预测 lncRNA-疾病协会使用多个元路径在等级图注意力网络
Dengju Yao1, Yuexiao Deng2, Xiaojuan Zhan2,3
1School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150080, China. ydkvictory@hrbust.edu.cn.
BMC bioinformatics
|January 29, 2024
概括
本研究介绍了MMHGAN,这是一种深度学习模型,通过分析复杂的网络结构,有效预测长非编码RNA (lncRNA) -疾病关联. 该模型的准确性很高,性能优于现有方法,有助于了解疾病的发病因子.
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) 在调节基因表达中起着至关重要的作用,并与复杂疾病的发病有关.
- 预测lncRNA与疾病的关联对于了解疾病机制至关重要,但由于大量的lncRNA和实验的局限性,这具有挑战性.
- 现有的计算方法往往忽略了网络结构中中间节点提供的信息.
研究的目的:
- 开发一种新的深度学习模型,MMHGAN,用于预测未知的lncRNA-疾病关联.
- 为了利用层次化的图形注意网络和多个元路径类型来增强功能提取.
- 通过考虑更广泛的网络信息,包括中间节点来改进现有方法.
主要方法:
- 构建一个整合lncRNA-疾病-miRNA关联的异质图和lncRNA和疾病的同质图.
- 多头注意力机制的应用,用于在同质图中聚合特征.
- 在异质图中选择和权衡具有不同中间节点的元路,以获得最终的嵌入式特征.
主要成果:
- 通过五重交叉验证,通过五重交叉验证获得了平均AUC96.07%和平均AUPR93.23%.
- 废弃实验证实了同质图形和不同的中间节点路径权重的重要性.
- 肺癌,食道癌和乳腺癌的案例研究显示,预测的lncRNA与疾病相关性具有很高的验证率.
结论:
- 与六种现有的预测模型相比,MMHGAN模型显示出更高的性能.
- 该模型有效地预测了潜在的lncRNA-疾病相关性,并通过案例研究进行了验证.
- MMHGAN提供了一种有前途的计算方法,用于推进 lncRNA-疾病关联研究.
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