首页|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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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.”

MinneapolisMinnesotaUnited StatesN orth and Central AmericaAmino AcidsBasic Amino AcidsBiological FactorsCy borgsDiamino Amino AcidsDioxygenasesEmerging TechnologiesEngineeringEn zymes and CoenzymesEssential Amino AcidsHemeLysineMachine LearningMeta lloporphyrinsOxygenasesUniversity of Minnesota Twin Cities

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN：
年,卷(期)：2024.(Dec.27)