English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Representation learning provides new and powerful graph analytical approachesand tools for the highly valued data science challenge of mining knowledgegraphs. Since previous graph analytical methods have mostly focused onhomogeneous graphs, an important current challenge is extending thismethodology for richly heterogeneous graphs and knowledge domains. Thebiomedical sciences are such a domain, reflecting the complexity of biology,with entities such as genes, proteins, drugs, diseases, and phenotypes, andrelationships such as gene co-expression, biochemical regulation, andbiomolecular inhibition or activation. Therefore, the semantics of edges andnodes are critical for representation learning and knowledge discovery in realworld biomedical problems. In this paper, we propose the edge2vec model, whichrepresents graphs considering edge semantics. An edge-type transition matrix istrained by an Expectation-Maximization approach, and a stochastic gradientdescent model is employed to learn node embedding on a heterogeneous graph viathe trained transition matrix. edge2vec is validated on three biomedical domaintasks: biomedical entity classification, compound-gene bioactivity prediction,and biomedical information retrieval. Results show that by consideringedge-types into node embedding learning in heterogeneous graphs,\textbf{edge2vec}\ significantly outperforms state-of-the-art models on allthree tasks. We propose this method for its added value relative to existinggraph analytical methodology, and in the real world context of biomedicalknowledge discovery applicability.

edge2vec: Representation learning using edge semantics for biomedical knowledge discovery