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释放药物组合的治疗潜力,通过使用图形变压器网络的协同预测
Waleed Alam1, Hilal Tayara2, Kil To Chong3
1Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, 54896, South Korea.
Computers in biology and medicine
|January 19, 2024
概括
一个新的计算模型,SynergyGTN,使用深度学习来预测用于癌症治疗的协同药物组合. 这种方法比现有的方法更有效,更准确,有助于药物发现.
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.
- 药物发现 药物发现
背景情况:
- 药物组合对于癌症治疗至关重要,改善疗效并减少副作用.
- 对于大型数据集来说,对协同作用药物组合的实验性识别是昂贵且耗时的.
- 深度学习为分析复杂的生物数据提供了强大的计算方法.
研究的目的:
- 开发一种高效可靠的计算模型,用于预测针对癌症细胞系的协同药物组合.
- 为了利用图形转换器网络 (GTN) 来预测药物协同作用.
主要方法:
- 开发了基于图形变压器网络 (GTN) 的模型SynergyGTN.
- 以图形形式表示药物,其中包含原子特征向量和键特征.
- 综合药物图表特征与癌症细胞系基因表达特征.
- 利用密集连接的层来进行最终的预测.
主要成果:
- 与最先进的方法相比,SynergyGTN表现出优越的性能.
- 通过5倍交叉验证,通过曲线下的接收器运行特征面积 (ROC AUC) 实现了5%的改善.
- 在离开药物 (8%),离开组合 (1%) 和离开组织 (2%) 验证策略中表现出更高的准确性.
- 在Astrazeneca梦想数据集上验证了概括性,在平衡准确度上提高了13%.
结论:
- 协同GTN提供了一种可靠和高效的计算方法,用于预测癌症治疗中的协同药物组合.
- 开发的SynergyGTN模型及其相关的Web服务器工具可以显著帮助制药行业和研究人员在药物发现工作中.
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