Identifying circRNA-miRNA interaction based on multi-biological interaction fusion
CircRNA is a new type of non-coding RNA with a closed loop structure. More and more biological experiments show that circRNA plays important roles in many diseases by regulating the target genes of miRNA. Therefore, correct identification of the potential interaction between circRNA and miRNA not on...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-12-01
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| Series: | Frontiers in Microbiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmicb.2022.987930/full |
| _version_ | 1797979314437750784 |
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| author | Dunwei Yao Dunwei Yao Lidan Nong Minzhen Qin Minzhen Qin Shengbin Wu Shengbin Wu Shunhan Yao |
| author_facet | Dunwei Yao Dunwei Yao Lidan Nong Minzhen Qin Minzhen Qin Shengbin Wu Shengbin Wu Shunhan Yao |
| author_sort | Dunwei Yao |
| collection | DOAJ |
| description | CircRNA is a new type of non-coding RNA with a closed loop structure. More and more biological experiments show that circRNA plays important roles in many diseases by regulating the target genes of miRNA. Therefore, correct identification of the potential interaction between circRNA and miRNA not only helps to understand the mechanism of the disease, but also contributes to the diagnosis, treatment, and prognosis of the disease. In this study, we propose a model (IIMCCMA) by using network embedding and matrix completion to predict the potential interaction of circRNA-miRNA. Firstly, the corresponding adjacency matrix is constructed based on the experimentally verified circRNA-miRNA interaction, circRNA-cancer interaction, and miRNA-cancer interaction. Then, the Gaussian kernel function and the cosine function are used to calculate the circRNA Gaussian interaction profile kernel similarity, circRNA functional similarity, miRNA Gaussian interaction profile kernel similarity, and miRNA functional similarity. In order to reduce the influence of noise and redundant information in known interactions, this model uses network embedding to extract the potential feature vectors of circRNA and miRNA, respectively. Finally, an improved inductive matrix completion algorithm based on the feature vectors of circRNA and miRNA is used to identify potential interactions between circRNAs and miRNAs. The 10-fold cross-validation experiment is utilized to prove the predictive ability of the IIMCCMA. The experimental results show that the AUC value and AUPR value of the IIMCCMA model are higher than other state-of-the-art algorithms. In addition, case studies show that the IIMCCMA model can correctly identify the potential interactions between circRNAs and miRNAs. |
| first_indexed | 2024-04-11T05:37:04Z |
| format | Article |
| id | doaj.art-871f3e986ed14912a8276e31e5f4c1ec |
| institution | Directory Open Access Journal |
| issn | 1664-302X |
| language | English |
| last_indexed | 2024-04-11T05:37:04Z |
| publishDate | 2022-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Microbiology |
| spelling | doaj.art-871f3e986ed14912a8276e31e5f4c1ec2022-12-22T12:02:56ZengFrontiers Media S.A.Frontiers in Microbiology1664-302X2022-12-011310.3389/fmicb.2022.987930987930Identifying circRNA-miRNA interaction based on multi-biological interaction fusionDunwei Yao0Dunwei Yao1Lidan Nong2Minzhen Qin3Minzhen Qin4Shengbin Wu5Shengbin Wu6Shunhan Yao7Department of Gastroenterology, The People’s Hospital of Baise, Baise, ChinaThe Southwest Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, ChinaDepartment of Child Healthcare, Baise Maternal and Child Hospital, Baise, ChinaDepartment of Gastroenterology, The People’s Hospital of Baise, Baise, ChinaThe Southwest Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, ChinaThe Southwest Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, ChinaDepartment of Pulmonary and Critical Care Medicine, The People's Hospital of Baise, Baise, ChinaMedical College of Guangxi University, Nanning, ChinaCircRNA is a new type of non-coding RNA with a closed loop structure. More and more biological experiments show that circRNA plays important roles in many diseases by regulating the target genes of miRNA. Therefore, correct identification of the potential interaction between circRNA and miRNA not only helps to understand the mechanism of the disease, but also contributes to the diagnosis, treatment, and prognosis of the disease. In this study, we propose a model (IIMCCMA) by using network embedding and matrix completion to predict the potential interaction of circRNA-miRNA. Firstly, the corresponding adjacency matrix is constructed based on the experimentally verified circRNA-miRNA interaction, circRNA-cancer interaction, and miRNA-cancer interaction. Then, the Gaussian kernel function and the cosine function are used to calculate the circRNA Gaussian interaction profile kernel similarity, circRNA functional similarity, miRNA Gaussian interaction profile kernel similarity, and miRNA functional similarity. In order to reduce the influence of noise and redundant information in known interactions, this model uses network embedding to extract the potential feature vectors of circRNA and miRNA, respectively. Finally, an improved inductive matrix completion algorithm based on the feature vectors of circRNA and miRNA is used to identify potential interactions between circRNAs and miRNAs. The 10-fold cross-validation experiment is utilized to prove the predictive ability of the IIMCCMA. The experimental results show that the AUC value and AUPR value of the IIMCCMA model are higher than other state-of-the-art algorithms. In addition, case studies show that the IIMCCMA model can correctly identify the potential interactions between circRNAs and miRNAs.https://www.frontiersin.org/articles/10.3389/fmicb.2022.987930/fullcircRNA-miRNA interactionmulti-biological interaction fusioninductive matrix completionnetwork embeddingcomputational method |
| spellingShingle | Dunwei Yao Dunwei Yao Lidan Nong Minzhen Qin Minzhen Qin Shengbin Wu Shengbin Wu Shunhan Yao Identifying circRNA-miRNA interaction based on multi-biological interaction fusion Frontiers in Microbiology circRNA-miRNA interaction multi-biological interaction fusion inductive matrix completion network embedding computational method |
| title | Identifying circRNA-miRNA interaction based on multi-biological interaction fusion |
| title_full | Identifying circRNA-miRNA interaction based on multi-biological interaction fusion |
| title_fullStr | Identifying circRNA-miRNA interaction based on multi-biological interaction fusion |
| title_full_unstemmed | Identifying circRNA-miRNA interaction based on multi-biological interaction fusion |
| title_short | Identifying circRNA-miRNA interaction based on multi-biological interaction fusion |
| title_sort | identifying circrna mirna interaction based on multi biological interaction fusion |
| topic | circRNA-miRNA interaction multi-biological interaction fusion inductive matrix completion network embedding computational method |
| url | https://www.frontiersin.org/articles/10.3389/fmicb.2022.987930/full |
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