AIMC Topic: Mass Spectrometry

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Tailored SONAR-MSI: Converting SONAR-MS Data into Pseudoimages for Deep-Learning-Based Natural Products Analysis.

Analytical chemistry
LC-MS has become an essential tool for the analysis of complex samples. However, conventional MS data processing often involves cumbersome workflows and is prone to loss of information, particularly in the context of chemically diverse natural produc...

Multimodal landscape of atherosclerotic plaques: A spatial omics approach with mass spectrometry imaging.

Analytica chimica acta
Atherosclerotic plaques are complex and heterogeneous structures, originating as fatty streaks in the vasculature and formed by the accumulation of lipids and foam cells. Over time, these lesions progress as inflammation, smooth muscle cell prolifera...

Rapid Screening and Prioritization of Culture Conditions for Natural Product Discovery using the Liquid Microjunction Surface Sampling Probe.

Journal of the American Society for Mass Spectrometry
The discovery of novel bioactive compounds remains a cornerstone of natural product (NP) chemistry. However, traditional NP discovery workflows are time- and resource-intensive, hindering sustainability and efficiency of multicondition screening proj...

Unsupervised machine learning for mass spectrometry imaging data analysis with isotope labeling.

The Analyst
Mass spectrometry imaging (MSI) has emerged as a powerful tool for spatial metabolomics, but untargeted data analysis has proven to be challenging. When combined with isotope labeling (MSI), MSI provides insights into metabolic dynamics with high sp...

MassVision: An Open-Source End-to-End Platform for AI-Driven Mass Spectrometry Imaging Analysis.

Analytical chemistry
Mass spectrometry imaging (MSI) combines spatial and spectral data to reveal detailed molecular compositions within biological samples. Despite their immense potential, MSI workflows are hindered by the complexity and high dimensionality of the data,...

Differentiating Gastric Cancers from Acid Peptic Diseases through Integrative Targeted Proteomics and Machine Learning Approaches.

Journal of proteome research
Gastric cancers (GCs) are often diagnosed in advanced stages owing to nonspecific early symptoms resembling Acid Peptic Diseases (APDs). Despite recent efforts, a simple, liquid biopsy-based multiprotein panel prediagnostic assay capable of different...

De-MSI: A Deep Learning-Based Data Denoising Method to Enhance Mass Spectrometry Imaging by Leveraging the Chemical Prior Knowledge.

Analytical chemistry
Mass spectrometry imaging (MSI) is a label-free technique that enables the visualization of the spatial distribution of thousands of ions within biosamples. Data denoising is the computational strategy aimed at enhancing the MSI data quality, providi...

Machine learning- and multilayer molecular network-assisted screening hunts fentanyl compounds.

Science advances
Fentanyl and its analogs are a global concern, making their accurate identification essential for public health. Here, we introduce Fentanyl-Hunter, a screening platform that uses a machine learning classifier and multilayer molecular network to sele...

Lipid metabolites as biomarkers and therapeutic targets in oral squamous cell carcinoma.

BMC oral health
This study explores the association of lipid metabolism disruption and Oral Squamous Cell Carcinoma (OSCC). We aim to identify specific lipid biomarkers and therapeutic targets for OSCC. We included 78 OSCC patients and 80 healthy controls, and appli...

IQUP identifies quantitatively unreliable spectra with machine learning for isobaric labeling-based proteomics.

Scientific reports
Mass spectrometry‑based proteomics using isobaric labeling technology has become popular for proteomic quantitation. Existing approaches rely on the mechanism of target-decoy search and false discovery rate control to examine whether a peptide-spectr...