Morten Arendt Rasmussen

This study compares agreement between Garmin and Ultrahuman wearables and their association with continuous glucose monitoring in one adult, finding close agreement on sleep duration but variable heart rate and glucose correlations.

This paper introduces coupled matrix factorizations to extract subject-specific temporal patterns from longitudinal data, showing improved insights in metabolomics and sensitization studies.

The paper introduces PARAFAC2 to model subject-specific temporal patterns in longitudinal microbiome data, outperforming CP in capturing individual variations and enabling replicable pattern discovery.

This study uses calorimetry and metabolomics to compare metabolic activity of Bacillus cereus and B. subtilis in different food matrices, revealing matrix-dependent metabolic strategies and cereulide production.

This study identifies metabolomic biomarkers of PAH exposure using 3D liver spheroids, finding distinct metabolic changes at 96 hours compared to 24 hours.

This study shows that gut microbiota influences liver pathology caused by Schistosoma mansoni infection in mice, but not ileum inflammation.

The paper introduces a method combining simulated and real metabolomics data using tensor factorization to improve pattern discovery, showing better performance in males for BMI-related phenotypes.

This study shows that gut environmental factors explain more variation in microbiome and metabolome than diet or personal traits in healthy adults.

This study uses multiway data analysis to characterize postprandial metabolic responses in humans, revealing BMI-related differences in metabolite groups and temporal profiles between males.

The paper uses metabolomics and machine learning to identify metabolites associated with early childhood hs-CRP levels, finding 52 metabolites with potential bioactivity linked to systemic inflammation.

This study evaluates CP models for analyzing postprandial metabolomics data through simulations, showing their effectiveness in revealing metabolic patterns and group differences.

The paper examines the stability of maternal vaginal microbiota during pregnancy and its impact on early infant colonization through analysis of 700 mother-child dyads.

The paper introduces a method for integrating heterogeneous biological data using a graphical LASSO framework and applies it to study asthma and eczema risk factors in children.