基于GCN的异质复合特征学习以提高LncRNA-疾病关联的可预测性
Yi Zhang1,2, Gangsheng Cai1,2, Xin Li1,2
1Guilin University of Technology, Guilin 541004, China.
ACS omega
|January 15, 2024
概括
一个新的计算模型,HGCNLDA,通过整合图形卷积网络和异质信息融合,准确地预测长非编码RNA疾病关联 (LDAs),优于现有的方法识别与疾病相关的lncRNAs.
科学领域:
- 计算生物学是一种计算生物学.
- 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:
- 计算模型对于预测长非编码RNA-疾病关联 (LDAs) 来理解疾病发病过程至关重要.
- 现有的模型很难捕捉生物网络中的复杂特征.
研究的目的:
- 提出HGCNLDA,一种用于推断LDA的新型计算模型.
- 加强从生物网络数据中提取基本特征.
主要方法:
- 构建了一个三方异质网络 (lncRNA-疾病-miRNA网络,LDMN).
- 使用基于图形卷积网络 (GCN) 的编码器进行特征提取.
- 利用具有双聚合和注意力机制的特征融合.
- 应用了二线解码器来预测关联得分.
主要成果:
- 在两个数据集的5倍交叉验证中,HGCNLDA在5个现有模型中表现出优异的性能.
- 实现了高AUROC和AUPR值,特别是在具有挑战性的数据集上.
- 案例研究证实了HGCNLDA在识别癌症中潜在的LDA中的实用性.
结论:
- 通过利用异构信息融合和GCN,HGCNLDA有效地预测了 lncRNA-疾病关联.
- 该模型提供了一个实用的工具,用于探索疾病的发病因子和识别潜在的治疗点.
- 源代码和数据是公开可用的,以便进一步研究.
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