基于图表生成和多源信息融合的因果增强药物向相互作用预测
Guanyu Qiao1, Guohua Wang1,2, Yang Li2
1School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.
Bioinformatics (Oxford, England)
|September 23, 2024
概括
这项研究引入了一种因果增强的药物向相互作用 (CE-DTI) 预测方法. 该方法通过识别潜在目标并提高模型解释性来改善药物发现.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 药物向相互作用 (DTI) 的预测对于开发有效疗法至关重要.
- 现有的DTI方法难以解释,需要手动的特性工程.
- 图表生成为DTI预测提供灵活的信息融合.
研究的目的:
- 开发一种用于药物向相互作用 (CE-DTI) 预测的新型因果增强方法.
- 提高DTI预测模型的准确性和可解释性.
- 利用图表生成和多源信息融合来提高DTI预测.
主要方法:
- 通过自动图表生成,通过融合多源信息来表示药物和目标.
- 为节点分类构建药物标对网络.
- 分离因果和非因果变量节点并应用因果不变性来进行对比学习.
主要成果:
- 拟议的CE-DTI方法在多个数据集上的基准方法相比,实现了更高的性能.
- 因果增强策略有效地确定了药物标对之间的潜在因果影响.
- 该方法在发现新的潜在药物点方面取得了成功.
结论:
- CE-DTI提供了一种有效和可解释的方法来预测药物向相互作用.
- 因果增强策略有助于发现新的治疗点.
- 这种方法有助于开发有针对性的疗法,提高疗效,减少副作用.
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