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Tandem Mass Spectrometry

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Metabolomics-Based Machine Learning for Predicting Mortality: Unveiling Multisystem Impacts on Health.

International journal of molecular sciences
Reliable predictors of long-term all-cause mortality are needed for middle-aged and older populations. Previous metabolomics mortality studies have limitations: a low number of participants and metabolites measured, measurements mainly using nuclear ...

Effect-directed analysis of genotoxicants in food packaging based on HPTLC fractionation, bioassays, and toxicity prediction with machine learning.

Analytical and bioanalytical chemistry
Many chemicals in food packaging can leach as complex mixtures to food, potentially including substances hazardous to consumer health. Detecting and identifying all of the leachable chemicals are impractical with current analytical instrumentation an...

Geographical classification of population: Analysis of amino acid in fingermark residues using UHPLC-QQQ-MS/MS combined with machine learning.

Forensic science international
OBJECTIVE: To determine the living regions of individuals based on amino acids in fingermark residues and to establish a rapid and accurate regional classification method using machine learning.

Mass Spectrometry Probe Combined with Machine Learning to Capture the Relationship between Metabolites and Mitochondrial Complex Activity at the Whole-Cell Level.

Analytical chemistry
Mitochondrial complex activity controls a multitude of physiological processes by regulating the cellular metabolism. Current methods for evaluating mitochondrial complex activity mainly focus on single metabolic reactions within mitochondria. These ...

NovoRank: Refinement for Peptide Sequencing Based on Spectral Clustering and Deep Learning.

Journal of proteome research
peptide sequencing is a valuable technique in mass-spectrometry-based proteomics, as it deduces peptide sequences directly from tandem mass spectra without relying on sequence databases. This database-independent method, however, relies solely on im...

Machine Learning for Predicting Zearalenone Contamination Levels in Pet Food.

Toxins
Zearalenone (ZEN) has been detected in both pet food ingredients and final products, causing acute toxicity and chronic health problems in pets. Therefore, the early detection of mycotoxin contamination in pet food is crucial for ensuring the safety ...

LipoCLEAN: A Machine Learning Filter to Improve Untargeted Lipid Identification Confidence.

Analytical chemistry
In untargeted lipidomics experiments, putative lipid identifications generated by automated analysis software require substantial manual filtering to arrive at usable high-confidence data. However, identification software tools do not make full use o...

Advancing Anticancer Drug Discovery: Leveraging Metabolomics and Machine Learning for Mode of Action Prediction by Pattern Recognition.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
A bottleneck in the development of new anti-cancer drugs is the recognition of their mode of action (MoA). Metabolomics combined with machine learning allowed to predict MoAs of novel anti-proliferative drug candidates, focusing on human prostate can...

MAGPIE: A Machine Learning Approach to Decipher Protein-Protein Interactions in Human Plasma.

Journal of proteome research
Immunoprecipitation coupled to tandem mass spectrometry (IP-MS/MS) methods are often used to identify protein-protein interactions (PPIs). While these approaches are prone to false positive identifications through contamination and antibody nonspecif...

π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing.

Nature communications
Peptide sequencing via tandem mass spectrometry (MS/MS) is essential in proteomics. Unlike traditional database searches, deep learning excels at de novo peptide sequencing, even for peptides missing from existing databases. Current deep learning mod...