CircRNA-Disease Associations Prediction Based on Metapath2vec++ and Matrix Factorization
Circular RNA (circRNA) is a novel non-coding endogenous RNAs. Evidence has shown that circRNAs are related to many biological processes and play essential roles in different biological functions. Although increasing numbers of circRNAs are discovered using high-throughput sequencing technologies, th...
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| Language: | English |
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Tsinghua University Press
2020-12-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2020.9020025 |
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| author | Yuchen Zhang Xiujuan Lei Zengqiang Fang Yi Pan |
| author_facet | Yuchen Zhang Xiujuan Lei Zengqiang Fang Yi Pan |
| author_sort | Yuchen Zhang |
| collection | DOAJ |
| description | Circular RNA (circRNA) is a novel non-coding endogenous RNAs. Evidence has shown that circRNAs are related to many biological processes and play essential roles in different biological functions. Although increasing numbers of circRNAs are discovered using high-throughput sequencing technologies, these techniques are still time-consuming and costly. In this study, we propose a computational method to predict circRNA-disesae associations which is based on metapath2vec++ and matrix factorization with integrated multiple data (called PCD_MVMF). To construct more reliable networks, various aspects are considered. Firstly, circRNA annotation, sequence, and functional similarity networks are established, and disease-related genes and semantics are adopted to construct disease functional and semantic similarity networks. Secondly, metapath2vec++ is applied on an integrated heterogeneous network to learn the embedded features and initial prediction score. Finally, we use matrix factorization, take similarity as a constraint, and optimize it to obtain the final prediction results. Leave-one-out cross-validation, five-fold cross-validation, and f-measure are adopted to evaluate the performance of PCD_MVMF. These evaluation metrics verify that PCD_MVMF has better prediction performance than other methods. To further illustrate the performance of PCD_MVMF, case studies of common diseases are conducted. Therefore, PCD_MVMF can be regarded as a reliable and useful circRNA-disease association prediction tool. |
| format | Article |
| id | doaj-art-02fbea7cc3f3483e92516e51fd89c72e |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2020-12-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-02fbea7cc3f3483e92516e51fd89c72e2025-02-02T05:59:18ZengTsinghua University PressBig Data Mining and Analytics2096-06542020-12-013428029110.26599/BDMA.2020.9020025CircRNA-Disease Associations Prediction Based on Metapath2vec++ and Matrix FactorizationYuchen Zhang0Xiujuan Lei1Zengqiang Fang2Yi Pan3<institution>School of Computer Science, Shaanxi Normal University</institution>, <city>Xi’an</city> <postal-code>710119</postal-code>, <country>China</country><institution>School of Computer Science, Shaanxi Normal University</institution>, <city>Xi’an</city> <postal-code>710119</postal-code>, <country>China</country><institution>School of Computer Science, Shaanxi Normal University</institution>, <city>Xi’an</city> <postal-code>710119</postal-code>, <country>China</country><institution content-type="dept">Department of Computer Science</institution>, <institution>Georgia State University</institution>, <city>Atlanta</city>, <state>GA</state> <postal-code>30302</postal-code>, <country>USA</country>Circular RNA (circRNA) is a novel non-coding endogenous RNAs. Evidence has shown that circRNAs are related to many biological processes and play essential roles in different biological functions. Although increasing numbers of circRNAs are discovered using high-throughput sequencing technologies, these techniques are still time-consuming and costly. In this study, we propose a computational method to predict circRNA-disesae associations which is based on metapath2vec++ and matrix factorization with integrated multiple data (called PCD_MVMF). To construct more reliable networks, various aspects are considered. Firstly, circRNA annotation, sequence, and functional similarity networks are established, and disease-related genes and semantics are adopted to construct disease functional and semantic similarity networks. Secondly, metapath2vec++ is applied on an integrated heterogeneous network to learn the embedded features and initial prediction score. Finally, we use matrix factorization, take similarity as a constraint, and optimize it to obtain the final prediction results. Leave-one-out cross-validation, five-fold cross-validation, and f-measure are adopted to evaluate the performance of PCD_MVMF. These evaluation metrics verify that PCD_MVMF has better prediction performance than other methods. To further illustrate the performance of PCD_MVMF, case studies of common diseases are conducted. Therefore, PCD_MVMF can be regarded as a reliable and useful circRNA-disease association prediction tool.https://www.sciopen.com/article/10.26599/BDMA.2020.9020025circular rnas (circrnas)circrna-disease associationsmatepath2vec++matrix factorization |
| spellingShingle | Yuchen Zhang Xiujuan Lei Zengqiang Fang Yi Pan CircRNA-Disease Associations Prediction Based on Metapath2vec++ and Matrix Factorization Big Data Mining and Analytics circular rnas (circrnas) circrna-disease associations matepath2vec++ matrix factorization |
| title | CircRNA-Disease Associations Prediction Based on Metapath2vec++ and Matrix Factorization |
| title_full | CircRNA-Disease Associations Prediction Based on Metapath2vec++ and Matrix Factorization |
| title_fullStr | CircRNA-Disease Associations Prediction Based on Metapath2vec++ and Matrix Factorization |
| title_full_unstemmed | CircRNA-Disease Associations Prediction Based on Metapath2vec++ and Matrix Factorization |
| title_short | CircRNA-Disease Associations Prediction Based on Metapath2vec++ and Matrix Factorization |
| title_sort | circrna disease associations prediction based on metapath2vec and matrix factorization |
| topic | circular rnas (circrnas) circrna-disease associations matepath2vec++ matrix factorization |
| url | https://www.sciopen.com/article/10.26599/BDMA.2020.9020025 |
| work_keys_str_mv | AT yuchenzhang circrnadiseaseassociationspredictionbasedonmetapath2vecandmatrixfactorization AT xiujuanlei circrnadiseaseassociationspredictionbasedonmetapath2vecandmatrixfactorization AT zengqiangfang circrnadiseaseassociationspredictionbasedonmetapath2vecandmatrixfactorization AT yipan circrnadiseaseassociationspredictionbasedonmetapath2vecandmatrixfactorization |