A Novel Network-Based Computational Model for Prediction of Potential LncRNA–Disease Association
Accumulating studies have shown that long non-coding RNAs (lncRNAs) are involved in many biological processes and play important roles in a variety of complex human diseases. Developing effective computational models to identify potential relationships between lncRNAs and diseases can not only help...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-03-01
|
| Series: | International Journal of Molecular Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1422-0067/20/7/1549 |
| _version_ | 1811344182491480064 |
|---|---|
| author | Yang Liu Xiang Feng Haochen Zhao Zhanwei Xuan Lei Wang |
| author_facet | Yang Liu Xiang Feng Haochen Zhao Zhanwei Xuan Lei Wang |
| author_sort | Yang Liu |
| collection | DOAJ |
| description | Accumulating studies have shown that long non-coding RNAs (lncRNAs) are involved in many biological processes and play important roles in a variety of complex human diseases. Developing effective computational models to identify potential relationships between lncRNAs and diseases can not only help us understand disease mechanisms at the lncRNA molecular level, but also promote the diagnosis, treatment, prognosis, and prevention of human diseases. For this paper, a network-based model called NBLDA was proposed to discover potential lncRNA–disease associations, in which two novel lncRNA–disease weighted networks were constructed. They were first based on known lncRNA–disease associations and topological similarity of the lncRNA–disease association network, and then an lncRNA–lncRNA weighted matrix and a disease–disease weighted matrix were obtained based on a resource allocation strategy of unequal allocation and unbiased consistence. Finally, a label propagation algorithm was applied to predict associated lncRNAs for the investigated diseases. Moreover, in order to estimate the prediction performance of NBLDA, the framework of leave-one-out cross validation (LOOCV) was implemented on NBLDA, and simulation results showed that NBLDA can achieve reliable areas under the ROC curve (AUCs) of 0.8846, 0.8273, and 0.8075 in three known lncRNA–disease association datasets downloaded from the lncRNADisease database, respectively. Furthermore, in case studies of lung cancer, leukemia, and colorectal cancer, simulation results demonstrated that NBLDA can be a powerful tool for identifying potential lncRNA–disease associations as well. |
| first_indexed | 2024-04-13T19:43:13Z |
| format | Article |
| id | doaj.art-7ada0342eadf4bfc92c5bfea4b601717 |
| institution | Directory Open Access Journal |
| issn | 1422-0067 |
| language | English |
| last_indexed | 2024-04-13T19:43:13Z |
| publishDate | 2019-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Molecular Sciences |
| spelling | doaj.art-7ada0342eadf4bfc92c5bfea4b6017172022-12-22T02:32:49ZengMDPI AGInternational Journal of Molecular Sciences1422-00672019-03-01207154910.3390/ijms20071549ijms20071549A Novel Network-Based Computational Model for Prediction of Potential LncRNA–Disease AssociationYang Liu0Xiang Feng1Haochen Zhao2Zhanwei Xuan3Lei Wang4College of Computer Engineering & Applied Mathematics, Changsha University, Changsha 410000, ChinaCollege of Computer Engineering & Applied Mathematics, Changsha University, Changsha 410000, ChinaKey Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan 411100, ChinaKey Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan 411100, ChinaCollege of Computer Engineering & Applied Mathematics, Changsha University, Changsha 410000, ChinaAccumulating studies have shown that long non-coding RNAs (lncRNAs) are involved in many biological processes and play important roles in a variety of complex human diseases. Developing effective computational models to identify potential relationships between lncRNAs and diseases can not only help us understand disease mechanisms at the lncRNA molecular level, but also promote the diagnosis, treatment, prognosis, and prevention of human diseases. For this paper, a network-based model called NBLDA was proposed to discover potential lncRNA–disease associations, in which two novel lncRNA–disease weighted networks were constructed. They were first based on known lncRNA–disease associations and topological similarity of the lncRNA–disease association network, and then an lncRNA–lncRNA weighted matrix and a disease–disease weighted matrix were obtained based on a resource allocation strategy of unequal allocation and unbiased consistence. Finally, a label propagation algorithm was applied to predict associated lncRNAs for the investigated diseases. Moreover, in order to estimate the prediction performance of NBLDA, the framework of leave-one-out cross validation (LOOCV) was implemented on NBLDA, and simulation results showed that NBLDA can achieve reliable areas under the ROC curve (AUCs) of 0.8846, 0.8273, and 0.8075 in three known lncRNA–disease association datasets downloaded from the lncRNADisease database, respectively. Furthermore, in case studies of lung cancer, leukemia, and colorectal cancer, simulation results demonstrated that NBLDA can be a powerful tool for identifying potential lncRNA–disease associations as well.https://www.mdpi.com/1422-0067/20/7/1549lncRNAdiseaseassociation predictionresource allocationlabel propagation |
| spellingShingle | Yang Liu Xiang Feng Haochen Zhao Zhanwei Xuan Lei Wang A Novel Network-Based Computational Model for Prediction of Potential LncRNA–Disease Association International Journal of Molecular Sciences lncRNA disease association prediction resource allocation label propagation |
| title | A Novel Network-Based Computational Model for Prediction of Potential LncRNA–Disease Association |
| title_full | A Novel Network-Based Computational Model for Prediction of Potential LncRNA–Disease Association |
| title_fullStr | A Novel Network-Based Computational Model for Prediction of Potential LncRNA–Disease Association |
| title_full_unstemmed | A Novel Network-Based Computational Model for Prediction of Potential LncRNA–Disease Association |
| title_short | A Novel Network-Based Computational Model for Prediction of Potential LncRNA–Disease Association |
| title_sort | novel network based computational model for prediction of potential lncrna disease association |
| topic | lncRNA disease association prediction resource allocation label propagation |
| url | https://www.mdpi.com/1422-0067/20/7/1549 |
| work_keys_str_mv | AT yangliu anovelnetworkbasedcomputationalmodelforpredictionofpotentiallncrnadiseaseassociation AT xiangfeng anovelnetworkbasedcomputationalmodelforpredictionofpotentiallncrnadiseaseassociation AT haochenzhao anovelnetworkbasedcomputationalmodelforpredictionofpotentiallncrnadiseaseassociation AT zhanweixuan anovelnetworkbasedcomputationalmodelforpredictionofpotentiallncrnadiseaseassociation AT leiwang anovelnetworkbasedcomputationalmodelforpredictionofpotentiallncrnadiseaseassociation AT yangliu novelnetworkbasedcomputationalmodelforpredictionofpotentiallncrnadiseaseassociation AT xiangfeng novelnetworkbasedcomputationalmodelforpredictionofpotentiallncrnadiseaseassociation AT haochenzhao novelnetworkbasedcomputationalmodelforpredictionofpotentiallncrnadiseaseassociation AT zhanweixuan novelnetworkbasedcomputationalmodelforpredictionofpotentiallncrnadiseaseassociation AT leiwang novelnetworkbasedcomputationalmodelforpredictionofpotentiallncrnadiseaseassociation |