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一种人工智能过的混合无监督方法,用于自动处理细胞DNA编码库 (DEL) 数据集.

Yiran Huang1, Xiao Tan2,3, Xiaoyu Li4,5

  • 1School of Pharmacy, Shenzhen University Medical School, Shenzhen University, Shenzhen 518060, China.

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本研究引入了一种自动化方法,用于分析来自活细胞选择的DNA编码库 (DEL) 数据. 这种新方法提高了打击识别的准确性和效率,克服了药物发现中背景噪声带来的挑战.

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科学领域:

  • 生物技术是生物技术.
  • 药用化学 医学化学
  • 计算生物学 计算生物学

背景情况:

  • DNA编码图书馆 (DEL) 技术是药物开发中的一个关键平台.
  • 基于活细胞的选择增强了药物候选发现中的生物相关性.
  • 噪音数据和背景信号在基于细胞的DEL选择中挑战了击中特征.

研究的目的:

  • 开发用于基于细胞的DEL数据集的自动化数据处理方法.
  • 提高确定有前途的候选药物的准确性和效率.
  • 为了应对背景噪音在噪音测序数据中所带来的挑战.

主要方法:

  • 开发了一个无监督的算法,用于数据预处理,特征提取和异常选.
  • 实现了基于描述符的分类和相似度评分排名,用于匹配识别.
  • 在针对胰岛素受体 (INSR) 和细胞血栓形成素受体 (TPOR) 的DEL数据集上验证了该方法.

主要成果:

  • 该自动化方法准确有效地从大型DEL数据集中识别出有希望的结果.
  • 在各种图书馆规模 (3000万至1033亿成员) 中与实验结果保持高度一致.
  • 在不同目标蛋白 (INSR和TPOR) 中展示了算法概括能力.

结论:

  • 开发的自动化工作流程在基于单元格的DEL选择中有效地将被击中化合物与背景噪声区分开来.
  • 这种方法通过简化候选药物识别来促进加速药物发现.
  • 该方法广泛适用于药物开发管道中的自动化命中分化.