AIMC Topic: Metabolomics

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Revealing disease-associated pathways by network integration of untargeted metabolomics.

Nature methods
Uncovering the molecular context of dysregulated metabolites is crucial to understand pathogenic pathways. However, their system-level analysis has been limited owing to challenges in global metabolite identification. Most metabolite features detecte...

Drug target identification using network analysis: Taking active components in Sini decoction as an example.

Scientific reports
Identifying the molecular targets for the beneficial effects of active small-molecule compounds simultaneously is an important and currently unmet challenge. In this study, we firstly proposed network analysis by integrating data from network pharmac...

A Systematic Strategy for Screening and Application of Specific Biomarkers in Hepatotoxicity Using Metabolomics Combined With ROC Curves and SVMs.

Toxicological sciences : an official journal of the Society of Toxicology
Current studies that evaluate toxicity based on metabolomics have primarily focused on the screening of biomarkers while largely neglecting further verification and biomarker applications. For this reason, we used drug-induced hepatotoxicity as an ex...

Illuminating the dark matter in metabolomics.

Proceedings of the National Academy of Sciences of the United States of America

Searching molecular structure databases with tandem mass spectra using CSI:FingerID.

Proceedings of the National Academy of Sciences of the United States of America
Metabolites provide a direct functional signature of cellular state. Untargeted metabolomics experiments usually rely on tandem MS to identify the thousands of compounds in a biological sample. Today, the vast majority of metabolites remain unknown. ...

Prediction of Anti-inflammatory Plants and Discovery of Their Biomarkers by Machine Learning Algorithms and Metabolomic Studies.

Planta medica
Nonsteroidal anti-inflammatory drugs are the most used anti-inflammatory medicines in the world. Side effects still occur, however, and some inflammatory pathologies lack efficient treatment. Cyclooxygenase and lipoxygenase pathways are of utmost imp...

An Interpretable Machine Learning Model Based on Metabolomics for Predicting Plaque Burden in Cryptogenic Stroke.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology
Cryptogenic stroke represents 25%-40% of ischemic strokes, with many cases harboring unrecognized large artery atherosclerosis (LAA) requiring specific secondary prevention. In this multicenter pilot study, we developed a metabolomics-based machine l...

Machine learning-enabled non-targeted metabolomics reveals nutritional and metabolic responses of Brachypodium distachyon to drought and elevated CO2.

Journal of experimental botany
Rising atmospheric CO2 and intensified drought are reshaping nutrient dynamics in C3 plants, with implications for ecosystem function and food security. To investigate how these stressors jointly affect nutrient homeostasis, we examined Brachypodium ...

Unveiling the dark matter of the metabolome: A narrative review of bioinformatics tools for LC-HRMS-based compound annotation.

Talanta
Compound annotation, including the unveiling of dark matter in the metabolomics study represents a pivotal undertaking within the metabolomics field, serving as the linchpin for unraveling the identities and attributes of chemical entities. This narr...

Characterization of volatile flavour compounds and characteristic flavour precursors in poultry eggs based on multi-omics and machine learning.

Food chemistry
Although egg flavour influences consumer preference and satisfaction, the features governing the flavour profiles have been poorly studied. This study investigated the volatile compounds and lipid profiles of different poultry egg yolks (chicken, duc...