AIMC Topic: Lipidomics
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Improving lipid mapping in Genome Scale Metabolic Networks using ontologies.
Metabolomics : Official journal of the Metabolomic Society
Mar 25, 2020
INTRODUCTION: To interpret metabolomic and lipidomic profiles, it is necessary to identify the metabolic reactions that connect the measured molecules. This can be achieved by putting them in the context of genome-scale metabolic network reconstructi...
Lipidome-based rapid diagnosis with machine learning for detection of TGF-β signalling activated area in head and neck cancer.
British journal of cancer
Feb 5, 2020
BACKGROUND: Several pro-oncogenic signals, including transforming growth factor beta (TGF-β) signalling from tumour microenvironment, generate intratumoural phenotypic heterogeneity and result in tumour progression and treatment failure. However, the...
Machine learning of human plasma lipidomes for obesity estimation in a large population cohort.
PLoS biology
Oct 18, 2019
Obesity is associated with changes in the plasma lipids. Although simple lipid quantification is routinely used, plasma lipids are rarely investigated at the level of individual molecules. We aimed at predicting different measures of obesity based on...
The Future of a Myriad of Accelerated Biodiscoveries Lies in AI-Powered Mass Spectrometry and Multiomics Integration.
Journal of mass spectrometry : JMS
Aug 1, 2025
The intersection of modern artificial intelligence (AI) and mass spectrometry (MS) is set to transform the MS-based "omics" research fields, particularly proteomics, metabolomics, lipidomics, and glycomics, enabling advancements across a wide range o...
Developing a Two-Dimensional Size-Exclusion Liquid Chromatography Platform for Isolating the High-Quality Extracellular Vesicles to Predict and Evaluate Metabolic Improvement Outcomes of Sleeve Gastrectomy.
Analytical chemistry
Jul 15, 2025
The direct analysis of plasma exosomal components still presents significant challenges due to the interference of high concentrations of albumin, immunoglobulins, and other abundant proteins. Herein, we developed a two-dimensional size-exclusion liq...
Integration of Metabolomics, Lipidomics, and Machine Learning for Developing a Biomarker Panel to Distinguish the Severity of Metabolic-Associated Fatty Liver Disease.
Biomedical chromatography : BMC
Jul 1, 2025
Metabolic-associated fatty liver disease (MAFLD), a global health challenge linked to metabolic syndrome, requires accurate severity stratification for clinical management. Current invasive diagnostic methods limit practical implementation. This stud...
Predicting Placenta Accreta Spectrum Disorder Through Machine Learning Using Metabolomic and Lipidomic Profiling and Clinical Characteristics.
Obstetrics and gynecology
Jun 1, 2025
OBJECTIVE: To perform metabolomic and lipidomic profiling with plasma samples from patients with placenta accreta spectrum (PAS) to identify possible biomarkers for PAS and to predict PAS with machine learning methods that incorporated clinical chara...
Multi-Omics Integration With Machine Learning Identified Early Diabetic Retinopathy, Diabetic Macula Edema and Anti-VEGF Treatment Response.
Translational vision science & technology
Dec 2, 2024
PURPOSE: Identify optimal metabolic features and pathways across diabetic retinopathy (DR) stages, develop risk models to differentiate diabetic macular edema (DME), and predict anti-vascular endothelial growth factor (anti-VEGF) therapy response.
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 ...
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