IGNSCDA: Predicting CircRNA-Disease Associations Based on Improved Graph Convolutional Network and Negative Sampling

Wei Lan; Yi Dong; Qingfeng ChenJin LiuJianxin WangYi-Ping Phoebe ChenShirui Pan

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IGNSCDA: Predicting CircRNA-Disease Associations Based on Improved Graph Convolutional Network and Negative Sampling

机译：IGNSCDA: Predicting CircRNA-Disease Associations Based on Improved Graph Convolutional Network and Negative Sampling

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Accumulating evidences have shown that circRNA plays an important role in human diseases. It can be used as potential biomarker for diagnose and treatment of disease. Although some computational methods have been proposed to predict circRNA-disease associations, the performance still need to be improved. In this paper, we propose a new computational model based on Improved Graph convolutional network and Negative Sampling to predict CircRNA-Disease Associations. In our method, it constructs the heterogeneous network based on known circRNA-disease associations. Then, an improved graph convolutional network is designed to obtain the feature vectors of circRNA and disease. Further, the multi-layer perceptron is employed to predict circRNA-disease associations based on the feature vectors of circRNA and disease. In addition, the negative sampling method is employed to reduce the effect of the noise samples, which selects negative samples based on circRNA's expression profile similarity and Gaussian Interaction Profile kernel similarity. The 5-fold cross validation is utilized to evaluate the performance of the method. The results show that IGNSCDA outperforms than other state-of-the-art methods in the prediction performance. Moreover, the case study shows that IGNSCDA is an effective tool for predicting potential circRNA-disease associations.

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《IEEE/ACM transactions on computational biology and bioinformatics》 |2022年第6期|3530-3538|共9页
作者
Wei Lan; Yi Dong; Qingfeng ChenJin LiuJianxin WangYi-Ping Phoebe ChenShirui Pan;
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作者单位

School of Computer, Electronic and Information, Guangxi University, Nanning, Guangxi, China;

Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan, China;

Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC, AustraliaDepartment of Data Science and AI, Faculty of Information Technology, Monash University, Melbourne, VIC, Australia;

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原文格式 PDF
正文语种英语
中图分类生物信息论;
关键词
Diseases; Heterogeneous networks; Feature extraction; Computational modeling; Kernel; Predictive models; Data mining;

IGNSCDA: Predicting CircRNA-Disease Associations Based on Improved Graph Convolutional Network and Negative Sampling

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