A recent paper published in BioRxiv presents a generative AI engine that generates personalized nutraceutical regimes in a novel way.
A recent paper published in BioRxiv presents a generative AI engine that generates personalized nutraceutical regimes in a novel way. The paper, titled "Utilizing Pre-trained Network Medicine Models for Generating Biomarkers, Targets, Re-purposing Drugs, and Personalized Therapeutic Regimes: COVID-19 Applications," introduces a generative AI engine called SEMO. SEMO is a pre-trained network medicine model developed by scientists from DeepoMe, a startup focused on generative AI for longevity. It uses protein-protein interaction networks and chemical data to predict COVID-19 severity.
The SEMO model identified several nutraceuticals and associated protein networks that could effectively predict severity. These insights can be used to generate personalized nutraceutical regimes, simultaneously generating biomarkers, targets, re-purposing drugs, and nutraceutical interventions tailored to an individual's specific needs. This is a significant breakthrough in personalized medicine, with the potential to revolutionize the healthcare industry.
Nutritional factors have a significant impact on the immune system, making it challenging to study the effects of multiple nutrients simultaneously. However, the paper proposes a method for quickly generating hypotheses on the effects of multiple nutrients on immunity. This method could accelerate research on the potential benefits of nutraceuticals in managing COVID-19 symptoms.
DeepoMe's scientists are at the forefront of developing innovative technologies like SEMO that revolutionize personalized medicine. To learn more about their perspective on the future of generative AI tech in precision nutrient, contact info@DeepoMe.com.