特定拓和拓连接灵敏度增强图形学习用于lncRNA-疾病关联预测
Ping Xuan1, Honglei Bai2, Hui Cui3
1Department of Computer Science, School of Engineering, Shantou University, Shantou, China.
Computers in biology and medicine
|August 2, 2023
概括
这项研究引入了NCPred,这是一种新的图形学习方法,用于预测与疾病相关的长非编码RNA (lncRNAs). NCPred有效地整合了网络拓和属性,在识别疾病候选 lncRNAs 方面表现优于现有的方法.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
背景情况:
- 长非编码RNAs (lncRNAs) 在人类疾病的发病过程中至关重要.
- 预测与疾病相关的lncRNAs有助于了解疾病机制.
- 在 lncRNA 和疾病网络中提取网络拓和关系是具有挑战性的.
研究的目的:
- 开发一种基于图形学习的新方法,用于预测与疾病相关的候选长非编码RNA (lncRNAs).
- 从单个网络中有效编码特定和本地拓,并集成连接关系.
- 提高识别潜在疾病相关 lncRNA 的准确性和效率.
主要方法:
- 基于相似之处构建了个别的 lncRNA 和疾病网络.
- 采用了网络意识的图形卷积自编码器进行拓编码.
- 在一个整合lncRNAs,疾病和miRNAs的异质网络上利用了连接敏感的图形神经网络.
- 集成的注意力机制和多层卷积神经网络与加权残余.
主要成果:
- 与七种最先进的预测方法相比,提出的NCPred方法显示出更高的性能.
- 废除研究证实了本地拓学习,邻居拓学习和对对属性编码的重要性.
- 关于前列腺癌,肺癌和乳腺癌的案例研究验证了NCPred选潜在候选疾病相关 lncRNA 的能力.
结论:
- NCPred提供了一种强大而有效的方法来预测与疾病相关的lncRNAs.
- 该方法能够整合多样化的网络信息,提高了预测准确度.
- 对于推进疾病病原体研究和生物标志物发现,NCPred具有显著的潜力.
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