IGNSCDA : Predicting CircRNA-Disease Associations Based on Improved Graph Convolutional Network and Negative Sampling
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:19 |
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| Enthalten in: |
IEEE/ACM transactions on computational biology and bioinformatics - 19(2022), 6 vom: 31. Nov., Seite 3530-3538 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Lan, Wei [VerfasserIn] |
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| Links: |
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| Themen: |
Journal Article |
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| Anmerkungen: |
Date Completed 06.04.2023 Date Revised 20.04.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1109/TCBB.2021.3111607 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM33048513X |
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| 100 | 1 | |a Lan, Wei |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a IGNSCDA |b Predicting CircRNA-Disease Associations Based on Improved Graph Convolutional Network and Negative Sampling |
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| 500 | |a Date Revised 20.04.2023 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 7 | |a RNA, Circular |2 NLM | |
| 700 | 1 | |a Dong, Yi |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Qingfeng |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Jin |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Jianxin |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Yi-Ping Phoebe |e verfasserin |4 aut | |
| 700 | 1 | |a Pan, Shirui |e verfasserin |4 aut | |
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