首页|University of Potsdam Reports Findings in Machine Learning (Machine learning of metabolite-protein interactions from model-derived metabolic phenotypes)
University of Potsdam Reports Findings in Machine Learning (Machine learning of metabolite-protein interactions from model-derived metabolic phenotypes)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news originating from Potsdam, Germany, by N ewsRx correspondents, research stated, “Unraveling metaboliteprotein interactio ns is key to identifying the mechanisms by which metabolism affects the function of other cellular layers. Despite extensive experimental and computational effo rts to identify the regulatory roles of metabolites in interaction with proteins , it remains challenging to achieve a genome-scale coverage of these interaction s.” Financial supporters for this research include European Union’s Horizon 2020, Kl aus Tschira Boost Fund. Our news journalists obtained a quote from the research from the University of P otsdam, “Here, we leverage established gold standards for metabolite-protein int eractions to train supervised classifiers using features derived from genome-sca le metabolic models and matched data on protein abundance and reaction fluxes to distinguish interacting from non-interacting pairs. Through a comprehensive com parative study, we explore the impact of different features and assess the effec t of gold standards for noninteracting pairs on the performance of the classifi ers. Using data sets from and , we demonstrate that the features constructed by integrating fluxomic and proteomic data with metabolic phenotypes predicted from genome-scale metabolic models can be effectively used to train classifiers, acc urately predicting metabolite-protein interactions in the context of metabolism. Our results reveal that the high performance of classifiers trained on these fe atures is unaffected by the method used to generate gold standards for non-inter acting pairs.”
PotsdamGermanyEuropeCyborgsEmerg ing TechnologiesGeneticsMachine Learning
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