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Metabolomics

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Integrating omics data and machine learning techniques for precision detection of oral squamous cell carcinoma: evaluating single biomarkers.

Frontiers in immunology
INTRODUCTION: Early detection of oral squamous cell carcinoma (OSCC) is critical for improving clinical outcomes. Precision diagnostics integrating metabolomics and machine learning offer promising non-invasive solutions for identifying tumor-derived...

Enhancing type 2 diabetes mellitus prediction by integrating metabolomics and tree-based boosting approaches.

Frontiers in endocrinology
BACKGROUND: Type 2 diabetes mellitus (T2DM) is a global health problem characterized by insulin resistance and hyperglycemia. Early detection and accurate prediction of T2DM is crucial for effective management and prevention. This study explores the ...

Integrating Metabolomics Domain Knowledge with Explainable Machine Learning in Atherosclerotic Cardiovascular Disease Classification.

International journal of molecular sciences
Metabolomic data often present challenges due to high dimensionality, collinearity, and variability in metabolite concentrations. Machine learning (ML) application in metabolomic analyses is enabling the extraction of meaningful information from comp...

Advantages of Metabolomics-Based Multivariate Machine Learning to Predict Disease Severity: Example of COVID.

International journal of molecular sciences
The COVID-19 outbreak caused saturations of hospitals, highlighting the importance of early patient triage to optimize resource prioritization. Herein, our objective was to test if high definition metabolomics, combined with ML, can improve prognosti...

Promoting LC-QToF based non-targeted fingerprinting and biomarker selection with machine learning for the discrimination of black tea geographical origin.

Food chemistry
Traceability and mislabelling of black tea for their geographical origin is known as a major fraud concern of the sector. Discrimination among various geographical indications (GIs) can be challenging due to the complexity of chemical fingerprints in...

The Application of Machine Learning in Doping Detection.

Journal of chemical information and modeling
Detecting doping agents in sports poses a significant challenge due to the continuous emergence of new prohibited substances and methods. Traditional detection methods primarily rely on targeted analysis, which is often labor-intensive and is suscept...

Uncertainty Quantification and Flagging of Unreliable Predictions in Predicting Mass Spectrometry-Related Properties of Small Molecules Using Machine Learning.

International journal of molecular sciences
Mass spectral identification (in particular, in metabolomics) can be refined by comparing the observed and predicted properties of molecules, such as chromatographic retention. Significant advancements have been made in predicting these values using ...

Metabolomics-Based Machine Learning Models Accurately Predict Breast Cancer Estrogen Receptor Status.

International journal of molecular sciences
Breast cancer is a global concern as a leading cause of death for women. Early and precise diagnosis can be vital in handling the disease efficiently. Breast cancer subtyping based on estrogen receptor (ER) status is crucial for determining prognosis...

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 based metabolomic and genetic profiles for predicting multiple brain phenotypes.

Journal of translational medicine
BACKGROUND: It is unclear regarding the association between metabolomic state/genetic risk score(GRS) and brain volumes and how much of variance of brain volumes is attributable to metabolomic state or GRS.