BioERP A biomedical heterogeneous network-based self-supervised representation learning approach for entity relationship predictions

Introduction

BioERP is a biomedical heterogeneous network-based self-supervised representation learning approach for entity relationship predictions. A self-supervised meta path detection mechanism is proposed to train a deep Transformer encoder model that can capture the global structure and semantic feature in BioHNs. Meanwhile, a biomedical entity mask learning strategy is designed to reflect local associations of vertices. Finally, the representations from different task models are concatenated to generate two-level representation vectors for predicting relationship among entities. The results on eight datasets show BioERP outperforms 30 state-of-the-art methods. In particular, BioERP reveals great performance with results close to 1 in terms of AUC and AUPR on the drug-target interaction predictions. In summary, BioERP is a promising bio-entity relationship prediction approach.

Publications

  1. BioERP: biomedical heterogeneous network-based self-supervised representation learning approach for entity relationship predictions
    Cite this
    Xiaoqi Wang, Yaning Yang, Kenli Li, Wentao Li, Fei Li, Shaoliang Peng, - Bioinformatics

Credits

  1. Xiaoqi Wang xqw@hnu.edu.cn
    InvestigatorDeveloper

    College of Computer Science and Electronic Engineering, Hunan University, China

  2. Shaoliang Peng slpeng@hnu.edu.cn
    InvestigatorDeveloper

    College of Computer Science and Electronic Engineering, Hunan University, China

  3. Fei Li pittacus@gmail.com
    InvestigatorDeveloper

    Computer Network Information Center, Chinese Academy of Sciences, Beijing, China

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Summary
AccessionBT007163
Tool TypeApplication
CategoryData integration, Biological network reconstruction, Network analysis, Drug targets, Drug repositioning
PlatformsLinux/Unix
TechnologiesPython3
User InterfaceTerminal Command Line
Latest Release1.0 (May 31, 2021)
Download Count765
Country/RegionChina
Submitted ByShaoliang Peng
Fundings

2018YFC0910400