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Start of funding 01.07.2021
Machine learning and multi-omics metabolic health around the clock
Prof. Dr. Henriette Uhlenhaut
Technische Universität München
School of Life Sciences - Metabolic Programming
Prof. Dr. Fabian Filipp
UC Merced
School of Life Science
Low carb or low fat? How can we maintain metabolic youth? Is a fried egg good or bad in the morning? Such questions are strikingly simple, yet science is struggling to deliver answers. Deep learning approached developed in the Filipp lab for machine learning, UC Merced, CA, USA, offers an unbiased data lens to decipher complex patterns such as the underlying control elements of circadian rhythms. Our daily nutritional cycle is the focus of the Uhlenhaut lab for Metabolic Programming, TUM, Munich. The strength of the lab is generating multi-omics data sets under specific nutritional triggers suitable as input for deep learning networks. Our collaboration aims to investigate the application potential of AI for genome-wide analyses of transcriptional control and motifs for applications in endocrinology, nutritional science, the internal clock and metabolic medicine. The cooperation will combine experimental expertise from the Uhlenhaut lab with the computer-aided AI analysis from the Filipp laboratory. The planned U.S.-German collaboration will shed light on how our daily hormone cycle regulates metabolic health.