an emerging area of investigation in the fields of machine learning and
artificial intelligence, has markedly advanced over the past years. DL techniques are being …

The 'inverse problem'of mass spectrometric molecular identification ('given a mass spectrum,
calculate/predict the 2D structure of the molecule whence it came') is largely unsolved, and …

Abstract Machine learning (ML) methods have been present in the field of NMR since
decades, but it has experienced a tremendous growth in the last few years, especially …

Metabolomic studies generate large and exceptionally complex datasets. The chemical
diversity that exists within the metabolome presents an immense analytical challenge …