AIMC Topic: Mass Spectrometry
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MSMCE: A novel representation module for classification of raw mass spectrometry data.
PloS one
Aug 6, 2025
Mass spectrometry (MS) analysis plays a crucial role in the biomedical field; however, the high dimensionality and complexity of MS data pose significant challenges for feature extraction and classification. Deep learning has become a dominant approa...
Advances in mass spectrometry of lipids for the investigation of Niemann-pick type C disease.
Lipids in health and disease
Jul 30, 2025
Niemann-Pick type C (NPC) disease is a devastating, fatal, neurodegenerative disease and a form of lysosomal storage disorder. It is caused by mutations in either NPC1 or NPC2 genes, leading to the accumulation of cholesterol and other lipids in the ...
Integrating Non-Targeted Mass Spectrometry and Machine Learning for the Classification of Organic and Conventionally Grown Agricultural Products: A Case Study on Tomatoes.
Journal of agricultural and food chemistry
Jul 24, 2025
Rising demand for organic agricultural products has made the verification of their authenticity a critical concern. Traditional classification approaches for mass spectrometry using full-scan high-resolution mass spectrometry data often emphasize fea...
Untangling the Postmortem Metabolome: A Machine Learning Approach for Accurate PMI Estimation.
Analytical chemistry
Jul 23, 2025
Accurate estimation of the postmortem interval (PMI) is crucial for medico-legal investigations, providing critical timelines for criminal cases. Current PMI methods, however, often lack precision, limiting their forensic utility. In this study, we d...
Efficient Compression of Mass Spectrometry Images via Contrastive Learning-Based Encoding.
Analytical chemistry
Jul 21, 2025
In this study, we introduce a novel encoding algorithm utilizing contrastive learning to address the substantial data size challenges inherent in mass spectrometry imaging. Our algorithm compresses MSI data into fixed-length vectors, significantly re...
Biological Function Assignment across Taxonomic Levels in Mass-Spectrometry-Based Metaproteomics via a Modified Expectation Maximization Algorithm.
Journal of proteome research
Jul 18, 2025
A major challenge in mass-spectrometry-based metaproteomics is accurately identifying and quantifying biological functions across the full taxonomic lineage of microorganisms. This issue stems from what we refer to as the "shared confidently identifi...
Evaluation of the False Discovery Rate in Library-Free Search by DIA-NN Using Human Proteome.
Journal of proteome research
Jul 18, 2025
Recently, deep-learning-based spectral libraries have gained increasing attention. Several data-independent acquisition (DIA) software tools have integrated this feature, known as a library-free search, thereby making DIA analysis more accessible. H...
Machine Learning-Based Retention Time Prediction Tool for Routine LC-MS Data Analysis.
Journal of chemical information and modeling
Jul 16, 2025
Accurate retention time () prediction models can significantly improve liquid chromatography-mass spectrometry (LC-MS) data analysis widely used in chemical synthesis. As hundreds of thousands of syntheses are performed annually at Enamine, a large a...
Machine Learning-Assisted Iterative Screening for Efficient Detection of Drug Discovery Starting Points.
Journal of medicinal chemistry
Jul 14, 2025
High-throughput screening (HTS) remains central to small molecule lead discovery, but increasing assay complexity challenges the screening of large compound libraries. While retrospective studies have assessed active-learning-guided screening, extens...
Measurement and prediction of small molecule retention by Gram-negative bacteria based on a large-scale LC/MS screen.
Scientific reports
Jul 14, 2025
The challenge of assessing intracellular accumulation represents a major hurdle to the discovery of new antibiotics with Gram-negative activity. To address this, a high-throughput assay was developed to measure compound uptake and retention in Escher...
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