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一个多任务图表深度学习模型来预测药物组合的协同效应和灵敏度得分
Samar Monem1,2, Aboul Ella Hassanien3,4, Alaa H Abdel-Hamid5
1Mathematics and Computer Science Department, Faculty of Science, Beni-Suef University, Beni Suef, 62521, Egypt. samarmahmoud@science.bsu.edu.eg.
BMC bioinformatics
|October 10, 2024
概括
这项研究介绍了MultiComb,这是一种深度学习模型,可以预测药物组合协同作用和癌症治疗敏感性. 该模型显示了比现有方法更好的性能,有助于开发有效的组合疗法.
科学领域:
- 计算生物学是一种计算生物学.
- 药理学 药理学是指药理学的学科.
- 人工智能的人工智能是人工智能.
背景情况:
- 药物组合可以提高疗效,减少治疗癌症等复杂疾病的副作用.
- 多重向药物组合对于最大化治疗效果和实现协同作用至关重要.
研究的目的:
- 开发一个名为"MultiComb"的多任务深度学习 (MTDL) 模型,用于预测药物组合协同作用和灵敏度.
- 同时预测药物组合对特定癌症细胞系的协同作用和灵敏度得分.
主要方法:
- 使用图形卷积网络处理药物SMILES (简化分子输入线输入) 表示.
- 采用完全连接的子网络和注意力机制来提取和整合药物和癌症细胞系特征.
- 实施了交叉拼接模型来学习任务间的关系,以预测协同效应和灵敏度.
主要成果:
- 在O'Neil基准数据集 (17,901种药物对跨越37种癌细胞系) 上得到验证.
- 取得的平均协同效应得分为232.37 (MSE),9.59 (MAE),0.57 (R2),0.76 (斯皮尔曼) 和0.73 (皮尔森).
- 获得的平均敏感度分数为15.59 (MSE),2.74 (MAE),0.90 (R2),0.95 (斯皮尔曼) 和0.95 (皮尔森).
结论:
- 拟议的MTDL模型MultiComb有效地预测了药物组合的协同作用和敏感性.
- 与针对癌症治疗开发的现有方法相比,MultiComb表现出优越的性能.
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