GCNFORMER:图形卷积网络和变压器用于预测 lncRNA-疾病关联
Dengju Yao1, Bailin Li2, 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 3, 2024
概括
这项研究介绍了GCNFORMER,这是一种用于预测长非编码RNA疾病关联 (LDAs) 的新算法. GCNFORMER有效地识别了 lncRNA 和疾病之间的联系,有助于诊断和降低成本.
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) 的表达中断与各种人类疾病有关.
- 准确预测 lncRNA-疾病关联 (LDAs) 对诊断和成本效益至关重要.
研究的目的:
- 开发一种新的算法,GCNFORMER,用于预测lncRNA与疾病的关联.
- 利用图形卷积网络和变压器进行增强的LDA预测.
主要方法:
- 集成的miRNAs, lncRNAs和疾病的类内相似性和类间连接,以构建图形邻矩阵.
- 使用图形卷积网络在节点之间进行特征提取.
- 使用具有多头注意力的变压器编码器进行全球依赖性分析和LDA预测.
主要成果:
- 在五倍交叉验证中,GCNFORMER获得了高性能,AUC为0.9739和AUPR为0.9812.
- 与六个现有的LDA预测模型相比,表现优越.
- 通过对乳腺癌,结肠癌和肺癌的病例研究验证了有效性.
结论:
- 图形卷积网络和变压器的集成显著改善了LDA预测模型的性能.
- 这种方法促进了 lncRNA-疾病关联研究的进步.
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