Background: Post-COVID Condition (PCC) affects a substantial proportion of individuals following SARS-CoV-2 infection, and the mechanisms driving symptom persistence remain an area of active research. Identifying risk factors associated with PCC development is important for targeted prevention strategies and clinical management. Machine learning (ML) models offer powerful tools for prediction...
Introduction
Metabolomics measures small molecules (called metabolites) in cells, tissues, biofluids, that represent intermediates and/or end-products of biochemical/cellular processes. As a results, metabolomics has shown to be useful for predicting disease risks or associated biomarkers. Given the large data complexity and size, the Machine learning (ML) approach represents an appropriate...
Objectives: To evaluate whether machine learning (ML) applied to comprehensive claims data without diagnostic codes can distinguish a high proportion of antibiotic treatment episodes as urinary tract infection (UTI) or non-UTI cases. Such approaches may be valuable for antimicrobial stewardship when diagnosis-linked datasets are unavailable.
Methods: Outpatient antibiotic prescription claims...
Genome-wide association studies (GWAS) often identify genomic regions containing hundreds or thousands of genetic variants with comparable statistical evidence. Extensive linkage disequilibrium (LD) and the sparsity of causal variants obscure association signals, hindering the identification of true causal variants underlying complex traits. Fine-mapping approaches are introduced to...
Metabolite discovery can provide insights into disease mechanisms and help to identify potential biomarkers that contribute to the development of new treatments. We present a self-supervised deep learning approach for metabolite discovery. Molecular intensity distributions obtained via MALDI-MSI (matrix-assisted laser desorption/ionization mass spectrometry imaging) are compared with...
