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Biomedical Interaction Prediction with Adaptive Line Graph Contrastive Learning

Author

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  • Shilin Sun

    (College of Information and Computer, Taiyuan University of Technology, Jinzhong 030600, China)

  • Hua Tian

    (College of Information and Computer, Taiyuan University of Technology, Jinzhong 030600, China)

  • Runze Wang

    (College of Information and Computer, Taiyuan University of Technology, Jinzhong 030600, China)

  • Zehua Zhang

    (College of Information and Computer, Taiyuan University of Technology, Jinzhong 030600, China)

Abstract

Biomedical interaction prediction is essential for the exploration of relationships between biomedical entities. Predicted biomedical interactions can help researchers with drug discovery, disease treatment, and more. In recent years, graph neural networks have taken advantage of their natural structure to achieve great progress in biomedical interaction prediction. However, most of them use node embedding instead of directly using edge embedding, resulting in information loss. Moreover, they predict links based on node similarity correlation assumptions, which have poor generalization. In addition, they do not consider the difference in topological information between negative and positive sample links, which limits their performance. Therefore, in this paper, we propose an adaptive line graph contrastive (ALGC) method to convert negative and positive sample links into two kinds of line graph nodes. By adjusting the number of intra-class line graph edges and inter-class line graph edges, an augmented line graph is generated and, finally, the information of the two views is balanced by contrastive learning. Through experiments on four public datasets, it is proved that the ALGC model outperforms the state-of-the-art methods.

Suggested Citation

  • Shilin Sun & Hua Tian & Runze Wang & Zehua Zhang, 2023. "Biomedical Interaction Prediction with Adaptive Line Graph Contrastive Learning," Mathematics, MDPI, vol. 11(3), pages 1-14, February.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:3:p:732-:d:1053672
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    References listed on IDEAS

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    1. Hang-Yuan Du & Wen-Jian Wang, 2022. "A Clustering Ensemble Framework with Integration of Data Characteristics and Structure Information: A Graph Neural Networks Approach," Mathematics, MDPI, vol. 10(11), pages 1-23, May.
    2. Chao Li & Qiming Yang & Bowen Pang & Tiance Chen & Qian Cheng & Jiaomin Liu, 2021. "A Mixed Strategy of Higher-Order Structure for Link Prediction Problem on Bipartite Graphs," Mathematics, MDPI, vol. 9(24), pages 1-13, December.
    3. Aaron Clauset & Cristopher Moore & M. E. J. Newman, 2008. "Hierarchical structure and the prediction of missing links in networks," Nature, Nature, vol. 453(7191), pages 98-101, May.
    4. Haji Gul & Feras Al-Obeidat & Adnan Amin & Fernando Moreira & Kaizhu Huang, 2022. "Hill Climbing-Based Efficient Model for Link Prediction in Undirected Graphs," Mathematics, MDPI, vol. 10(22), pages 1-15, November.
    5. Leo Katz, 1953. "A new status index derived from sociometric analysis," Psychometrika, Springer;The Psychometric Society, vol. 18(1), pages 39-43, March.
    6. Katja Luck & Dae-Kyum Kim & Luke Lambourne & Kerstin Spirohn & Bridget E. Begg & Wenting Bian & Ruth Brignall & Tiziana Cafarelli & Francisco J. Campos-Laborie & Benoit Charloteaux & Dongsic Choi & At, 2020. "A reference map of the human binary protein interactome," Nature, Nature, vol. 580(7803), pages 402-408, April.
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    Cited by:

    1. Dongdong An & Zongxu Pan & Qin Zhao & Wenyan Liu & Jing Liu, 2024. "Unsupervised Graph Structure Learning Based on Optimal Graph Topology Modeling and Adaptive Data Augmentation," Mathematics, MDPI, vol. 12(13), pages 1-22, June.

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