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

20-02-2021

A Hybrid VAE Based Network Embedding Method for Biomedical Relation Mining

Authors: Tian Bai, Ying Li, Ye Wang, Lan Huang

Published in: Neural Processing Letters | Issue 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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Literature
1.
Janani S, Ramyachitra D, Ranjani RR (2018) PCD-DPPI: protein complex detection from dynamic PPI using shuffled frog-leaping algorithm. Gene Rep 12:89–98CrossRef
2.
SabziNezhad A, Jalili S (2020) DPCT: a dynamic method for detecting protein complexes from TAP-aware weighted PPI network. Front Genet 11:567CrossRef
3.
Kim S, Liu H, Yeganova L, Wilbur WJ (2015) Extracting drug–drug interactions from literature using a rich feature-based linear kernel approach. J Biomed Inform 55:23–30CrossRef
4.
Wang W, Yang X, Yang C, Guo X, Zhang X, Wu C (2017) Dependency-based long short term memory network for drug–drug interaction extraction. BMC Bioinform 18(Suppl 16):578CrossRef
5.
Le DH, Tran TTH (2018) autoHGPE: automated prediction of novel disease-gene and disease-disease associations and evidence collection based on a random walk on heterogeneous network. F1000 Res 7(658):658CrossRef
6.
Ding P, Luo J, Liang C, Xiao Q, Cao B (2018) Human disease MiRNA inference by combining target information based on heterogeneous manifolds. J Biomed Inform 80:26–36CrossRef
7.
Huang L, Wang Y, Wang Y, Bai T (2016) Gene-disease interaction retrieval from multiple sources: a network based method. Biomed Res Int 2016:1–9CrossRef
8.
Xiao Q, Luo P, Li M, Wang J, Wu FX (2019) A novel core-attachment based method to identify dynamic protein complexes based on gene expression profiles and PPI networks. Proteomics 19(5):1800129CrossRef
9.
Bai T, Gong L, Wang Y, Wang Y, Kulikowski CA (2016) A method for exploring implicit concept relatedness in biomedical knowledge network. BMC Bioinform 17(9):53–66
10.
Yu F, Yang Z, Hu X, Sun Y, Hong F, Wang J (2015) Protein complex detection in PPI networks based on data integration and supervised learning method. BMC Bioinform 16(Suppl 12):S3CrossRef
11.
Quan C, Lei H, Sun X, Bai W (2016) Multichannel convolutional neural network for biological relation extraction. Biomed Res Int 2016:1–10
12.
13.
Goyal P, Ferrara E (2018) Graph embedding techniques, applications, and performance: a survey. Knowl Based Syst 151:78–94CrossRef
14.
Liu X, Yang Z, Sang S, Zhou Z, Wang L, Zhang Y et al (2018) Identifying protein complexes based on node embeddings obtained from protein-protein interaction networks. BMC Bioinform 19(1):332CrossRef
15.
16.
Yang K, Wang N, Liu G et al (2018) Heterogeneous network embedding for identifying symptom candidate genes. J Am Med Inform Assoc (JAMIA) 25(11):1452–1459CrossRef
17.
Wang H, Wang J, Wang J, Zhao M, Zhang W, Zhang F et al (2018) GraphGAN: graph representation learning with generative adversarial nets. IEEE Trans Knowl Data Eng
18.
Wang D, Cui P, Zhu W (2016) Structural deep network embedding. In: Proceedings of the 22nd ACM SIGKDD international conference. ACM, pp 1225–1234
19.
Kingma DP, Welling M (2013) Auto-encoding variational bayes
20.
21.
Sun Y, Han J, Yan X et al (2011) PathSim: meta path-based Top-K similarity search in heterogeneous information networks. Proc VLDB Endow 4(11):992–1003CrossRef
22.
Nagarajan N, Dhillon IS (2014) Inductive matrix completion for predicting gene–disease associations. Bioinformatics 30(12):i60–i68CrossRef
23.
Bai T, Ge Y, Yang C et al (2019) BERST: an engine and tool for exploring biomedical entities and relationships. Chin J Electron 28(4):797–804CrossRef
24.
Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323–2326CrossRef
25.
Perozzi B, Al-Rfou R, Skiena S (2014) DeepWalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, pp 701–710
26.
Zhuge B, Li G (2017) MiR-150 deficiency ameliorated hepatosteatosis and insulin resistance in nonalcoholic fatty liver disease via targeting casp8 and fadd-like apoptosis regulator. Biochem Biophys Res Commun 494(3–4):687–692CrossRef
27.
Jagannathan L, Jose CC, Tanwar VS et al (2017) Identification of a unique gene expression signature in mercury and 2, 3, 7, 8-tetrachlorodibenzo-p-dioxin co-exposed cells. Toxicol Res 6(3):312–323CrossRef
Metadata
Title
A Hybrid VAE Based Network Embedding Method for Biomedical Relation Mining
Authors
Tian Bai
Ying Li
Ye Wang
Lan Huang
Publication date
20-02-2021
Publisher
Springer US
Published in
Neural Processing Letters / Issue 1/2023
Print ISSN: 1370-4621
Electronic ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-021-10454-5

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