青岛大学学报（自然科学版）2024，Vol.37Issue(1)：20-26,7.DOI:10.3969/j.issn.1006-1037.2024.01.04
基于图数据增强的疾病与基因关联挖掘
Disease and Gene Association Mining Based on Graph Data Enhancement
摘要
Abstract
In view of the incompleteness of existing association data and the inadequacy of multi-source omics data,computational indexes based on three-hop local topological similarity were designed to identify biologically significant but unmapped Protein-Protein Interactions(PPI).A novel graph neural network method(GDaEPred)based on graph data enhancement was proposed for mining disease-gene associations.Experimental results showed that the average accuracy of GDaEPred was improved by 4.1%,and the pre-cision,recall and F1 score were also improved.关键词
图神经网络/图数据增强/致病基因预测Key words
graph neural networks/graph data enhancement/disease gene prediction分类
信息技术与安全科学引用本文复制引用
贾祥虎,吴舜尧..基于图数据增强的疾病与基因关联挖掘[J].青岛大学学报（自然科学版）,2024,37(1):20-26,7.基金项目
山东省自然科学基金(批准号:ZR2019PF012)资助 （批准号:ZR2019PF012）
山东省高等学校科技计划项目(批准号:J18KA356)资助. （批准号:J18KA356）
