首页|Findings on Machine Learning Detailed by Investigators at Swiss Federal Institut e of Technology Lausanne (EPFL) (Generative Machine Learning Produces Kinetic Mo dels That Accurately Characterize Intracellular Metabolic States)
Findings on Machine Learning Detailed by Investigators at Swiss Federal Institut e of Technology Lausanne (EPFL) (Generative Machine Learning Produces Kinetic Mo dels That Accurately Characterize Intracellular Metabolic States)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Machine Learning are pre sented in a new report. According to news reporting out of Lausanne, Switzerland , by NewsRx editors, research stated, “Generating large omics datasets has becom e routine for gaining insights into cellular processes, yet deciphering these da tasets to determine metabolic states remains challenging. Kinetic models can hel p integrate omics data by explicitly linking metabolite concentrations, metaboli c fluxes and enzyme levels.” Financial supporters for this research include Swiss National Science Foundation (SNSF), European Union (EU), Swedish Research Council, Ecole Polytechnique Fede rale de Lausanne (EPFL).
LausanneSwitzerlandEuropeCyborgsEmerging TechnologiesMachine LearningSwiss Federal Institute of Technology L ausanne (EPFL)
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