AIMC Topic: Tandem Mass Spectrometry
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Convolutional Neural Networks Assisted Peak Classification in Targeted LC-HRMS/MS for Equine Doping Control Screening Analyses.
Analytical chemistry
Feb 3, 2025
Doping control screening analyses usually involve visual inspection of extracted ion chromatograms (EIC) by a trained analytical chemist, followed by further investigations if needed. This task is both highly repetitive and time-consuming, given the ...
PeptideForest: Semisupervised Machine Learning Integrating Multiple Search Engines for Peptide Identification.
Journal of proteome research
Jan 22, 2025
The first step in bottom-up proteomics is the assignment of measured fragmentation mass spectra to peptide sequences, also known as peptide spectrum matches. In recent years novel algorithms have pushed the assignment to new heights; unfortunately, d...
iDIA-QC: AI-empowered data-independent acquisition mass spectrometry-based quality control.
Nature communications
Jan 21, 2025
Quality control (QC) in mass spectrometry (MS)-based proteomics is mainly based on data-dependent acquisition (DDA) analysis of standard samples. Here, we collect 2754 files acquired by data independent acquisition (DIA) and paired 2638 DDA files fro...
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 ...
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