LncRNA-Disease Association Prediction Based on Integrated Application of Matrix Decomposition and Graph Contrastive Learning

被引:0
|
作者
Tang, Guangyi [1 ]
Zhang, Qingbao [1 ]
Yao, Dengju [1 ]
Zhan, Xiaojuan [1 ,2 ]
机构
[1] Harbin Univ Sci & Technol, Sch Comp Sci & Technol, Harbin 150080, Peoples R China
[2] Heilongjiang Inst Technol, Coll Comp Sci & Technol, Harbin 150050, Peoples R China
基金
中国国家自然科学基金;
关键词
lncRNA-disease association prediction; three-layer heterogeneous network; nonnegative matrix decomposition; singular value decomposition; graph contrastive learning; SIMILARITY; DATABASE;
D O I
10.1007/978-981-97-5128-0_18
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Investigating the potential associations between long noncoding RNAs (lncRNAs) and diseases is crucial for advancing disease research and the development of therapeutic approaches. Nevertheless, the current lncRNA-disease association (LDA) data exhibits sparsity, hindering existing LDA prediction models from effectively capturing the features of lncRNAs and diseases. We introduce an LDA prediction model that integrates matrix decomposition and graph contrastive learning, MDGCLLDA, to address these challenges. Our approach accurately predicts lncRNA-disease associations (LDAs) by extracting features of lncRNAs, miRNAs, and embedded disease characteristics. We constructed a three-layer heterogeneous network encompassing lncRNAs, miRNAs, and diseases (LMDG), integrating their intricate interactions by examining similarities and associations. To capture comprehensive features of lncRNAs and diseases, we applied nonnegative matrix decomposition and singular value decomposition to the adjacency matrix of the heterogeneous network. Subsequently, an unsupervised embedding model enhanced local and global information exchange between nodes using a graph convolutional network encoder within graph contrastive learning. Finally, XGBoost was employed to predict LDA. The MDGCLLDA model demonstrated superior performance to the four benchmark models. Ablation experiments and case studies further validated the model's reliability and effectiveness
引用
收藏
页码:224 / 236
页数:13
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