miRNA-disease Association Prediction Model Based on Stacked Autoencoder
As a group of small non-coding RNA,the abnormal regulation of miRNA is closely related to the occurrence and deve-lopment of human diseases.The study on the associations between miRNA and disease is important for understanding the pathogenic mechanism of human diseases.Machine learning methods are w...
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| Format: | Article |
| Language: | zho |
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Editorial office of Computer Science
2021-10-01
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| Series: | Jisuanji kexue |
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| Online Access: | http://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2021-10-114.pdf |
| _version_ | 1819021693619798016 |
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| author | LIU Dan, ZHAO Sen, YAN Zhi-liang, ZHAO Jing, WANG Hui-qing |
| author_facet | LIU Dan, ZHAO Sen, YAN Zhi-liang, ZHAO Jing, WANG Hui-qing |
| author_sort | LIU Dan, ZHAO Sen, YAN Zhi-liang, ZHAO Jing, WANG Hui-qing |
| collection | DOAJ |
| description | As a group of small non-coding RNA,the abnormal regulation of miRNA is closely related to the occurrence and deve-lopment of human diseases.The study on the associations between miRNA and disease is important for understanding the pathogenic mechanism of human diseases.Machine learning methods are widely used to predict miRNA-disease associations.However,existing methods only consider the information of miRNA and disease similarity networks,ignoring the topology structure of the similarity networks.Therefore,SAEMDA model based on stacked autoencoder is proposed in this paper,it gets the topological structure features of miRNA and disease similarity networks by restart random walk,obtains the abstract low dimensional features of miRNA and disease by stacked autoencoder,and the low dimensional features are input into deep neural network for miRNA-disease associations prediction.SAEMDA model has achieved great results in 5-fold cross-validation,and it has been validated in cases of colon cancer and lung cancer additionally.As for colon cancer,45 of the top 50 miRNA-disease associations predicted by this model are verified in the database;and in the cases of lung cancer,all the top 50 miRNAs are verified in the database.<br/> |
| first_indexed | 2024-12-21T04:11:10Z |
| format | Article |
| id | doaj.art-f3f320b9c25e41e29c7d5a8071e85508 |
| institution | Directory Open Access Journal |
| issn | 1002-137X |
| language | zho |
| last_indexed | 2024-12-21T04:11:10Z |
| publishDate | 2021-10-01 |
| publisher | Editorial office of Computer Science |
| record_format | Article |
| series | Jisuanji kexue |
| spelling | doaj.art-f3f320b9c25e41e29c7d5a8071e855082022-12-21T19:16:28ZzhoEditorial office of Computer ScienceJisuanji kexue1002-137X2021-10-01481011412010.11896/jsjkx.200900169miRNA-disease Association Prediction Model Based on Stacked AutoencoderLIU Dan, ZHAO Sen, YAN Zhi-liang, ZHAO Jing, WANG Hui-qing0College of Information and Computer,Taiyuan University of Technology,Taiyuan 030606,ChinaAs a group of small non-coding RNA,the abnormal regulation of miRNA is closely related to the occurrence and deve-lopment of human diseases.The study on the associations between miRNA and disease is important for understanding the pathogenic mechanism of human diseases.Machine learning methods are widely used to predict miRNA-disease associations.However,existing methods only consider the information of miRNA and disease similarity networks,ignoring the topology structure of the similarity networks.Therefore,SAEMDA model based on stacked autoencoder is proposed in this paper,it gets the topological structure features of miRNA and disease similarity networks by restart random walk,obtains the abstract low dimensional features of miRNA and disease by stacked autoencoder,and the low dimensional features are input into deep neural network for miRNA-disease associations prediction.SAEMDA model has achieved great results in 5-fold cross-validation,and it has been validated in cases of colon cancer and lung cancer additionally.As for colon cancer,45 of the top 50 miRNA-disease associations predicted by this model are verified in the database;and in the cases of lung cancer,all the top 50 miRNAs are verified in the database.<br/>http://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2021-10-114.pdfmirna-disease associations|similarity networks|topological structure|random walk|stacked autoencoder |
| spellingShingle | LIU Dan, ZHAO Sen, YAN Zhi-liang, ZHAO Jing, WANG Hui-qing miRNA-disease Association Prediction Model Based on Stacked Autoencoder Jisuanji kexue mirna-disease associations|similarity networks|topological structure|random walk|stacked autoencoder |
| title | miRNA-disease Association Prediction Model Based on Stacked Autoencoder |
| title_full | miRNA-disease Association Prediction Model Based on Stacked Autoencoder |
| title_fullStr | miRNA-disease Association Prediction Model Based on Stacked Autoencoder |
| title_full_unstemmed | miRNA-disease Association Prediction Model Based on Stacked Autoencoder |
| title_short | miRNA-disease Association Prediction Model Based on Stacked Autoencoder |
| title_sort | mirna disease association prediction model based on stacked autoencoder |
| topic | mirna-disease associations|similarity networks|topological structure|random walk|stacked autoencoder |
| url | http://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2021-10-114.pdf |
| work_keys_str_mv | AT liudanzhaosenyanzhiliangzhaojingwanghuiqing mirnadiseaseassociationpredictionmodelbasedonstackedautoencoder |