AIMC Topic: Metabolomics
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Serum Metabolomics Profiling Coupled with Machine Learning Identifies Potential Diagnostic and Prognostic Candidate Markers in Meningioma Using Raman Spectroscopy, ATR-FTIR, and LC-MS/MS.
Journal of proteome research
Feb 25, 2025
Meningioma, the most prevalent brain tumor, poses significant challenges due to its unclear transition from low-grade to aggressive forms, with limited knowledge about grade-specific markers. We have utilized vibrational spectroscopic techniques such...
Meningeal Neoplasms
Liquid Chromatography-Mass Spectrometry
Tandem Mass Spectrometry
Meningioma
Aged
Adult
Biomarkers, Tumor
Machine Learning
Humans
Prognosis
Female
Spectrum Analysis, Raman
Spectroscopy, Fourier Transform Infrared
Male
Middle Aged
Case-Control Studies
Metabolomics
Chromatography, Liquid
Toward an integrated omics approach for plant biosynthetic pathway discovery in the age of AI.
Trends in biochemical sciences
Feb 24, 2025
Elucidating plant biosynthetic pathways is key to advancing a sustainable bioeconomy by enabling access to complex natural products through synthetic biology. Despite progress from genomic, transcriptomic, and metabolomic approaches, much multiomics ...
Identification of metabolite-disease associations based on knowledge graph.
Metabolomics : Official journal of the Metabolomic Society
Feb 22, 2025
BACKGROUND: Despite the insights that metabolite analysis can provide into the onset, development, and progression of diseases-thus offering new concepts and methodologies for prevention, diagnosis, and treatment-traditional wet lab experiments are o...
Deep learning-based clustering for endotyping and post-arthroplasty response classification using knee osteoarthritis multiomic data.
Annals of the rheumatic diseases
Feb 12, 2025
OBJECTIVES: Primary knee osteoarthritis (KOA) is a heterogeneous disease with clinical and molecular contributors. Biofluids contain microRNAs and metabolites that can be measured by omic technologies. Multimodal deep learning is adept at uncovering ...
A metabolic fingerprint of ovarian cancer: a novel diagnostic strategy employing plasma EV-based metabolomics and machine learning algorithms.
Journal of ovarian research
Feb 12, 2025
Ovarian cancer (OC) is the third most common malignant tumor of women and is accompanied by an alteration of systemic metabolism. A liquid biopsy that captures and detects tumor-related biomarkers in body fluids has great potential for OC diagnosis. ...
Rapid metabolic profiling and authentication of Cordyceps using ambient ionization mass spectrometry and machine learning.
Analytical and bioanalytical chemistry
Feb 11, 2025
Cordyceps sinensis, a symbiotic organism formed between a fungus and an insect, is celebrated for its substantial medicinal benefits and economic significance in traditional Chinese medicine. However, the market for Cordyceps sinensis is rife with co...
Machine Learning-Based Bioactivity Classification of Natural Products Using LC-MS/MS Metabolomics.
Journal of natural products
Feb 7, 2025
The rediscovery of known drug classes represents a major challenge in natural products drug discovery. Compound rediscovery inhibits the ability of researchers to explore novel natural products and wastes significant amounts of time and resources. Th...
Machine learning-based plasma metabolomics for improved cirrhosis risk stratification.
BMC gastroenterology
Feb 6, 2025
BACKGROUND: Cirrhosis is a leading cause of mortality in patients with chronic liver disease (CLD). The rapid development of metabolomic technologies has enabled the capture of metabolic changes related to the progression of cirrhosis.
A holistic strategy for the in-depth discrimination and authentication of 16 citrus herbs and associated commercial products based on machine learning techniques and non-targeted metabolomics.
Journal of chromatography. A
Feb 1, 2025
Citrus-derived raw medicinal materials are frequently used for health care, flavoring, and therapeutic purposes. However, Due to similarities in origin or appearance, citrus herbs are often misused in the market, necessitating effective differentiati...
Untargeted metabolomics and machine learning unveil the exposome and metabolism linked with the risk of early pregnancy loss.
Journal of hazardous materials
Jan 31, 2025
Early pregnancy loss (EPL) may result from exposure to emerging contaminants (ECs), although the underlying mechanisms remain poorly understood. This case-control study measured over 2000 serum features, including 37 ECs, 6 biochemicals, and 2057 end...
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