AIMC Topic: Metabolomics
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Integrating Metabolomics Domain Knowledge with Explainable Machine Learning in Atherosclerotic Cardiovascular Disease Classification.
International journal of molecular sciences
Nov 30, 2024
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...
Promoting LC-QToF based non-targeted fingerprinting and biomarker selection with machine learning for the discrimination of black tea geographical origin.
Food chemistry
Nov 23, 2024
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
Nov 22, 2024
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...
Interpretable Machine Learning Algorithms Identify Inetetamab-Mediated Metabolic Signatures and Biomarkers in Treating Breast Cancer.
Journal of clinical laboratory analysis
Nov 21, 2024
BACKGROUND: HER2-positive breast cancer (BC), a highly aggressive malignancy, has been treated with the targeted therapy inetetamab for metastatic cases. Inetetamab (Cipterbin) is a recently approved targeted therapy for HER2-positive metastatic BC, ...
Accurate non-invasive detection of MASH with fibrosis F2-F3 using a lightweight machine learning model with minimal clinical and metabolomic variables.
Metabolism: clinical and experimental
Nov 19, 2024
BACKGROUND: There are no known non-invasive tests (NITs) designed for accurately detecting metabolic dysfunction-associated steatohepatitis (MASH) with liver fibrosis stages F2-F3, excluding cirrhosis-the FDA-defined range for prescribing Resmetirom ...
Advantages of Metabolomics-Based Multivariate Machine Learning to Predict Disease Severity: Example of COVID.
International journal of molecular sciences
Nov 13, 2024
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...
Enhancing type 2 diabetes mellitus prediction by integrating metabolomics and tree-based boosting approaches.
Frontiers in endocrinology
Nov 11, 2024
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 ...
Identification of novel hypertension biomarkers using explainable AI and metabolomics.
Metabolomics : Official journal of the Metabolomic Society
Nov 3, 2024
BACKGROUND: The global incidence of hypertension, a condition of elevated blood pressure, is rising alarmingly. According to the World Health Organization's Qatar Hypertension Profile for 2023, around 33% of adults are affected by hypertension. This ...
Metabolomics-Based Machine Learning for Predicting Mortality: Unveiling Multisystem Impacts on Health.
International journal of molecular sciences
Oct 30, 2024
Reliable predictors of long-term all-cause mortality are needed for middle-aged and older populations. Previous metabolomics mortality studies have limitations: a low number of participants and metabolites measured, measurements mainly using nuclear ...
Advancing Anticancer Drug Discovery: Leveraging Metabolomics and Machine Learning for Mode of Action Prediction by Pattern Recognition.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Oct 21, 2024
A bottleneck in the development of new anti-cancer drugs is the recognition of their mode of action (MoA). Metabolomics combined with machine learning allowed to predict MoAs of novel anti-proliferative drug candidates, focusing on human prostate can...
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