机器学习驱动的数据估值,用于优化高通量选管道

Joshua Hesse1, Davide Boldini1, Stephan A Sieber1

  • 1Technical University of Munich, TUM School of Natural Sciences, Department of Bioscience, Center for Functional Protein Assemblies (CPA), 85748 Garching bei München, Germany.

概括

本研究应用数据估值来改进药物发现的高通量查 (HTS). 它增强了积极的学习,识别了真正的积极/消极因素,并平衡了数据,使药物开发更加有效和准确.

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