AIMC Topic: Metabolomics
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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...
Urinary Metabolic Biomarkers of Attentional Control in Children With Attention-Deficit/Hyperactivity Disorder: A Dimensional Approach Through H NMR-Based Metabolomics.
NMR in biomedicine
Aug 1, 2025
Enhancing the understanding of attention-deficit/hyperactivity disorder (ADHD) by linking biological processes with behavioral manifestations is a primary objective of the Research Domain Criteria (RDoC) framework, which aims to transcend traditional...
Predicting metabolite-disease associations based on dynamic adaptive feature learning architecture.
Computer methods and programs in biomedicine
Aug 1, 2025
BACKGROUND AND OBJECTIVE: In recent years, the association between metabolites and complex human diseases has increasingly been recognized as a major research focus. Traditional wet-lab experiments are considered time-consuming and labor-intensive, w...
Machine learning-guided Orbitrap-HRAMS-based metabolomic fingerprinting for geographical origin, variety and tissue specific authentication, and adulteration detection of turmeric and ashwagandha.
Food chemistry
Aug 1, 2025
The increasing global demand for herbs and spices in food and nutraceutical industries highlights their key functional benefits, including antioxidant and anti-inflammatory properties. Ensuring authenticity and traceability is essential to counteract...
MS2MP: A Deep Learning Framework for Metabolic Pathway Prediction from MS/MS-Based Untargeted Metabolomics.
Analytical chemistry
Jul 15, 2025
MS/MS-based untargeted metabolomics generates complex data, but pathway enrichment analysis is constrained by the low annotation rates of metabolic features. Here, we propose MS2MP, a novel deep learning-based framework for KEGG pathway prediction di...
Metabolic pathway alterations in cerebrospinal fluid as diagnostic biomarkers for primary central nervous system lymphoma.
Clinica chimica acta; international journal of clinical chemistry
Jul 15, 2025
Primary Central Nervous System Lymphoma (PCNSL) is a rare and aggressive type of hematological malignancy that can pose diagnostic challenges. Early detection is critical for effective treatment and better patient outcomes. The goal of this study was...
Metabolomic machine learning predictor for arsenic-associated hypertension risk in male workers.
Journal of pharmaceutical and biomedical analysis
Jul 15, 2025
Arsenic (As)-induced hypertension is a significant public health concern, highlighting the need for early risk prediction. This study aimed to develop a predictive model for occupational As exposure and hypertension using metabolomics and machine lea...
Assessing the Impact of Measurement Precision on Metabolite Identification Probability in Multidimensional Mass Spectrometry-Based, Reference-Free Metabolomics.
Analytical chemistry
Jul 8, 2025
Identification of compounds with minimal ambiguity remains a central challenge in mass spectrometry-based metabolomics. Conventional compound identification relies on comparing analytical signatures (e.g., mass-to-charge ratio, collision cross sectio...
A multi-omics machine learning classifier for outgrowth of cow's milk allergy in children.
Molecular omics
Jul 7, 2025
Cow's milk protein allergy (CMA) is one of the most common food allergies in children worldwide. However, it is still not well understood why certain children outgrow their CMA and others do not. While there is increasing evidence for a link of CMA w...
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...
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