edge2vec: Representation learning using edge semantics for biomedical knowledge discovery

BMC Bioinformatics

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[1] 0000 0001 0266 8918, grid.412017.1, University of South China, Hengyang, Hunan, China;0000 0001 2181 3404, grid.419815.0, Microsoft Corporation, Seattle, Washington, USA;0000 0004 1798 4018, grid.263452.4, School of Management, Shanxi Medical University, Taiyuan, Shanxi, China;Data2Discovery, Inc., Bloomington, IN, USA;School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA;School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA;0000 0001 2181 3404, grid.419815.0, Microsoft Corporation, Seattle, Washington, USA;0000 0001 2188 8502, grid.266832.b, School of Medicine, University of New Mexico, Albuquerque, NM, USA;School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA;Data2Discovery, Inc., Bloomington, IN, USA;

关键词: Knowledge graph;    Heterogeneous network;    Biomedical knowledge discovery;    Representation learning;    Graph embedding;    Node embedding;    Edge semantics;    Applied machine learning;    Data science;    Linked data;    Semantic web;    Network science;    Systems biology;   

DOI  :  10.1186/s12859-019-2914-2

来源: publisher

【 摘 要 】

BackgroundRepresentation learning provides new and powerful graph analytical approaches and tools for the highly valued data science challenge of mining knowledge graphs. Since previous graph analytical methods have mostly focused on homogeneous graphs, an important current challenge is extending this methodology for richly heterogeneous graphs and knowledge domains. The biomedical sciences are such a domain, reflecting the complexity of biology, with entities such as genes, proteins, drugs, diseases, and phenotypes, and relationships such as gene co-expression, biochemical regulation, and biomolecular inhibition or activation. Therefore, the semantics of edges and nodes are critical for representation learning and knowledge discovery in real world biomedical problems.ResultsIn this paper, we propose the edge2vec model, which represents graphs considering edge semantics. An edge-type transition matrix is trained by an Expectation-Maximization approach, and a stochastic gradient descent model is employed to learn node embedding on a heterogeneous graph via the trained transition matrix. edge2vec is validated on three biomedical domain tasks: biomedical entity classification, compound-gene bioactivity prediction, and biomedical information retrieval. Results show that by considering edge-types into node embedding learning in heterogeneous graphs, edge2vec significantly outperforms state-of-the-art models on all three tasks.ConclusionsWe propose this method for its added value relative to existing graph analytical methodology, and in the real world context of biomedical knowledge discovery applicability.

【 授权许可】

CC BY