可解释和可解释的预测机器学习模型用于数据驱动的蛋白质工程

David Medina-Ortiz1, Ashkan Khalifeh2, Hoda Anvari-Kazemabad3

  • 1Department of Bioorganic Chemistry, Leibniz Institute of Plant Biochemistry, Weinberg 3, 06120 Halle, Germany; Departamento de Ingeniería En Computación, Universidad de Magallanes, Avenida Bulnes, 01855, Punta Arenas, Chile.; Centre for Biotechnology and Bioengineering, CeBiB, Universidad de Chile, Beauchef 851, Santiago, Chile.

Biotechnology advances
|December 7, 2024
PubMed
概括

可解释的人工智能 (XAI) 通过使人工智能模型可解释来增强蛋白质工程. 这种方法提高了信任,并指导机器学习辅助的定向进化,以获得更好的蛋白质设计.

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