AIMC Topic: Metabolomics

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Single-cell omics: moving towards a new era in ischemic stroke research.

European journal of pharmacology
Ischemic stroke (IS) is a highly complex and heterogeneous disease involving multiple pathophysiological events. A better understanding of the pathophysiology of IS will enhance preventive, diagnostic and therapeutic strategies. Despite significant a...

The Future of a Myriad of Accelerated Biodiscoveries Lies in AI-Powered Mass Spectrometry and Multiomics Integration.

Journal of mass spectrometry : JMS
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
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
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...

MS2MP: A Deep Learning Framework for Metabolic Pathway Prediction from MS/MS-Based Untargeted Metabolomics.

Analytical chemistry
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
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
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
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
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...