Predicting MiRNA-Disease Associations Using Chebyshev Graph Convolution and Graph

Lihao Zhou; Zhengwei Li; Ru Nie; Lei Wang; Zhuhong You

doi:10.1007/978-981-95-0027-7_3

Predicting MiRNA-Disease Associations Using Chebyshev Graph Convolution and Graph

Lihao Zhou
, Zhengwei Li
, Ru Nie
, Lei Wang
, Zhuhong You

School of Computer Science

China University of Mining and Technology
Guangxi Academy of Science
Zaozhuang University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

MicroRNAs (miRNAs) are small non-coding RNAs involved in gene regulation and closely associated with various diseases, especially cancers. To resolve the issues inherent in experimental methods, we propose GCCDN, a computational model for predicting miRNA–disease associations. GCCDN integrates Chebyshev Graph Convolution and Graph Diffusion, using similarity-based graphs constructed from HMDD v2.0. Personalized PageRank enhances feature propagation, while ChebConv captures multi-hop information efficiently. Experiments on HMDD v2.0 and v3.2 show that GCCDN achieves AUCs of 94.28% and 95.04%, outperforming existing methods and demonstrating its potential for disease diagnosis and treatment discovery.

Original language	English
Title of host publication	Advanced Intelligent Computing Technology and Applications - 21st International Conference, ICIC 2025, Proceedings
Editors	De-Shuang Huang, Yijie Pan, Wei Chen, Bo Li
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	24-35
Number of pages	12
ISBN (Print)	9789819500260
DOIs	https://doi.org/10.1007/978-981-95-0027-7_3
State	Published - 2025
Event	21st International Conference on Intelligent Computing, ICIC 2025 - Ningbo, China Duration: 26 Jul 2025 → 29 Jul 2025

Publication series

Name	Lecture Notes in Computer Science
Volume	15866 LNBI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	21st International Conference on Intelligent Computing, ICIC 2025
Country/Territory	China
City	Ningbo
Period	26/07/25 → 29/07/25

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

R-Drop
attention mechanism
miRNA-disease graph

Access to Document

10.1007/978-981-95-0027-7_3

Cite this

Zhou, L., Li, Z., Nie, R., Wang, L., & You, Z. (2025). Predicting MiRNA-Disease Associations Using Chebyshev Graph Convolution and Graph. In D.-S. Huang, Y. Pan, W. Chen, & B. Li (Eds.), Advanced Intelligent Computing Technology and Applications - 21st International Conference, ICIC 2025, Proceedings (pp. 24-35). (Lecture Notes in Computer Science; Vol. 15866 LNBI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-95-0027-7_3

Zhou, Lihao ; Li, Zhengwei ; Nie, Ru et al. / Predicting MiRNA-Disease Associations Using Chebyshev Graph Convolution and Graph. Advanced Intelligent Computing Technology and Applications - 21st International Conference, ICIC 2025, Proceedings. editor / De-Shuang Huang ; Yijie Pan ; Wei Chen ; Bo Li. Springer Science and Business Media Deutschland GmbH, 2025. pp. 24-35 (Lecture Notes in Computer Science).

@inproceedings{95d63cad0f4c4919bc5637857161fdca,

title = "Predicting MiRNA-Disease Associations Using Chebyshev Graph Convolution and Graph",

abstract = "MicroRNAs (miRNAs) are small non-coding RNAs involved in gene regulation and closely associated with various diseases, especially cancers. To resolve the issues inherent in experimental methods, we propose GCCDN, a computational model for predicting miRNA–disease associations. GCCDN integrates Chebyshev Graph Convolution and Graph Diffusion, using similarity-based graphs constructed from HMDD v2.0. Personalized PageRank enhances feature propagation, while ChebConv captures multi-hop information efficiently. Experiments on HMDD v2.0 and v3.2 show that GCCDN achieves AUCs of 94.28\% and 95.04\%, outperforming existing methods and demonstrating its potential for disease diagnosis and treatment discovery.",

keywords = "R-Drop, attention mechanism, miRNA-disease graph",

author = "Lihao Zhou and Zhengwei Li and Ru Nie and Lei Wang and Zhuhong You",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.; 21st International Conference on Intelligent Computing, ICIC 2025 ; Conference date: 26-07-2025 Through 29-07-2025",

year = "2025",

doi = "10.1007/978-981-95-0027-7\_3",

language = "英语",

isbn = "9789819500260",

series = "Lecture Notes in Computer Science",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "24--35",

editor = "De-Shuang Huang and Yijie Pan and Wei Chen and Bo Li",

booktitle = "Advanced Intelligent Computing Technology and Applications - 21st International Conference, ICIC 2025, Proceedings",

}

Zhou, L, Li, Z, Nie, R, Wang, L & You, Z 2025, Predicting MiRNA-Disease Associations Using Chebyshev Graph Convolution and Graph. in D-S Huang, Y Pan, W Chen & B Li (eds), Advanced Intelligent Computing Technology and Applications - 21st International Conference, ICIC 2025, Proceedings. Lecture Notes in Computer Science, vol. 15866 LNBI, Springer Science and Business Media Deutschland GmbH, pp. 24-35, 21st International Conference on Intelligent Computing, ICIC 2025, Ningbo, China, 26/07/25. https://doi.org/10.1007/978-981-95-0027-7_3

Predicting MiRNA-Disease Associations Using Chebyshev Graph Convolution and Graph. / Zhou, Lihao; Li, Zhengwei; Nie, Ru et al.
Advanced Intelligent Computing Technology and Applications - 21st International Conference, ICIC 2025, Proceedings. ed. / De-Shuang Huang; Yijie Pan; Wei Chen; Bo Li. Springer Science and Business Media Deutschland GmbH, 2025. p. 24-35 (Lecture Notes in Computer Science; Vol. 15866 LNBI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Predicting MiRNA-Disease Associations Using Chebyshev Graph Convolution and Graph

AU - Zhou, Lihao

AU - Li, Zhengwei

AU - Nie, Ru

AU - Wang, Lei

AU - You, Zhuhong

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

PY - 2025

Y1 - 2025

N2 - MicroRNAs (miRNAs) are small non-coding RNAs involved in gene regulation and closely associated with various diseases, especially cancers. To resolve the issues inherent in experimental methods, we propose GCCDN, a computational model for predicting miRNA–disease associations. GCCDN integrates Chebyshev Graph Convolution and Graph Diffusion, using similarity-based graphs constructed from HMDD v2.0. Personalized PageRank enhances feature propagation, while ChebConv captures multi-hop information efficiently. Experiments on HMDD v2.0 and v3.2 show that GCCDN achieves AUCs of 94.28% and 95.04%, outperforming existing methods and demonstrating its potential for disease diagnosis and treatment discovery.

AB - MicroRNAs (miRNAs) are small non-coding RNAs involved in gene regulation and closely associated with various diseases, especially cancers. To resolve the issues inherent in experimental methods, we propose GCCDN, a computational model for predicting miRNA–disease associations. GCCDN integrates Chebyshev Graph Convolution and Graph Diffusion, using similarity-based graphs constructed from HMDD v2.0. Personalized PageRank enhances feature propagation, while ChebConv captures multi-hop information efficiently. Experiments on HMDD v2.0 and v3.2 show that GCCDN achieves AUCs of 94.28% and 95.04%, outperforming existing methods and demonstrating its potential for disease diagnosis and treatment discovery.

KW - R-Drop

KW - attention mechanism

KW - miRNA-disease graph

UR - https://www.scopus.com/pages/publications/105011828334

U2 - 10.1007/978-981-95-0027-7_3

DO - 10.1007/978-981-95-0027-7_3

M3 - 会议稿件

AN - SCOPUS:105011828334

SN - 9789819500260

T3 - Lecture Notes in Computer Science

SP - 24

EP - 35

BT - Advanced Intelligent Computing Technology and Applications - 21st International Conference, ICIC 2025, Proceedings

A2 - Huang, De-Shuang

A2 - Pan, Yijie

A2 - Chen, Wei

A2 - Li, Bo

PB - Springer Science and Business Media Deutschland GmbH

T2 - 21st International Conference on Intelligent Computing, ICIC 2025

Y2 - 26 July 2025 through 29 July 2025

ER -

Zhou L, Li Z, Nie R, Wang L, You Z. Predicting MiRNA-Disease Associations Using Chebyshev Graph Convolution and Graph. In Huang DS, Pan Y, Chen W, Li B, editors, Advanced Intelligent Computing Technology and Applications - 21st International Conference, ICIC 2025, Proceedings. Springer Science and Business Media Deutschland GmbH. 2025. p. 24-35. (Lecture Notes in Computer Science). doi: 10.1007/978-981-95-0027-7_3

Predicting MiRNA-Disease Associations Using Chebyshev Graph Convolution and Graph

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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