IDLDA: An Improved Diffusion Model for Predicting LncRNA–Disease Associations
It has been demonstrated that long non-coding RNAs (lncRNAs) play important roles in a variety of biological processes associated with human diseases. However, the identification of lncRNA–disease associations by experimental methods is time-consuming and labor-intensive. Computational methods provi...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2019-12-01
|
| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fgene.2019.01259/full |
| _version_ | 1828522594686468096 |
|---|---|
| author | Qi Wang Qi Wang Guiying Yan Guiying Yan |
| author_facet | Qi Wang Qi Wang Guiying Yan Guiying Yan |
| author_sort | Qi Wang |
| collection | DOAJ |
| description | It has been demonstrated that long non-coding RNAs (lncRNAs) play important roles in a variety of biological processes associated with human diseases. However, the identification of lncRNA–disease associations by experimental methods is time-consuming and labor-intensive. Computational methods provide an effective strategy to predict more potential lncRNA–disease associations to some degree. Based on the hypothesis that phenotypically similar diseases are often associated with functionally similar lncRNAs and vice versa, we developed an improved diffusion model to predict potential lncRNA–disease associations (IDLDA). As a result, our model performed well in the global and local cross-validations, which indicated that IDLDA had a great performance in predicting novel associations. Case studies of colon cancer, breast cancer, and gastric cancer were also implemented, all lncRNAs which ranked top 10 in both databases were verified by databases and related literature. The results showed that IDLDA might play a key role in biomedical research. |
| first_indexed | 2024-12-11T20:11:55Z |
| format | Article |
| id | doaj.art-a5e2617a61ea44f385945bb2b9575039 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-11T20:11:55Z |
| publishDate | 2019-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-a5e2617a61ea44f385945bb2b95750392022-12-22T00:52:17ZengFrontiers Media S.A.Frontiers in Genetics1664-80212019-12-011010.3389/fgene.2019.01259474587IDLDA: An Improved Diffusion Model for Predicting LncRNA–Disease AssociationsQi Wang0Qi Wang1Guiying Yan2Guiying Yan3 Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, ChinaSchool of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, ChinaSchool of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, ChinaIt has been demonstrated that long non-coding RNAs (lncRNAs) play important roles in a variety of biological processes associated with human diseases. However, the identification of lncRNA–disease associations by experimental methods is time-consuming and labor-intensive. Computational methods provide an effective strategy to predict more potential lncRNA–disease associations to some degree. Based on the hypothesis that phenotypically similar diseases are often associated with functionally similar lncRNAs and vice versa, we developed an improved diffusion model to predict potential lncRNA–disease associations (IDLDA). As a result, our model performed well in the global and local cross-validations, which indicated that IDLDA had a great performance in predicting novel associations. Case studies of colon cancer, breast cancer, and gastric cancer were also implemented, all lncRNAs which ranked top 10 in both databases were verified by databases and related literature. The results showed that IDLDA might play a key role in biomedical research.https://www.frontiersin.org/article/10.3389/fgene.2019.01259/fulllong non-coding RNAdiseaseassociation predictioncomputational prediction modeldiffusion model |
| spellingShingle | Qi Wang Qi Wang Guiying Yan Guiying Yan IDLDA: An Improved Diffusion Model for Predicting LncRNA–Disease Associations Frontiers in Genetics long non-coding RNA disease association prediction computational prediction model diffusion model |
| title | IDLDA: An Improved Diffusion Model for Predicting LncRNA–Disease Associations |
| title_full | IDLDA: An Improved Diffusion Model for Predicting LncRNA–Disease Associations |
| title_fullStr | IDLDA: An Improved Diffusion Model for Predicting LncRNA–Disease Associations |
| title_full_unstemmed | IDLDA: An Improved Diffusion Model for Predicting LncRNA–Disease Associations |
| title_short | IDLDA: An Improved Diffusion Model for Predicting LncRNA–Disease Associations |
| title_sort | idlda an improved diffusion model for predicting lncrna disease associations |
| topic | long non-coding RNA disease association prediction computational prediction model diffusion model |
| url | https://www.frontiersin.org/article/10.3389/fgene.2019.01259/full |
| work_keys_str_mv | AT qiwang idldaanimproveddiffusionmodelforpredictinglncrnadiseaseassociations AT qiwang idldaanimproveddiffusionmodelforpredictinglncrnadiseaseassociations AT guiyingyan idldaanimproveddiffusionmodelforpredictinglncrnadiseaseassociations AT guiyingyan idldaanimproveddiffusionmodelforpredictinglncrnadiseaseassociations |