AIMC Topic: Mass Spectrometry
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3D-MSNet: a point cloud-based deep learning model for untargeted feature detection and quantification in profile LC-HRMS data.
Bioinformatics (Oxford, England)
May 4, 2023
MOTIVATION: Liquid chromatography coupled with high-resolution mass spectrometry is widely used in composition profiling in untargeted metabolomics research. While retaining complete sample information, mass spectrometry (MS) data naturally have the ...
A noise-robust deep clustering of biomolecular ions improves interpretability of mass spectrometric images.
Bioinformatics (Oxford, England)
Feb 3, 2023
MOTIVATION: Mass Spectrometry Imaging (MSI) analyzes complex biological samples such as tissues. It simultaneously characterizes the ions present in the tissue in the form of mass spectra, and the spatial distribution of the ions across the tissue in...
Deep learning-based pseudo-mass spectrometry imaging analysis for precision medicine.
Briefings in bioinformatics
Sep 20, 2022
Liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics provides systematic profiling of metabolic. Yet, its applications in precision medicine (disease diagnosis) have been limited by several challenges, including metabolite id...
Unbiased spatial proteomics with single-cell resolution in tissues.
Molecular cell
Jun 16, 2022
Mass spectrometry (MS)-based proteomics has become a powerful technology to quantify the entire complement of proteins in cells or tissues. Here, we review challenges and recent advances in the LC-MS-based analysis of minute protein amounts, down to ...
Complementing machine learning-based structure predictions with native mass spectrometry.
Protein science : a publication of the Protein Society
Jun 1, 2022
The advent of machine learning-based structure prediction algorithms such as AlphaFold2 (AF2) and RoseTTa Fold have moved the generation of accurate structural models for the entire cellular protein machinery into the reach of the scientific communit...
Improving confidence in lipidomic annotations by incorporating empirical ion mobility regression analysis and chemical class prediction.
Bioinformatics (Oxford, England)
May 13, 2022
MOTIVATION: Mass spectrometry-based untargeted lipidomics aims to globally characterize the lipids and lipid-like molecules in biological systems. Ion mobility increases coverage and confidence by offering an additional dimension of separation and a ...
massNet: integrated processing and classification of spatially resolved mass spectrometry data using deep learning for rapid tumor delineation.
Bioinformatics (Oxford, England)
Mar 28, 2022
MOTIVATION: Mass spectrometry imaging (MSI) provides rich biochemical information in a label-free manner and therefore holds promise to substantially impact current practice in disease diagnosis. However, the complex nature of MSI data poses computat...
Metabolite discovery: Biochemistry's scientific driver.
Cell metabolism
Jan 4, 2022
Metabolite identification represents a major challenge, and opportunity, for biochemistry. The collective characterization and quantification of metabolites in living organisms, with its many successes, represents a major biochemical knowledgebase an...
Deep Learning-Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2022
Posttranslational modification (PTM ) is a ubiquitous phenomenon in both eukaryotes and prokaryotes which gives rise to enormous proteomic diversity. PTM mostly comes in two flavors: covalent modification to polypeptide chain and proteolytic cleavage...
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