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Neural Processing Letters
Erschienen in: Neural Processing Letters 1/2023

20.02.2021

A Hybrid VAE Based Network Embedding Method for Biomedical Relation Mining

verfasst von: Tian Bai, Ying Li, Ye Wang, Lan Huang

Erschienen in: Neural Processing Letters | Ausgabe 1/2023

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Abstract

Mining biomedical entity association and extracting the implicit knowledge from biomedical entity relation networks are important for precision medicine. In this paper, we propose a novel method for implicit relation mining from biomedical multi-entity network. In the embedding part, we combine two kinds of model (1) the graph representation learning model like GraphGAN and (2) the network embedding model like VAE based SDNE, to construct a hybrid model GVS. In the prediction part, the positive samples selected from original network and the negative samples generated by ranking meta-paths are used to train kNN. To evaluate the performances of GVS, we compare the proposed method with three state-of-the-art methods (Katz, Catapult and IMC) on benchmark datasets. Moreover, we evaluate GVS on a real biomedical entity relation network, it shows advantages compared with other network embedding methods and successfully mines implicit relationships which validated by PubMed.
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Metadaten
Titel
A Hybrid VAE Based Network Embedding Method for Biomedical Relation Mining
verfasst von
Tian Bai
Ying Li
Ye Wang
Lan Huang
Publikationsdatum
20.02.2021
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 1/2023
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-021-10454-5

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