基于自编码器的恶性疾病药物协同作用框架
Pooja Rani1, Kamlesh Dutta1, Vijay Kumar2
1Computer Science and Engineering Department, National Institute of Technology, Hamirpur, HP, 177005, India.
Computational biology and chemistry
|November 10, 2024
概括
这项研究介绍了AESyn,这是一种基于自编码器的框架,可以准确预测用于癌症治疗的协同药物组合. 它有效地在广的药物空间中导航,超过了改善癌症治疗的现有方法.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 机器学习在药物发现中的作用
背景情况:
- 与单一治疗相比,药物组合提供了更好的疗效,降低了毒性,并在治疗恶性疾病时克服了耐药性.
- 对潜在药物组合的实证探索是具有挑战性的,因为组合空间广.
- 机器学习和深度学习方法越来越多地用于在大型数据集中识别协同作用的药物组合.
研究的目的:
- 提出AESyn,一种基于自编码器的新型框架,用于预测恶性疾病中的药物协同作用.
- 为了利用一个字包编码技术来提取药物向基因和药物特征.
- 使用分类和回归指标评估框架的性能,并与现有方法进行比较.
主要方法:
- 开发了AESyn,这是一个基于自动编码器的框架,利用词包编码来表示药物向基因.
- 将药物嵌入物和药物向基因输入到自动编码器中以提取特征.
- 通过使用来自NCI-ALMANAC和O'Neil数据集的选数据进行了框架培训和验证.
- 使用分类和回归指标评估性能,包括准确性,AUROC和MAPE.
主要成果:
- 拟议的AESyn框架实现了高预测性能.
- 获得了95%的准确性和94.2%的接收器操作特征曲线 (AUROC) 下面面积.
- 显示了7.2的平均绝对百分比误差 (MAPE),表明精确的回归预测.
结论:
- 基于自编码器的AESyn框架提供了一种稳定且独立于顺序的方法,用于预测恶性疾病中的药物协同作用.
- 该框架有效地提取药物特征并预测协同组合,提供了一个有前途的计算方法.
- 与现有方法相比,AESyn表现出优越的性能,为在瘤学中更有效的药物发现铺平了道路.
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