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基于多特征学习的表观遗传标识策略.

Lingfeng Chen1, Rui Gu1, Yuanyuan Li1

  • 1Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China.

Journal of biomolecular structure & dynamics
|October 12, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种使用机器学习的新型表观遗传标识策略 (ETI-Strategy). 该ETI战略准确地预测了药物发现的表观遗传标,优于现有的方法.

关键词:
在表观遗传学上,表观遗传学是表观遗传学.机器学习是机器学习.多任务处理能力.目标识别 目标识别

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科学领域:

  • 生物化学 生物化学
  • 计算生物学 计算生物学
  • 药理学 药理学是指药理学的学科.

背景情况:

  • 在癌症治疗中,表观遗传药物越来越重要.
  • 鉴定生物活性化合物的表观遗传标对于药物发现至关重要.
  • 化学基因组数据的进步需要改进目标识别方法.

研究的目的:

  • 引入一种新的表观遗传标识策略 (ETI-战略).
  • 利用机器学习来预测蛋白质 - 配体相互作用和识别表观遗传点.
  • 提高表观遗传药物发现的准确性和效率.

主要方法:

  • 集成一个多任务图形卷积神经网络之前的模型.
  • 使用蛋白质 - 配体相互作用分类歧视模型的利用.
  • 使用55个表观遗传标的大规模生物活性数据进行培训和验证.

主要成果:

  • 实现了先前模型的AUC为0.934和歧视模型的0.830.
  • 在预测蛋白质-连接体相互作用方面表现优于反向对接.
  • 与其他开源目标识别工具相比,证明了更高的准确性.

结论:

  • 该ETI战略有效地利用分子和蛋白质水平的信息来准确预测活动.
  • 机器学习对识别潜在的表观遗传点作出了重大贡献.
  • 这种方法为表观遗传药物发现和开发提供了一条新的途径.