从基因网络到疗法:一种因果推理和深度学习方法用于药物发现
Sudhir Ghandikota1, Anil G Jegga1,2
1Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA.
这项研究引入了一个结合因果推断和深度学习的计算框架,以加速治疗像异常性肺纤维化 (IPF) 这样的复杂疾病的药物发现. 它识别了新的基因标和潜在的药物候选者,简化了开发过程.
科学领域:
- 计算生物学是一种计算生物学.
- 基因组学就是基因组学.
- 药理学 药理学是指药理学的学科.
背景情况:
- 药物发现是耗时和昂贵的,特别是在复杂的疾病.
- 异形性肺纤维化 (IPF) 由于异质性和未知的机制而带来挑战.
研究的目的:
- 开发一个新的计算框架,集成网络分析,统计调解和深度学习.
- 为了确定因果目标基因和IPF的可重复利用的小分子候选者.
主要方法:
- 权重基因共同表达网络分析 (WGCNA) 和双向调解分析 (因果WGCNA) 应用于IPF患者的转录组数据.
- 使用DeepCE模型进行了基于深度学习的化合物查,对已识别的因果基因进行了查.
主要成果:
- 在IPF中发现了7个显著相关的模块和145个因果基因.
- 五个基因 (ITM2C,PRTFDC1,CRABP2,CPNE7,NMNAT2) 预测了IPF疾病的严重程度.
- 泰拉格莱纳斯塔特,梅雷斯蒂尼布和基洛斯塔成为有前途的候选药物.
结论:
- 结合因果推断和深度学习是药物发现的有效方法.
- 该框架确定了新的IPF目标和治疗候选药物.
- 这种方法为表型驱动的药物发现和重新利用提供了一个可扩展的策略.
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