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MMFSyn:一种多模式深度学习模型,用于预测抗癌协同药物组合效应.
Tao Yang1,2, Haohao Li2, Yanlei Kang1
1School of Information Engineering, Huzhou University, Huzhou 313000, China.
确定协同作用的药物组合是一个挑战. 这项研究介绍了MMFSyn,一种使用多式药物数据和细胞系特征的深度学习模型,以准确预测协同作用的抗癌效应.
科学领域:
- 计算生物学是一种计算生物学.
- 药物发现 药物发现
- 生物信息学是一种生物信息学.
背景情况:
- 组合疗法提高了疗效,降低了毒性.
- 确定协同作用的药物组合是复杂和具有挑战性的.
- 目前的方法与越来越多的药物组合作斗争.
研究的目的:
- 开发一种新的深度学习模型,MMFSyn,用于预测协同作用的抗癌药物组合.
- 将多模式药物数据与癌症细胞系特征集成,以提高预测准确度.
- 为了应对在复杂的治疗方案中识别协同作用药物关系的挑战.
主要方法:
- 使用了通过SMILES提取的多式联络药物数据 (摩根指纹,原子序列,分子图,原子点云).
- 应用Bi-LSTM,gMLP,多头注意力和多尺度GCN用于药物特征提取.
- 综合基因表达和突变将癌症细胞系的数据用于构建细胞系特征.
- 结合提取特征来预测协同作用的抗癌药物组合效应.
主要成果:
- 与现有方法相比,MMFSyn表现出优越的性能.
- 实现了根平均平方误差 (RMSE) 的13.33.
- 获得了0.81的皮尔森相关系数 (PCC),表明了强大的预测准确性.
结论:
- MMFSyn有效地捕捉了多式联络药物数据和omics特征之间的复杂关系.
- 该模型显著改善了协同作用的抗癌药物组合的预测.
- 突出了深度学习在推动药物发现和组合疗法的潜力.
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