节点适应性图形变压器具有结构编码,用于准确和强大的lncRNA疾病关联预测
Guanghui Li1, Peihao Bai2, Cheng Liang3
1School of Information Engineering, East China Jiaotong University, Nanchang, China. ghli16@hnu.edu.cn.
BMC genomics
|January 17, 2024
概括
我们开发了NAGTLDA,这是一种新的计算模型,可以准确地预测长非编码RNA (lncRNA) -疾病关联. 这种方法提高了对疾病机制的理解,并有助于开发新的治疗策略.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 长非编码RNAs (lncRNAs) 在基因调节,细胞分化和癌症发育中起着至关重要的作用.
- 预测lncRNA与疾病的关联可以加深对疾病发病的理解,并支持治疗的发展.
研究的目的:
- 引入一个创新的计算模型,NAGTLDA,用于预测未知的lncRNA-疾病关联.
- 利用图形神经网络和注意力机制来改善关联预测.
主要方法:
- 利用节点适应性特征平滑 (NAFS) 进行本地特征学习.
- 员工结构深度网络嵌入 (SDNE) 用于编码网络结构.
- 集成式变压器模块具有多个头的注意力,用于捕获全球协会和网络结构编码的诱导偏差.
主要成果:
- NAGTLDA实现了高性能,平均AUC为0.9531和AUPR为0.9537. 这是一个很好的表现.
- 与现有的最先进的方法相比,在5倍交叉验证中表现出优越的性能.
- 案例研究验证了60个预测的lncRNA疾病关联中的55个,突出显示了该模型的预测准确性.
结论:
- 拟议的NAGTLDA模型为预测生物信息关联提供了一种高效的计算方法.
- 这些发现强调了图形转换器结构在揭示复杂的 lncRNA-疾病关系方面的潜力.
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