Transcriptome-driven Health-status Transversal-predictors
from APS Publications Podcast Hosted by apspublicationspodcast
In this episode of the APS Publications Podcast, Dr. Ralph Rühl discusses his team’s new article in Physiological Genomics, “Transcriptome-driven Health-status Transversal-predictor Analysis for health, food, microbiome and disease markers for understanding of lifestyle diseases.” The article outlines the development of a novel artificial intelligence approach based on machine-learning to predict general health and food-intake parameters. This novel technique, which is based on PBMC transcriptomics from human blood, can predict a wide range of health-related markers. Todt T, van Bussel I, Afman L, Brennan L, Ivanova DG, Kiselova-Kaneva Y, Thomas EL, Rühl R. Transcriptome-driven Health-status Transversal-predictor Analysis for health, food, microbiome and disease markers for understanding of lifestyle diseases. Physiol Genomics. 2025 Nov 19. doi: 10.1152/physiolgenomics.00026.2025. PMID: 41259124.
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