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基于集体和多任务注意力模型预测药物组合协同效应的多视图特征表示
Samar Monem1, Aboul Ella Hassanien2, Alaa H Abdel-Hamid3
1Mathematics and Computer Science Department, Faculty of Science, Beni-Suef University, Beni-Suef, 62521, Egypt. samarmahmoud@science.bsu.edu.eg.
Journal of cheminformatics
|September 28, 2024
概括
一种新的多视图整体预测模型通过整合多种分子和细胞特征,准确地识别癌症细胞系的协同药物组合. 这种方法通过预测协同成绩和类标签来增强药物发现,改善治疗策略.
科学领域:
- 计算生物学和生物信息学
- 药理学和药物发现
- 机器学习在医学中的应用
背景情况:
- 确定协同作用的药物组合对于有效的癌症治疗至关重要.
- 目前的方法经常与药物细胞系相互作用的复杂性作斗争.
- 预测建模为加速发现协同作用药物对提供了一个有希望的途径.
研究的目的:
- 开发和验证一种新型的多视图整体预测模型 (MVME),用于协同作用的药物组合.
- 预测药物对的定量协同得分和定性协同类标签.
- 利用多样化的分子和细胞数据视图来提高预测准确度.
主要方法:
- 使用四种视图表示药物:微笑,分子图,指纹和药物向相互作用.
- 通过四种视图捕获的细胞系特征:基因表达,拷贝数,突变和蛋白质组学.
- 采用了多任务注意力深度学习模型,整合了16个配对的药物-细胞系视图,然后采用组合模型进行最终预测.
主要成果:
- MVME模型在协同效应得分预测方面取得了0.76的皮尔森得分 (RMSE:14.30).
- 对于协同类预测,该模型表现出高性能,准确度为0.90,精度为0.96和ROC-AUC为0.96.
- 在O'Neil数据集上进行评估,包括39个癌症细胞系中的22,737种药物组合.
结论:
- 拟议的多视图组合方法有效预测协同药物组合.
- 整合多个特征表示增强了模型捕捉复杂药物反应关系的能力.
- 这种方法在推进精确瘤学和个性化医学方面具有重大潜力.
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