HRGCNLDA:基于层次精细化图形卷积神经网络的lncRNA疾病关联的预测
Li Peng1,2, Yujie Yang1, Cheng Yang1
1College of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China.
Mathematical biosciences and engineering : MBE
|June 14, 2024
概括
这项研究介绍了HRGCNLDA,这是一种用于预测长非编码RNA (lncRNA) -疾病关联的新计算方法. HRGCNLDA提高了识别潜在癌症生物标志物和治疗点的准确性.
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:
- 长非编码RNAs (lncRNAs) 是生物过程中的关键调节者,包括癌症免疫检查点.
- lncRNAs显示出作为癌症生物标志物和治疗点的前景.
- 对于 lncRNA-疾病关联发现的实验方法是耗时和劳动密集的.
研究的目的:
- 开发一种准确和高效的计算方法来预测 lncRNA 与疾病的关联.
- 为了解决这个任务的图形卷积神经网络中的过度平滑问题.
主要方法:
- 拟议的HRGCNLDA是一种层次精细化图形卷积神经网络方法.
- 在消息传播和节点更新期间增强层次表示.
- 专注于放大隐藏层贡献,同时最大限度地减少差异.
主要成果:
- 与现有方法相比,HRGCNLDA取得了更高的性能.
- 在实验中证明了最高的AUC-ROC和AUC-PR值.
- 通过对乳腺癌,肺癌和胃癌的病例研究验证了疗效.
结论:
- HRGCNLDA是一个可靠和有效的计算工具,用于 lncRNA-疾病关联预测.
- 该方法为实验技术提供了更有效的替代方案.
- 促进癌症生物标志物和治疗点发现的潜力.
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