AI Medical Compendium Topic

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Lipidomics

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LipoCLEAN: A Machine Learning Filter to Improve Untargeted Lipid Identification Confidence.

Analytical chemistry
In untargeted lipidomics experiments, putative lipid identifications generated by automated analysis software require substantial manual filtering to arrive at usable high-confidence data. However, identification software tools do not make full use o...

Multi-Omics Integration With Machine Learning Identified Early Diabetic Retinopathy, Diabetic Macula Edema and Anti-VEGF Treatment Response.

Translational vision science & technology
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.

Machine learning-driven Raman spectroscopy: A novel approach to lipid profiling in diabetic kidney disease.

Nanomedicine : nanotechnology, biology, and medicine
Diabetes mellitus is a chronic metabolic disease that increasingly affects people every year. It is known that with its progression and poor management, metabolic changes can lead to organ dysfunctions, including kidneys. The study aimed to combine R...

Enhancing lipid identification in LC-HRMS data through machine learning-based retention time prediction.

Journal of chromatography. A
The comprehensive identification of peaks in untargeted lipidomics using LC-MS/MS remains a significant challenge. Confidence in lipid annotation can be greatly improved by integrating a highly accurate machine learning-based retention time predictio...

Screening of serum biomarkers in patients with PCOS through lipid omics and ensemble machine learning.

PloS one
Polycystic ovary syndrome (PCOS) is a primary endocrine disorder affecting premenopausal women involving metabolic dysregulation. We aimed to screen serum biomarkers in PCOS patients using untargeted lipidomics and ensemble machine learning. Serum fr...

Untargeted Lipidomic Biomarkers for Liver Cancer Diagnosis: A Tree-Based Machine Learning Model Enhanced by Explainable Artificial Intelligence.

Medicina (Kaunas, Lithuania)
: Liver cancer ranks among the leading causes of cancer-related mortality, necessitating the development of novel diagnostic methods. Deregulated lipid metabolism, a hallmark of hepatocarcinogenesis, offers compelling prospects for biomarker identifi...

Toward Machine Learning Electrospray Ionization Sensitivity Prediction for Semiquantitative Lipidomics in Stem Cells.

Journal of chemical information and modeling
Specificity, sensitivity, and high metabolite coverage make mass spectrometry (MS) one of the most valuable tools in metabolomics and lipidomics. However, translation of metabolomics MS methods to multiyear studies conducted across multiple batches i...

Resolving multi-image spatial lipidomic responses to inhaled toxicants by machine learning.

Nature communications
Regional responses to inhaled toxicants are essential to understand the pathogenesis of lung disease under exposure to air pollution. We evaluate the effect of combined allergen sensitization and ozone exposure on eliciting spatial differences in lip...

Lipidomic analysis coupled with machine learning identifies unique urinary lipid signatures in patients with interstitial cystitis/bladder pain syndrome.

World journal of urology
PURPOSE: To identify biomarkers for diagnosis and classification of interstitial cystitis/bladder pain syndrome (IC/BPS) by urinary lipidomics coupled with machine learning.

Combining lipidomics and machine learning to identify lipid biomarkers for nonsyndromic cleft lip with palate.

JCI insight
Nonsyndromic cleft lip with palate (nsCLP) is a common birth defect disease. Current diagnostic methods comprise fetal ultrasound images, which are mainly limited by fetal position and technician skills. We aimed to identify reliable maternal serum l...