AIMC Topic: Mass Spectrometry

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Mapping a interactome by crosslinking mass spectrometry and machine learning.

mBio
, a widespread human parasite, persists in hosts through complex molecular interactions. Protein-protein interactions (PPIs) underpin essential biological processes, including parasite-host interactions and cellular invasion. Herein, we utilized adva...

Increasing the Scale of the Mass Spectrometry Query Language Compendium with Explainable AI.

Analytical chemistry
A significant bottleneck in metabolomics data interpretation is the effective use of domain knowledge to assign structural information based on fragmentation patterns. The mass spectrometry query language (MassQL) aims to make this process accessible...

Serological proteomic characterization for monitoring liver fibrosis regression in chronic hepatitis B patients on treatment.

Nature communications
Longitudinal serological proteomic dynamics during antiviral therapy (AVT) in chronic hepatitis B (CHB) patients with liver fibrosis remain poorly characterized. Here, using four-dimensional data-independent acquisition mass spectrometry (4D-DIA-MS),...

MSMCE: A novel representation module for classification of raw mass spectrometry data.

PloS one
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...

Off-site processing of data-dependent and data-independent acquisition shotgun proteomics data with MASSyPupX.

Journal of proteomics
The rapid pace of shotgun proteomics data generation presents challenges for timely data analysis. In parallel, the scientific community is creating novel data interpretation tools, such as artificial intelligence, that have not yet been integrated i...

Advances in mass spectrometry of lipids for the investigation of Niemann-pick type C disease.

Lipids in health and disease
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 ...

Application of metabolomics and MCDM approach in developing a novel strategy for disease diagnosis: A case study in Primary Sjögren's Syndrome.

Journal of pharmaceutical and biomedical analysis
Primary Sjögren's Syndrome (pSS) is a complex autoimmune disease with an unclear etiology. Due to the lack of a single diagnostic gold standard, multidisciplinary and invasive examinations are often required for pSS, underscoring the urgent need for ...

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
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
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
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...