AIMC Topic: Tandem Mass Spectrometry
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Machine Learning-Based Identification of Novel Exosome-Derived Metabolic Biomarkers for the Diagnosis of Systemic Lupus Erythematosus and Differentiation of Renal Involvement.
Current medical science
Feb 28, 2025
OBJECTIVE: This study aims to investigate the exosome-derived metabolomics profiles in systemic lupus erythematosus (SLE), identify differential metabolites, and analyze their potential as diagnostic markers for SLE and lupus nephritis (LN).
Machine Learning in Small-Molecule Mass Spectrometry.
Annual review of analytical chemistry (Palo Alto, Calif.)
Feb 27, 2025
Tandem mass spectrometry (MS/MS) is crucial for small-molecule analysis; however, traditional computational methods are limited by incomplete reference libraries and complex data processing. Machine learning (ML) is transforming small-molecule mass s...
IodoFinder: Machine Learning-Guided Recognition of Iodinated Chemicals in Nontargeted LC-MS/MS Analysis.
Environmental science & technology
Feb 27, 2025
Iodinated disinfection byproducts (I-DBPs) pose significant health concerns due to their high toxicity. Current approaches to recognize unknown I-DBPs in mass spectrometry (MS) analysis rely on negative ionization mode, in which the characteristic I ...
Intelligent characterization multi-components in Yangxinshi tablet by online comprehensive two-dimensional liquid chromatography-quadrupole time-of-flight mass spectrometry combined with deep learning-assisted mass defect filtering classification and multidimensional data annotation strategy.
Talanta
Feb 25, 2025
A comprehensive characterization strategy for the intelligent analysis of multiple chemical components in Yangxinshi Tablet (YXST) was established. The strategy developed the deep learning-assisted mass defect filtering intelligent classification, pr...
Serum Metabolomics Profiling Coupled with Machine Learning Identifies Potential Diagnostic and Prognostic Candidate Markers in Meningioma Using Raman Spectroscopy, ATR-FTIR, and LC-MS/MS.
Journal of proteome research
Feb 25, 2025
Meningioma, the most prevalent brain tumor, poses significant challenges due to its unclear transition from low-grade to aggressive forms, with limited knowledge about grade-specific markers. We have utilized vibrational spectroscopic techniques such...
Tandem Mass Spectrometry
Meningioma
Meningeal Neoplasms
Metabolomics
Chromatography, Liquid
Liquid Chromatography-Mass Spectrometry
Biomarkers, Tumor
Male
Middle Aged
Case-Control Studies
Spectrum Analysis, Raman
Spectroscopy, Fourier Transform Infrared
Female
Aged
Adult
Humans
Machine Learning
Prognosis
Machine Learning-Based Bioactivity Classification of Natural Products Using LC-MS/MS Metabolomics.
Journal of natural products
Feb 7, 2025
The rediscovery of known drug classes represents a major challenge in natural products drug discovery. Compound rediscovery inhibits the ability of researchers to explore novel natural products and wastes significant amounts of time and resources. Th...
An AI-assisted morphoproteomic approach is a supportive tool in esophagitis-related precision medicine.
EMBO molecular medicine
Feb 3, 2025
Esophagitis is a frequent, but at the molecular level poorly characterized condition with diverse underlying etiologies and treatments. Correct diagnosis can be challenging due to partially overlapping histological features. By proteomic profiling of...
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...
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