ScADSATGRN: An Adaptive Diffusion Structure-Aware Transformer Based Method Inferring Gene Regulatory Networks from Single-Cell Transcriptomic Data

被引:0
|
作者
Yuan, Lin [1 ,2 ,3 ]
Zhao, Ling [1 ,2 ,3 ]
Li, Zhujun [4 ]
Hu, Chunyu [1 ,2 ,3 ]
Zhang, Shoukang [1 ,2 ,3 ]
Wang, Xingang [1 ,2 ,3 ]
Geng, Yushui [1 ,2 ,3 ]
机构
[1] Qilu Univ Technol, Shandong Comp Sci Ctr, Key Lab Comp Power Network & Informat Secur, Minist Educ,Shandong Acad Sci, Jinan, Peoples R China
[2] Qilu Univ Technol, Fac Comp Sci & Technol, Shandong Engn Res Ctr Big Data Appl Technol, Shandong Acad Sci, Jinan, Peoples R China
[3] Shandong Fundamental Res Ctr Comp Sci, Shandong Prov Key Lab Comp Networks, Jinan, Peoples R China
[4] Jinan Springs Patent & Trademark Off, Jinan, Peoples R China
来源
ADVANCED INTELLIGENT COMPUTING IN BIOINFORMATICS, PT II, ICIC 2024 | 2024年 / 14882卷
基金
中国国家自然科学基金;
关键词
Gene regulatory networks; scRNA-seq; Transformer; GNN;
D O I
10.1007/978-981-97-5692-6_31
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Gene regulatory networks unveil the interactions and regulatory relationships between genes, offering profound insights into cellular functional mechanisms. Utilizing single-cell RNA sequencing (scRNA-seq) data, we can take advantage of unprecedented opportunities to reconstruct gene regulatory networks (GRNs) at ultra-fine resolution, thereby uncovering intricate details of gene regulation. However, the current accuracy of using single-cell transcriptome data to infer gene regulatory networks needs to be improved. Therefore, in this article, we introduce the Transformer concept into the inference of gene regulatory networks. We propose a graph neural network model based on the Transformer architecture. The model combines GNN with Transformers to learn graph-structured data, enabling it to capture global within graph information. Compared with several existing methods, our model demonstrates superior performance across seven scRNA-seq datasets containing four types of ground truth networks. This facilitates the study of gene regulatory networks.
引用
收藏
页码:347 / 356
页数:10
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