The innovation of biotechnologies has allowed the accumulation of omics data at an
alarming rate, thus introducing the era of 'big data'. Extracting inherent valuable knowledge …

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

Spectral similarity is used as a proxy for structural similarity in many tandem mass
spectrometry (MS/MS) based metabolomics analyses such as library matching and …

Mass spectrometry data is one of the key sources of information in many workflows in
medicine and across the life sciences. Mass fragmentation spectra are generally considered …

Metabolic engineering encompasses several widely-used strategies, which currently hold a
high seat in the field of biotechnology when its potential is manifesting through a plethora of …

The predicted liquid chromatographic retention times (RTs) of small molecules are not
accurate enough for wide adoption in structural identification. In this study, we used the …

Spectrum matching is the most common method for compound identification in mass
spectrometry (MS). However, some challenges limit its efficiency, including the coverage of …

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 …

Introduction Metabolomics has become a crucial part of systems biology; however, data
analysis is still often undertaken in a reductionist way focusing on changes in individual …