AMGDTI:基于异质网络中的自适应元图学习的药物向相互作用预测
Yansen Su1, Zhiyang Hu1, Fei Wang1
1Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, 230601, China.
Briefings in bioinformatics
|December 25, 2023
概括
我们开发了AMGDTI,这是一种用于预测药物向相互作用 (DTI) 的新方法. 这种自适应的元图方法通过准确识别潜在的分子结构和关系来增强药物发现.
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:
- 药物向相互作用 (DTI) 对于识别候选药物和重新使用现有的药物至关重要.
- 网络表示学习已经显示出对DTI预测的希望,但受到手动设计的元图形的限制.
- 从异质网络中提取复杂的语义信息仍然是一个挑战.
研究的目的:
- 为改进DTI预测引入基于自适应元图的方法 (AMGDTI).
- 在捕获复杂的语义信息时克服手工设计的元图的局限性.
- 为了促进有效的信息整合和药物发现,而不需要领域专业知识.
主要方法:
- 开发了AMGDTI,一种基于自适应元图的方法,用于DTI预测.
- 通过自适应的元图训练从异质网络中采用自动语义信息聚合.
- 在两个基准数据集上验证了方法.
主要成果:
- 在DTI预测中,AMGDTI的表现优于八种最先进的方法.
- 该方法准确地确定了新的药物向相互作用.
- 适应性元图表在捕获复杂的语义信息和网络拓学方面表现出灵活性.
结论:
- AMGDTI提供了一种强大而准确的方法来预测药物向相互作用.
- 适应性元图有效地学习复杂的网络拓,并推断出潜在的药物目标关系.
- 这种方法通过改进分子相互作用的识别来推进药物发现和重新定位.
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