Predicting Microbe-Disease Association Based on Heterogeneous Network and Global Graph Feature Learning

WANG Yueyue; LEI Xiujuan; PAN Yi

doi:10.1049/cje.2020.00.212

Volume 31 Issue 2

Mar. 2022

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WANG Yueyue, LEI Xiujuan, PAN Yi, “Predicting Microbe-Disease Association Based on Heterogeneous Network and Global Graph Feature Learning,” Chinese Journal of Electronics, vol. 31, no. 2, pp. 345-353, 2022, doi: 10.1049/cje.2020.00.212

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WANG Yueyue, LEI Xiujuan, PAN Yi, “Predicting Microbe-Disease Association Based on Heterogeneous Network and Global Graph Feature Learning,” Chinese Journal of Electronics, vol. 31, no. 2, pp. 345-353, 2022, doi: 10.1049/cje.2020.00.212

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Predicting Microbe-Disease Association Based on Heterogeneous Network and Global Graph Feature Learning

doi: 10.1049/cje.2020.00.212

WANG Yueyue^1
,,
LEI Xiujuan^1
,,
PAN Yi^{2, 3
,}

1.
School of Computer Science, Shaanxi Normal University, Xi’an 710119, China
2.
Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA
3.
Faculty of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China

Funds: This work was supported by the National Natural Science Foundation of China (61672334, 61972451, 61902230) and the Fundamental Research Funds for the Central Universities, Shaanxi Normal University (GK201901010)

More Information

Author Bio:
is currently working toward M.S. degree in Shaanxi Normal University, Xi’an, China. Her current research interests include bioinformatics, data mining and deep learning. (Email: yueyuewang@snnu.edu.cn)

(corresponding author) is a Professor in the School of Computer Science at Shaanxi Normal University, Xi’an, China. She received the M.S. and Ph.D. degrees from Northwestern Polytechnical University, Xi’an, China, in 2001 and 2005, respectively. Her current research interests mainly include intelligent computing and bioinformatics. (Email: xjlei@snnu.edu.cn)

is currently a Professor of the Faculty of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences. He has served as Chair of Computer Science Department at Georgia State University during 2005-2020. He received the B.E. and M.E. degrees in computer engineering from Tsinghua University, China, in 1982 and 1984, respectively, and the Ph.D. degree in computer science from the University of Pittsburgh, USA, in 1991. His current research interests mainly include bioinformatics and health informatics using big data analytics, cloud computing, and machine learning technologies. (Email: yipan@gsu.edu)
Received Date: 2020-07-16
Accepted Date: 2021-08-03

Available Online: 2021-10-08

Publish Date: 2022-03-05

Abstract

Abstract

Numerous microbes inhabit human body, making a vast difference in human health. Hence, discovering associations between microbes and diseases is beneficial to disease prevention and treatment. In this study, we develop a prediction method by learning global graph feature on the heterogeneous network (called HNGFL). Firstly, a heterogeneous network is integrated by known microbe-disease associations and multiple similarities. Based on microbe Gaussian interaction profile (GIP) kernel similarity, we consider different effects of these microbes on organs in the human body to further improve microbe similarity. For disease similarity network, we combine GIP kernel similarity, disease semantic similarity and disease-symptom similarity. And then, an embedding algorithm called GraRep is used to learn global structural information for this network. According to vector feature of every node, we utilize support vector machine classifier to calculate the score for each microbe-disease pair. HNGFL achieves a reliable performance in cross validation, outperforming the compared methods. In addition, we carry out case studies of three diseases. Results show that HNGFL can be considered as a reliable method for microbe-disease association prediction.
- Microbe-disease association,
- Heterogeneous network,
- Global graph feature learning,
- GraRep

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References(59)

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