AIMC Topic: Tandem Mass Spectrometry
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MAGPIE: A Machine Learning Approach to Decipher Protein-Protein Interactions in Human Plasma.
Journal of proteome research
Jan 7, 2025
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...
Rapid detection of drug abuse via tear analysis using surface enhanced Raman spectroscopy and machine learning.
Scientific reports
Jan 7, 2025
With the growing global challenge of drug abuse, there is an urgent need for rapid, accurate, and cost-effective drug detection methods. This study introduces an innovative approach to drug abuse screening by quickly detecting ephedrine (EPH) in tear...
Enhancing lipid identification in LC-HRMS data through machine learning-based retention time prediction.
Journal of chromatography. A
Jan 3, 2025
The comprehensive identification of peaks in untargeted lipidomics using LC-MS/MS remains a significant challenge. Confidence in lipid annotation can be greatly improved by integrating a highly accurate machine learning-based retention time predictio...
π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing.
Nature communications
Jan 2, 2025
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...
NovoRank: Refinement for Peptide Sequencing Based on Spectral Clustering and Deep Learning.
Journal of proteome research
Dec 31, 2024
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...
LipoCLEAN: A Machine Learning Filter to Improve Untargeted Lipid Identification Confidence.
Analytical chemistry
Dec 23, 2024
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...
Machine Learning for Predicting Zearalenone Contamination Levels in Pet Food.
Toxins
Dec 23, 2024
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 ...
Effect-directed analysis of genotoxicants in food packaging based on HPTLC fractionation, bioassays, and toxicity prediction with machine learning.
Analytical and bioanalytical chemistry
Nov 23, 2024
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
Nov 8, 2024
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
Nov 1, 2024
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 ...
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