Network Diffusion Approach to Predict LncRNA Disease Associations Using Multi-Type Biological Networks: LION
Recently, long-non-coding RNAs (lncRNAs) have attracted attention because of their emerging role in many important biological mechanisms. The accumulating evidence indicates that the dysregulation of lncRNAs is associated with complex diseases. However, only a few lncRNA-disease associations have be...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2019-07-01
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| Series: | Frontiers in Physiology |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fphys.2019.00888/full |
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| author | Marissa Sumathipala Marissa Sumathipala Enrico Maiorino Scott T. Weiss Scott T. Weiss Amitabh Sharma Amitabh Sharma Amitabh Sharma |
| author_facet | Marissa Sumathipala Marissa Sumathipala Enrico Maiorino Scott T. Weiss Scott T. Weiss Amitabh Sharma Amitabh Sharma Amitabh Sharma |
| author_sort | Marissa Sumathipala |
| collection | DOAJ |
| description | Recently, long-non-coding RNAs (lncRNAs) have attracted attention because of their emerging role in many important biological mechanisms. The accumulating evidence indicates that the dysregulation of lncRNAs is associated with complex diseases. However, only a few lncRNA-disease associations have been experimentally validated and therefore, predicting potential lncRNAs that are associated with diseases become an important task. Current computational approaches often use known lncRNA-disease associations to predict potential lncRNA-disease links. In this work, we exploited the topology of multi-level networks to propose the LncRNA rankIng by NetwOrk DiffusioN (LION) approach to identify lncRNA-disease associations. The multi-level complex network consisted of lncRNA-protein, protein–protein interactions, and protein-disease associations. We applied the network diffusion algorithm of LION to predict the lncRNA-disease associations within the multi-level network. LION achieved an AUC value of 96.8% for cardiovascular diseases, 91.9% for cancer, and 90.2% for neurological diseases by using experimentally verified lncRNAs associated with diseases. Furthermore, compared to a similar approach (TPGLDA), LION performed better for cardiovascular diseases and cancer. Given the versatile role played by lncRNAs in different biological mechanisms that are perturbed in diseases, LION’s accurate prediction of lncRNA-disease associations helps in ranking lncRNAs that could function as potential biomarkers and potential drug targets. |
| first_indexed | 2024-12-19T10:31:34Z |
| format | Article |
| id | doaj.art-7154f4a9b9c64aaabe4ad5436ed3ee4f |
| institution | Directory Open Access Journal |
| issn | 1664-042X |
| language | English |
| last_indexed | 2024-12-19T10:31:34Z |
| publishDate | 2019-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physiology |
| spelling | doaj.art-7154f4a9b9c64aaabe4ad5436ed3ee4f2022-12-21T20:25:44ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2019-07-011010.3389/fphys.2019.00888446144Network Diffusion Approach to Predict LncRNA Disease Associations Using Multi-Type Biological Networks: LIONMarissa Sumathipala0Marissa Sumathipala1Enrico Maiorino2Scott T. Weiss3Scott T. Weiss4Amitabh Sharma5Amitabh Sharma6Amitabh Sharma7Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United StatesHarvard College, Cambridge, MA, United StatesChanning Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United StatesChanning Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United StatesDepartment of Medicine, Harvard Medical School, Boston, MA, United StatesChanning Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United StatesDepartment of Medicine, Harvard Medical School, Boston, MA, United StatesCenter for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United StatesRecently, long-non-coding RNAs (lncRNAs) have attracted attention because of their emerging role in many important biological mechanisms. The accumulating evidence indicates that the dysregulation of lncRNAs is associated with complex diseases. However, only a few lncRNA-disease associations have been experimentally validated and therefore, predicting potential lncRNAs that are associated with diseases become an important task. Current computational approaches often use known lncRNA-disease associations to predict potential lncRNA-disease links. In this work, we exploited the topology of multi-level networks to propose the LncRNA rankIng by NetwOrk DiffusioN (LION) approach to identify lncRNA-disease associations. The multi-level complex network consisted of lncRNA-protein, protein–protein interactions, and protein-disease associations. We applied the network diffusion algorithm of LION to predict the lncRNA-disease associations within the multi-level network. LION achieved an AUC value of 96.8% for cardiovascular diseases, 91.9% for cancer, and 90.2% for neurological diseases by using experimentally verified lncRNAs associated with diseases. Furthermore, compared to a similar approach (TPGLDA), LION performed better for cardiovascular diseases and cancer. Given the versatile role played by lncRNAs in different biological mechanisms that are perturbed in diseases, LION’s accurate prediction of lncRNA-disease associations helps in ranking lncRNAs that could function as potential biomarkers and potential drug targets.https://www.frontiersin.org/article/10.3389/fphys.2019.00888/fulllncRNAnetwork medicineinteractomenetwork diffusiondiseaseprotein–protein interactions |
| spellingShingle | Marissa Sumathipala Marissa Sumathipala Enrico Maiorino Scott T. Weiss Scott T. Weiss Amitabh Sharma Amitabh Sharma Amitabh Sharma Network Diffusion Approach to Predict LncRNA Disease Associations Using Multi-Type Biological Networks: LION Frontiers in Physiology lncRNA network medicine interactome network diffusion disease protein–protein interactions |
| title | Network Diffusion Approach to Predict LncRNA Disease Associations Using Multi-Type Biological Networks: LION |
| title_full | Network Diffusion Approach to Predict LncRNA Disease Associations Using Multi-Type Biological Networks: LION |
| title_fullStr | Network Diffusion Approach to Predict LncRNA Disease Associations Using Multi-Type Biological Networks: LION |
| title_full_unstemmed | Network Diffusion Approach to Predict LncRNA Disease Associations Using Multi-Type Biological Networks: LION |
| title_short | Network Diffusion Approach to Predict LncRNA Disease Associations Using Multi-Type Biological Networks: LION |
| title_sort | network diffusion approach to predict lncrna disease associations using multi type biological networks lion |
| topic | lncRNA network medicine interactome network diffusion disease protein–protein interactions |
| url | https://www.frontiersin.org/article/10.3389/fphys.2019.00888/full |
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