通过自适应地调整异质网络的拓结构来预测药物蛋白相互作用
IEEE journal of biomedical and health informatics
|September 6, 2023
概括
这项研究引入了SATS,一种通过适应性调整网络结构来预测药物蛋白相互作用 (DPI) 的新方法. SATS 提高了预测准确度,特别是在不完整数据的情况下,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:
- 预测药物蛋白相互作用 (DPI) 对于药物发现和重新使用至关重要.
- 由于未知的药物/蛋白质功能,现有的方法难以处理不完整的数据和不可靠的异质网络.
- 图形神经网络 (GNN) 提供了强大的工具,但需要强大的网络结构.
研究的目的:
- 开发一种新的DPI预测方法,可以自适应地调整异质网络的拓结构.
- 在DPI预测中应对不完整数据和不可靠网络所带来的挑战.
- 提高计算药物发现管道的准确性和可靠性.
主要方法:
- 提出SATS,一种利用图表注意网络 (GAN) 在药物-蛋白质异质网络中进行表示学习的方法.
- 实现基于属性的节点关系的自我适应性学习.
- 包含基于模型训练损失的网络拓结构的动态调整.
- 使用已学习嵌入的预测药物蛋白相互作用倾向.
主要成果:
- SATS有效地改善了异质网络的拓结构.
- 与最先进的DPI预测方法相比,在各种指标上表现出卓越的性能.
- 展示了处理不完整数据和不可靠网络的实用性.
- 案例研究证实了SATS在发现新型药物蛋白相互作用方面的能力.
结论:
- SATS为预测药物与蛋白质相互作用提供了一个强大的解决方案,特别是在数据有限的场景中.
- 适应性网络结构调整机制提高了预测准确性和可靠性.
- 通过识别新的相互作用,SATS具有加速药物发现和重新定位努力的巨大潜力.
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