Predicting Disease Related microRNA Based on Similarity and Topology
It is known that many diseases are caused by mutations or abnormalities in microRNA (miRNA). The usual method to predict miRNA disease relationships is to build a high-quality similarity network of diseases and miRNAs. All unobserved associations are ranked by their similarity scores, such that a hi...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-11-01
|
| Series: | Cells |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4409/8/11/1405 |
| _version_ | 1797761259907579904 |
|---|---|
| author | Zhihua Chen Xinke Wang Peng Gao Hongju Liu Bosheng Song |
| author_facet | Zhihua Chen Xinke Wang Peng Gao Hongju Liu Bosheng Song |
| author_sort | Zhihua Chen |
| collection | DOAJ |
| description | It is known that many diseases are caused by mutations or abnormalities in microRNA (miRNA). The usual method to predict miRNA disease relationships is to build a high-quality similarity network of diseases and miRNAs. All unobserved associations are ranked by their similarity scores, such that a higher score indicates a greater probability of a potential connection. However, this approach does not utilize information within the network. Therefore, in this study, we propose a machine learning method, called STIM, which uses network topology information to predict disease−miRNA associations. In contrast to the conventional approach, STIM constructs features according to information on similarity and topology in networks and then uses a machine learning model to predict potential associations. To verify the reliability and accuracy of our method, we compared STIM to other classical algorithms. The results of fivefold cross validation demonstrated that STIM outperforms many existing methods, particularly in terms of the area under the curve. In addition, the top 30 candidate miRNAs recommended by STIM in a case study of lung neoplasm have been confirmed in previous experiments, which proved the validity of the method. |
| first_indexed | 2024-03-12T19:11:24Z |
| format | Article |
| id | doaj.art-af4a9aba43aa43a2bea17014c2fe8ef3 |
| institution | Directory Open Access Journal |
| issn | 2073-4409 |
| language | English |
| last_indexed | 2024-03-12T19:11:24Z |
| publishDate | 2019-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cells |
| spelling | doaj.art-af4a9aba43aa43a2bea17014c2fe8ef32023-08-02T05:56:01ZengMDPI AGCells2073-44092019-11-01811140510.3390/cells8111405cells8111405Predicting Disease Related microRNA Based on Similarity and TopologyZhihua Chen0Xinke Wang1Peng Gao2Hongju Liu3Bosheng Song4Institute of Computing Science and Technology, Guangzhou University, Guangzhou 510006, ChinaSchool of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, ChinaCollege of Information Technology and Computer Science, University of the Cordilleras, Baguio 2600, PhilippinesSchool of Information Science and Engineering, Hunan University, Changsha 410082, ChinaIt is known that many diseases are caused by mutations or abnormalities in microRNA (miRNA). The usual method to predict miRNA disease relationships is to build a high-quality similarity network of diseases and miRNAs. All unobserved associations are ranked by their similarity scores, such that a higher score indicates a greater probability of a potential connection. However, this approach does not utilize information within the network. Therefore, in this study, we propose a machine learning method, called STIM, which uses network topology information to predict disease−miRNA associations. In contrast to the conventional approach, STIM constructs features according to information on similarity and topology in networks and then uses a machine learning model to predict potential associations. To verify the reliability and accuracy of our method, we compared STIM to other classical algorithms. The results of fivefold cross validation demonstrated that STIM outperforms many existing methods, particularly in terms of the area under the curve. In addition, the top 30 candidate miRNAs recommended by STIM in a case study of lung neoplasm have been confirmed in previous experiments, which proved the validity of the method.https://www.mdpi.com/2073-4409/8/11/1405mirnanetwork embeddingheterogeneous networklink predictiontopology informationmachine learning |
| spellingShingle | Zhihua Chen Xinke Wang Peng Gao Hongju Liu Bosheng Song Predicting Disease Related microRNA Based on Similarity and Topology Cells mirna network embedding heterogeneous network link prediction topology information machine learning |
| title | Predicting Disease Related microRNA Based on Similarity and Topology |
| title_full | Predicting Disease Related microRNA Based on Similarity and Topology |
| title_fullStr | Predicting Disease Related microRNA Based on Similarity and Topology |
| title_full_unstemmed | Predicting Disease Related microRNA Based on Similarity and Topology |
| title_short | Predicting Disease Related microRNA Based on Similarity and Topology |
| title_sort | predicting disease related microrna based on similarity and topology |
| topic | mirna network embedding heterogeneous network link prediction topology information machine learning |
| url | https://www.mdpi.com/2073-4409/8/11/1405 |
| work_keys_str_mv | AT zhihuachen predictingdiseaserelatedmicrornabasedonsimilarityandtopology AT xinkewang predictingdiseaserelatedmicrornabasedonsimilarityandtopology AT penggao predictingdiseaserelatedmicrornabasedonsimilarityandtopology AT hongjuliu predictingdiseaserelatedmicrornabasedonsimilarityandtopology AT boshengsong predictingdiseaserelatedmicrornabasedonsimilarityandtopology |