WBSMDA: Within and Between Score for MiRNA-Disease Association prediction
Increasing evidences have indicated that microRNAs (miRNAs) are functionally associated with the development and progression of various complex human diseases. However, the roles of miRNAs in multiple biological processes or various diseases and their underlying molecular mechanisms still have not b...
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| Vydané v: | Scientific reports Ročník 6; číslo 1; s. 21106 |
|---|---|
| Hlavní autori: | , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
London
Nature Publishing Group UK
16.02.2016
Nature Publishing Group |
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| ISSN: | 2045-2322, 2045-2322 |
| On-line prístup: | Získať plný text |
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| Abstract | Increasing evidences have indicated that microRNAs (miRNAs) are functionally associated with the development and progression of various complex human diseases. However, the roles of miRNAs in multiple biological processes or various diseases and their underlying molecular mechanisms still have not been fully understood yet. Predicting potential miRNA-disease associations by integrating various heterogeneous biological datasets is of great significance to the biomedical research. Computational methods could obtain potential miRNA-disease associations in a short time, which significantly reduce the experimental time and cost. Considering the limitations in previous computational methods, we developed the model of Within and Between Score for MiRNA-Disease Association prediction (WBSMDA) to predict potential miRNAs associated with various complex diseases. WBSMDA could be applied to the diseases without any known related miRNAs. The AUC of 0.8031 based on Leave-one-out cross validation has demonstrated its reliable performance. WBSMDA was further applied to Colon Neoplasms, Prostate Neoplasms and Lymphoma for the identification of their potential related miRNAs. As a result, 90%, 84% and 80% of predicted miRNA-disease pairs in the top 50 prediction list for these three diseases have been confirmed by recent experimental literatures, respectively. It is anticipated that WBSMDA would be a useful resource for potential miRNA-disease association identification. |
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| AbstractList | Increasing evidences have indicated that microRNAs (miRNAs) are functionally associated with the development and progression of various complex human diseases. However, the roles of miRNAs in multiple biological processes or various diseases and their underlying molecular mechanisms still have not been fully understood yet. Predicting potential miRNA-disease associations by integrating various heterogeneous biological datasets is of great significance to the biomedical research. Computational methods could obtain potential miRNA-disease associations in a short time, which significantly reduce the experimental time and cost. Considering the limitations in previous computational methods, we developed the model of Within and Between Score for MiRNA-Disease Association prediction (WBSMDA) to predict potential miRNAs associated with various complex diseases. WBSMDA could be applied to the diseases without any known related miRNAs. The AUC of 0.8031 based on Leave-one-out cross validation has demonstrated its reliable performance. WBSMDA was further applied to Colon Neoplasms, Prostate Neoplasms, and Lymphoma for the identification of their potential related miRNAs. As a result, 90%, 84%, and 80% of predicted miRNA-disease pairs in the top 50 prediction list for these three diseases have been confirmed by recent experimental literatures, respectively. It is anticipated that WBSMDA would be a useful resource for potential miRNA-disease association identification. Increasing evidences have indicated that microRNAs (miRNAs) are functionally associated with the development and progression of various complex human diseases. However, the roles of miRNAs in multiple biological processes or various diseases and their underlying molecular mechanisms still have not been fully understood yet. Predicting potential miRNA-disease associations by integrating various heterogeneous biological datasets is of great significance to the biomedical research. Computational methods could obtain potential miRNA-disease associations in a short time, which significantly reduce the experimental time and cost. Considering the limitations in previous computational methods, we developed the model of Within and Between Score for MiRNA-Disease Association prediction (WBSMDA) to predict potential miRNAs associated with various complex diseases. WBSMDA could be applied to the diseases without any known related miRNAs. The AUC of 0.8031 based on Leave-one-out cross validation has demonstrated its reliable performance. WBSMDA was further applied to Colon Neoplasms, Prostate Neoplasms, and Lymphoma for the identification of their potential related miRNAs. As a result, 90%, 84%, and 80% of predicted miRNA-disease pairs in the top 50 prediction list for these three diseases have been confirmed by recent experimental literatures, respectively. It is anticipated that WBSMDA would be a useful resource for potential miRNA-disease association identification.Increasing evidences have indicated that microRNAs (miRNAs) are functionally associated with the development and progression of various complex human diseases. However, the roles of miRNAs in multiple biological processes or various diseases and their underlying molecular mechanisms still have not been fully understood yet. Predicting potential miRNA-disease associations by integrating various heterogeneous biological datasets is of great significance to the biomedical research. Computational methods could obtain potential miRNA-disease associations in a short time, which significantly reduce the experimental time and cost. Considering the limitations in previous computational methods, we developed the model of Within and Between Score for MiRNA-Disease Association prediction (WBSMDA) to predict potential miRNAs associated with various complex diseases. WBSMDA could be applied to the diseases without any known related miRNAs. The AUC of 0.8031 based on Leave-one-out cross validation has demonstrated its reliable performance. WBSMDA was further applied to Colon Neoplasms, Prostate Neoplasms, and Lymphoma for the identification of their potential related miRNAs. As a result, 90%, 84%, and 80% of predicted miRNA-disease pairs in the top 50 prediction list for these three diseases have been confirmed by recent experimental literatures, respectively. It is anticipated that WBSMDA would be a useful resource for potential miRNA-disease association identification. |
| ArticleNumber | 21106 |
| Author | Yan, Chenggang Clarence Liu, Ying You, Zhu-Hong Chen, Xing Zhang, Xu Zhang, Yongdong Deng, Lixi Dai, Qionghai |
| Author_xml | – sequence: 1 givenname: Xing surname: Chen fullname: Chen, Xing organization: National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences – sequence: 2 givenname: Chenggang Clarence surname: Yan fullname: Yan, Chenggang Clarence organization: Institute of Information and Control, Hangzhou Dianzi University, Department of Automation, Tsinghua University – sequence: 3 givenname: Xu surname: Zhang fullname: Zhang, Xu organization: School of Mechanical, Electrical & Information Engineering, Shandong University – sequence: 4 givenname: Zhu-Hong surname: You fullname: You, Zhu-Hong organization: School of Computer Science and Technology, China University of Mining and Technology – sequence: 5 givenname: Lixi surname: Deng fullname: Deng, Lixi organization: Institute of Computing Technology, Chinese Academy of Sciences, University of Chinese Academy of Sciences – sequence: 6 givenname: Ying surname: Liu fullname: Liu, Ying organization: School of Economics and Management, Beihang University – sequence: 7 givenname: Yongdong surname: Zhang fullname: Zhang, Yongdong organization: Key Lab of Intelligent Information Processing of Chinese Academy of Sciences, Institute of Computing Technology, Chinese Academy of Sciences – sequence: 8 givenname: Qionghai surname: Dai fullname: Dai, Qionghai organization: Department of Automation, Tsinghua University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/26880032$$D View this record in MEDLINE/PubMed |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 These authors contributed equally to this work. |
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