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蛋白质适应性预测受到语言模型,合奏学习和采样方法的相互作用的影响

Mehrsa Mardikoraem1,2, Daniel Woldring1,2

  • 1Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, MI 48824, USA.

Pharmaceutics
|May 27, 2023
PubMed
概括

机器学习 (ML) 通过分析序列数据来增强蛋白质设计. 这项研究通过比较采样技术和序列表示来优化蛋白质工程的ML模型,改善结合亲和力和稳定性预测.

关键词:
在MCDA中,MCDA是MCDA.这里是TOPSIS的地图.嵌入式 嵌入式 嵌入式组合学习组合学习不平衡的测试标记数据集.机器学习是机器学习.蛋白质健身预测 蛋白质健身预测采样方法 采样方法序列表示表示 序列表示

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