CircRNA-Disease Association Prediction based on Heterogeneous Graph Representation

Xinmeng Liu; Yuhe Zhang; Yewei Shen; Xuequn Shang; Yongtian Wang

doi:10.1109/BIBM55620.2022.9994988

CircRNA-Disease Association Prediction based on Heterogeneous Graph Representation

Xinmeng Liu, Yuhe Zhang, Yewei Shen, Xuequn Shang, Yongtian Wang

School of Computer Science

Northwestern Polytechnical University Xian

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

4 Scopus citations

Abstract

Circular RNAs (circRNAs) have important effects on various biological processes, and their dysfunction is closely related to the emergence and development of diseases. Identifying the associations between circRNAs and diseases is helpful in analyzing the pathogenesis of diseases. Therefore, it is necessary to develop effective computational methods for predicting circRNA-disease associations. Here, we present a computational model called HRCDA to predict associations between circRNA and disease based on heterogeneous graph representation. Firstly, an integrated network of circRNA functional similarity is built by Random Walk with Restart in the view of biological functions of circRNA. Then, a heterogeneous graph of circRNAs and diseases is constructed with known circRNA-disease associations. Finally, we design a heterogeneous graph representation learn model based on Graph Auto-Encoder (GAE) to predict circRNA-disease associations. Experiments have shown that the proposed method perform better than existing state-of-the-art methods and can be an effective tool to predict potential disease-related circRNAs.

Original language	English
Title of host publication	Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022
Editors	Donald Adjeroh, Qi Long, Xinghua Shi, Fei Guo, Xiaohua Hu, Srinivas Aluru, Giri Narasimhan, Jianxin Wang, Mingon Kang, Ananda M. Mondal, Jin Liu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2411-2417
Number of pages	7
ISBN (Electronic)	9781665468190
DOIs	https://doi.org/10.1109/BIBM55620.2022.9994988
State	Published - 2022
Event	2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022 - Las Vegas, United States Duration: 6 Dec 2022 → 8 Dec 2022

Publication series

Name	Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

Conference

Conference	2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022
Country/Territory	United States
City	Las Vegas
Period	6/12/22 → 8/12/22

Keywords

circRNA
circRNA-disease associations
GAE
heterogeneous graphs representation

UN SDGs

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

Access to Document

10.1109/BIBM55620.2022.9994988

Cite this

Liu, X., Zhang, Y., Shen, Y., Shang, X., & Wang, Y. (2022). CircRNA-Disease Association Prediction based on Heterogeneous Graph Representation. In D. Adjeroh, Q. Long, X. Shi, F. Guo, X. Hu, S. Aluru, G. Narasimhan, J. Wang, M. Kang, A. M. Mondal, & J. Liu (Eds.), Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022 (pp. 2411-2417). (Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM55620.2022.9994988

Liu, Xinmeng ; Zhang, Yuhe ; Shen, Yewei et al. / CircRNA-Disease Association Prediction based on Heterogeneous Graph Representation. Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022. editor / Donald Adjeroh ; Qi Long ; Xinghua Shi ; Fei Guo ; Xiaohua Hu ; Srinivas Aluru ; Giri Narasimhan ; Jianxin Wang ; Mingon Kang ; Ananda M. Mondal ; Jin Liu. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 2411-2417 (Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022).

@inproceedings{c504378b642a4359847100f1cecf4593,

title = "CircRNA-Disease Association Prediction based on Heterogeneous Graph Representation",

abstract = "Circular RNAs (circRNAs) have important effects on various biological processes, and their dysfunction is closely related to the emergence and development of diseases. Identifying the associations between circRNAs and diseases is helpful in analyzing the pathogenesis of diseases. Therefore, it is necessary to develop effective computational methods for predicting circRNA-disease associations. Here, we present a computational model called HRCDA to predict associations between circRNA and disease based on heterogeneous graph representation. Firstly, an integrated network of circRNA functional similarity is built by Random Walk with Restart in the view of biological functions of circRNA. Then, a heterogeneous graph of circRNAs and diseases is constructed with known circRNA-disease associations. Finally, we design a heterogeneous graph representation learn model based on Graph Auto-Encoder (GAE) to predict circRNA-disease associations. Experiments have shown that the proposed method perform better than existing state-of-the-art methods and can be an effective tool to predict potential disease-related circRNAs.",

keywords = "circRNA, circRNA-disease associations, GAE, heterogeneous graphs representation",

author = "Xinmeng Liu and Yuhe Zhang and Yewei Shen and Xuequn Shang and Yongtian Wang",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022 ; Conference date: 06-12-2022 Through 08-12-2022",

year = "2022",

doi = "10.1109/BIBM55620.2022.9994988",

language = "英语",

series = "Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "2411--2417",

editor = "Donald Adjeroh and Qi Long and Xinghua Shi and Fei Guo and Xiaohua Hu and Srinivas Aluru and Giri Narasimhan and Jianxin Wang and Mingon Kang and Mondal, {Ananda M.} and Jin Liu",

booktitle = "Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022",

}

Liu, X, Zhang, Y, Shen, Y, Shang, X & Wang, Y 2022, CircRNA-Disease Association Prediction based on Heterogeneous Graph Representation. in D Adjeroh, Q Long, X Shi, F Guo, X Hu, S Aluru, G Narasimhan, J Wang, M Kang, AM Mondal & J Liu (eds), Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022. Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022, Institute of Electrical and Electronics Engineers Inc., pp. 2411-2417, 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022, Las Vegas, United States, 6/12/22. https://doi.org/10.1109/BIBM55620.2022.9994988

CircRNA-Disease Association Prediction based on Heterogeneous Graph Representation. / Liu, Xinmeng; Zhang, Yuhe; Shen, Yewei et al.
Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022. ed. / Donald Adjeroh; Qi Long; Xinghua Shi; Fei Guo; Xiaohua Hu; Srinivas Aluru; Giri Narasimhan; Jianxin Wang; Mingon Kang; Ananda M. Mondal; Jin Liu. Institute of Electrical and Electronics Engineers Inc., 2022. p. 2411-2417 (Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022).

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

TY - GEN

T1 - CircRNA-Disease Association Prediction based on Heterogeneous Graph Representation

AU - Liu, Xinmeng

AU - Zhang, Yuhe

AU - Shen, Yewei

AU - Shang, Xuequn

AU - Wang, Yongtian

PY - 2022

Y1 - 2022

N2 - Circular RNAs (circRNAs) have important effects on various biological processes, and their dysfunction is closely related to the emergence and development of diseases. Identifying the associations between circRNAs and diseases is helpful in analyzing the pathogenesis of diseases. Therefore, it is necessary to develop effective computational methods for predicting circRNA-disease associations. Here, we present a computational model called HRCDA to predict associations between circRNA and disease based on heterogeneous graph representation. Firstly, an integrated network of circRNA functional similarity is built by Random Walk with Restart in the view of biological functions of circRNA. Then, a heterogeneous graph of circRNAs and diseases is constructed with known circRNA-disease associations. Finally, we design a heterogeneous graph representation learn model based on Graph Auto-Encoder (GAE) to predict circRNA-disease associations. Experiments have shown that the proposed method perform better than existing state-of-the-art methods and can be an effective tool to predict potential disease-related circRNAs.

AB - Circular RNAs (circRNAs) have important effects on various biological processes, and their dysfunction is closely related to the emergence and development of diseases. Identifying the associations between circRNAs and diseases is helpful in analyzing the pathogenesis of diseases. Therefore, it is necessary to develop effective computational methods for predicting circRNA-disease associations. Here, we present a computational model called HRCDA to predict associations between circRNA and disease based on heterogeneous graph representation. Firstly, an integrated network of circRNA functional similarity is built by Random Walk with Restart in the view of biological functions of circRNA. Then, a heterogeneous graph of circRNAs and diseases is constructed with known circRNA-disease associations. Finally, we design a heterogeneous graph representation learn model based on Graph Auto-Encoder (GAE) to predict circRNA-disease associations. Experiments have shown that the proposed method perform better than existing state-of-the-art methods and can be an effective tool to predict potential disease-related circRNAs.

KW - circRNA

KW - circRNA-disease associations

KW - GAE

KW - heterogeneous graphs representation

UR - http://www.scopus.com/inward/record.url?scp=85146659745&partnerID=8YFLogxK

U2 - 10.1109/BIBM55620.2022.9994988

DO - 10.1109/BIBM55620.2022.9994988

M3 - 会议稿件

AN - SCOPUS:85146659745

T3 - Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

SP - 2411

EP - 2417

BT - Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

A2 - Adjeroh, Donald

A2 - Long, Qi

A2 - Shi, Xinghua

A2 - Guo, Fei

A2 - Hu, Xiaohua

A2 - Aluru, Srinivas

A2 - Narasimhan, Giri

A2 - Wang, Jianxin

A2 - Kang, Mingon

A2 - Mondal, Ananda M.

A2 - Liu, Jin

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

Y2 - 6 December 2022 through 8 December 2022

ER -

Liu X, Zhang Y, Shen Y, Shang X, Wang Y. CircRNA-Disease Association Prediction based on Heterogeneous Graph Representation. In Adjeroh D, Long Q, Shi X, Guo F, Hu X, Aluru S, Narasimhan G, Wang J, Kang M, Mondal AM, Liu J, editors, Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 2411-2417. (Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022). doi: 10.1109/BIBM55620.2022.9994988

CircRNA-Disease Association Prediction based on Heterogeneous Graph Representation

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Keywords

UN SDGs

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