دورية أكاديمية
DeepWalk based method to predict lncRNA-miRNA associations via lncRNA-miRNA-disease-protein-drug graph
| العنوان: | DeepWalk based method to predict lncRNA-miRNA associations via lncRNA-miRNA-disease-protein-drug graph |
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| المؤلفون: | Long Yang, Li-Ping Li, Hai-Cheng Yi |
| المصدر: | BMC Bioinformatics, Vol 22, Iss S12, Pp 1-16 (2022) |
| بيانات النشر: | BMC, 2022. |
| سنة النشر: | 2022 |
| المجموعة: | LCC:Computer applications to medicine. Medical informatics LCC:Biology (General) |
| مصطلحات موضوعية: | DeepWalk, LncRNA, miRNA, Random forest, Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5 |
| الوصف: | Abstract Background Long non-coding RNAs (lncRNAs) play a crucial role in diverse biological processes and have been confirmed to be concerned with various diseases. Largely uncharacterized of the physiological role and functions of lncRNA remains. MicroRNAs (miRNAs), which are usually 20–24 nucleotides, have several critical regulatory parts in cells. LncRNA can be regarded as a sponge to adsorb miRNA and indirectly regulate transcription and translation. Thus, the identification of lncRNA-miRNA associations is essential and valuable. Results In our work, we present DWLMI to infer the potential associations between lncRNAs and miRNAs by representing them as vectors via a lncRNA-miRNA-disease-protein-drug graph. Specifically, DeepWalk can be used to learn the behavior representation of vertices. The methods of fingerprint, k-mer and MeSH descriptors were mainly used to learn the attribute representation of vertices. By combining the above two kinds of information, unknown lncRNA-miRNA associations can be predicted by the random forest classifier. Under the five-fold cross-validation, the proposed DWLMI model obtained an average prediction accuracy of 95.22% with a sensitivity of 94.35% at the AUC of 98.56%. Conclusions The experimental results demonstrated that DWLMI can effectively predict the potential lncRNA-miRNA associated pairs, and the results can provide a new insight for related non-coding RNA researchers in the field of combing biology big data with deep learning. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1471-2105 |
| Relation: | https://doaj.org/toc/1471-2105 |
| DOI: | 10.1186/s12859-022-04579-0 |
| URL الوصول: | https://doaj.org/article/7356e79ed0754ee4a961e62fce794bca |
| رقم الأكسشن: | edsdoj.7356e79ed0754ee4a961e62fce794bca |
| قاعدة البيانات: | Directory of Open Access Journals |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 14712105 |
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| DOI: | 10.1186/s12859-022-04579-0 |