KGRACDA:一个基于知识图的模型,来自循环和注意力聚合,用于CircRNA-疾病关联预测
概括
预测循环RNA (circRNA) 和疾病关联 (CDA) 对人类健康至关重要. 我们的新模型KGRACDA有效地捕捉了本地和全球图形特征,以准确地预测circRNA疾病关联.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 循环RNAs (circRNAs) 越来越多地被认为是它们在人类疾病中的作用.
- 准确预测circRNA-疾病关联 (CDAs) 对了解疾病机制至关重要.
- 现有的计算方法,特别是深度学习,往往忽视了在图形结构中提取局部深度信息.
研究的目的:
- 开发一种新的计算模型,KGRACDA,用于预测circRNA与疾病的关联.
- 整合明确的结构特征和隐含的图形嵌入信息,以提高预测准确度.
- 解决捕获局部深度信息的现有方法的局限性.
主要方法:
- 构建一个包含RNA和疾病的大规模,多源异质知识图.
- 使用递归方法生成多跳子图来挖掘局部深度信息.
- 采用一个优化的图表注意力机制与一个门机制和一个多头注意力机制,以平衡全球和本地图表特征.
主要成果:
- 拟议的KGRACDA模型在知识图中有效地捕捉了本地和全球深度特征.
- 与现有方法相比,KGRACDA在预测circRNA与疾病的关联方面表现优越.
- 为数据可视化和CDA预测提供了一个更新的交互式网络平台,HNRBase v2.0.
结论:
- 通过利用知识图表和注意力机制,KGRACDA提供了一种强大的新方法来预测circRNA与疾病的关联.
- 该模型能够挖掘本地和全球深度信息,从而提高预测准确度.
- 该HNRBase v2.0平台便于访问circRNA数据和利用KGRACDA模型进行研究.
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