Data from University of Minnesota Twin Cities Advance Knowledge in Machine Learn ing (Machine Learning Guided Rational Design of a Non-heme Iron-based Lysine Dio xygenase Improves Its Total Turnover Number)
Data from University of Minnesota Twin Cities Advance Knowledge in Machine Learn ing (Machine Learning Guided Rational Design of a Non-heme Iron-based Lysine Dio xygenase Improves Its Total Turnover Number)
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Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Current study results on Machine Learn ing have been published. According to newsreporting originating in Minneapolis, Minnesota, by NewsRx journalists, research stated, “Highly selectiveC-H functi onalization remains an ongoing challenge in organic synthetic methodologies. Bio catalysts arerobust tools for achieving these difficult chemical transformation s.”
Key words
Minneapolis/Minnesota/United States/N orth and Central America/Amino Acids/Basic Amino Acids/Biological Factors/Cy borgs/Diamino Amino Acids/Dioxygenases/Emerging Technologies/Engineering/En zymes and Coenzymes/Essential Amino Acids/Heme/Lysine/Machine Learning/Meta lloporphyrins/Oxygenases/University of Minnesota Twin Cities
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