摘要
基因组测序技术的快速发展使得生物数据库中的基因和基因组序列数据数量迅速增加,但其中仍有大量基因功能是未知的。为此,提出基于异质网络层次注意力机制的基因节点表示学习方法HAGE,用以预测基因功能。结合多种来源的数据集,构建一个具有节点属性的基因功能相关异质网络,在网络中使用层次注意力机制为每一个基因节点学习一个节点嵌入向量,该向量可用于后续的基因功能预测等任务。实验结果表明,与GraphSAGE和GAT等方法相比,HAGE具有更好的预测性能。
The rapid development of genome sequencing has led to the explosive growth of gene and genomic sequence data in biological databases,in which functions of a large number of genes still remain unknown.Therefore,this paper proposes a gene node representation learning method,HAGE,based on hierarchical attention mechanism in heterogeneous network to predict the function of genes.Firstly,a gene function-related heterogeneous network with node attributes is constructed.Then the hierarchical attention mechanism is used in network to enable each gene node to learn a node embedding vector,which can be used for subsequent tasks such as gene function prediction.Experimental results show that the proposed method has better performance than GraphSAGE,GAT and other methods.
作者
万美含
熊贇
朱扬勇
WAN Meihan;XIONG Yun;ZHU Yangyong(School of Computer Science and Technology,Fudan University,Shanghai 200433,China;Shanghai Key Laboratory of Data Science,Shanghai 200433,China;Shanghai Institute of Advanced Communications and Data Science,Shanghai 200433,China)
出处
《计算机工程》
CAS
CSCD
北大核心
2020年第7期43-49,共7页
Computer Engineering
基金
国家自然科学基金(U1636207,91546105)
上海市科技发展基金(16JC1400801)。
关键词
基因功能预测
异质信息网络
注意力机制
网络表示学习
网络嵌入
gene function prediction
heterogeneous information network
attention mechanism
network representation learning
network embedding
