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Metabolomics

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High throughput recurrent pregnancy loss screening: urine metabolic fingerprints LDI-MS and machine learning.

The Analyst
Infertility is a significant challenge faced by many families worldwide, with recurrent pregnancy loss (RPL) being a prevalent cause of infertility among women. This condition causes immense emotional and physical distress for affected individuals an...

Amogel: a multi-omics classification framework using associative graph neural networks with prior knowledge for biomarker identification.

BMC bioinformatics
The advent of high-throughput sequencing technologies, such as DNA microarray and DNA sequencing, has enabled effective analysis of cancer subtypes and targeted treatment. Furthermore, numerous studies have highlighted the capability of graph neural ...

AI-driven framework to map the brain metabolome in three dimensions.

Nature metabolism
High-resolution spatial imaging is transforming our understanding of foundational biology. Spatial metabolomics is an emerging field that enables the dissection of the complex metabolic landscape and heterogeneity from a thin tissue section. Currentl...

Machine learning-derived diagnostic model of epithelial ovarian cancer based on gut microbiome signatures.

Journal of translational medicine
BACKGROUND: Prior studies have elucidated that alterations in gut microbiota are associated with a spectrum of tumors and metabolic disorders. However, the diagnostic value of gut microbiota in epithelial ovarian cancer remains insufficiently investi...

Metabolomics and machine learning approaches for diagnostic biomarkers screening in systemic light chain amyloidosis.

Annals of hematology
Delayed diagnosis of systemic light chain (AL) amyloidosis is common and associated with worse survival and early mortality. Current diagnosis still relies on invasive tissue biopsies, highlighting the need for sensitive, noninvasive biomarkers for e...

FIORA: Local neighborhood-based prediction of compound mass spectra from single fragmentation events.

Nature communications
Non-targeted metabolomics holds great promise for advancing precision medicine and biomarker discovery. However, identifying compounds from tandem mass spectra remains a challenging task due to the incomplete nature of spectral reference libraries. A...

Bootstrap inference and machine learning reveal core differential plasma metabolic connectome signatures in major depressive disorder.

Journal of affective disorders
BACKGROUND: Major depressive disorder (MDD) involves molecular alterations and pathway dysregulation. Metabolic interconnections are crucial for normal functioning, yet current analysis focuses on individual pathways or biomarkers, overlooking intric...

Pseudotargeted metabolomics profiles potential damage-associated molecular patterns as machine learning predictors for acute pancreatitis.

Journal of pharmaceutical and biomedical analysis
Acute pancreatitis (AP) is a common gastrointestinal disease characterized by pancreatic cell damage and inflammation. Given the early clinical diagnosis and management challenges, exploring novel analytical frameworks from new orientations for inter...

Improved prediction and risk stratification of major adverse cardiovascular events using an explainable machine learning approach combining plasma biomarkers and traditional risk factors.

Cardiovascular diabetology
BACKGROUND: Cardiovascular diseases (CVD) remain the leading cause of morbidity and mortality globally. Traditional risk models, primarily based on established risk factors, often lack the precision needed to accurately predict new-onset major advers...