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A Novel Network-Based Computational Model for Prediction of Potential LncRNA–Disease Association
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| Zeitschriftentitel: | International Journal of Molecular Sciences |
|---|---|
| Personen und Körperschaften: | , , , , |
| In: | International Journal of Molecular Sciences, 20, 2019, 7, S. 1549 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
MDPI AG
|
| Schlagwörter: |
| author_facet |
Liu, Yang Feng, Xiang Zhao, Haochen Xuan, Zhanwei Wang, Lei Liu, Yang Feng, Xiang Zhao, Haochen Xuan, Zhanwei Wang, Lei |
|---|---|
| author |
Liu, Yang Feng, Xiang Zhao, Haochen Xuan, Zhanwei Wang, Lei |
| spellingShingle |
Liu, Yang Feng, Xiang Zhao, Haochen Xuan, Zhanwei Wang, Lei International Journal of Molecular Sciences A Novel Network-Based Computational Model for Prediction of Potential LncRNA–Disease Association Inorganic Chemistry Organic Chemistry Physical and Theoretical Chemistry Computer Science Applications Spectroscopy Molecular Biology General Medicine Catalysis |
| author_sort |
liu, yang |
| spelling |
Liu, Yang Feng, Xiang Zhao, Haochen Xuan, Zhanwei Wang, Lei 1422-0067 MDPI AG Inorganic Chemistry Organic Chemistry Physical and Theoretical Chemistry Computer Science Applications Spectroscopy Molecular Biology General Medicine Catalysis http://dx.doi.org/10.3390/ijms20071549 <jats:p>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.</jats:p> A Novel Network-Based Computational Model for Prediction of Potential LncRNA–Disease Association International Journal of Molecular Sciences |
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| title |
A Novel Network-Based Computational Model for Prediction of Potential LncRNA–Disease Association |
| title_unstemmed |
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 |
a novel network-based computational model for prediction of potential lncrna–disease association |
| topic |
Inorganic Chemistry Organic Chemistry Physical and Theoretical Chemistry Computer Science Applications Spectroscopy Molecular Biology General Medicine Catalysis |
| url |
http://dx.doi.org/10.3390/ijms20071549 |
| publishDate |
2019 |
| physical |
1549 |
| description |
<jats:p>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.</jats:p> |
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| author | Liu, Yang, Feng, Xiang, Zhao, Haochen, Xuan, Zhanwei, Wang, Lei |
| author_facet | Liu, Yang, Feng, Xiang, Zhao, Haochen, Xuan, Zhanwei, Wang, Lei, Liu, Yang, Feng, Xiang, Zhao, Haochen, Xuan, Zhanwei, Wang, Lei |
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| description | <jats:p>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.</jats:p> |
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| spelling | Liu, Yang Feng, Xiang Zhao, Haochen Xuan, Zhanwei Wang, Lei 1422-0067 MDPI AG Inorganic Chemistry Organic Chemistry Physical and Theoretical Chemistry Computer Science Applications Spectroscopy Molecular Biology General Medicine Catalysis http://dx.doi.org/10.3390/ijms20071549 <jats:p>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.</jats:p> A Novel Network-Based Computational Model for Prediction of Potential LncRNA–Disease Association International Journal of Molecular Sciences |
| spellingShingle | Liu, Yang, Feng, Xiang, Zhao, Haochen, Xuan, Zhanwei, Wang, Lei, International Journal of Molecular Sciences, A Novel Network-Based Computational Model for Prediction of Potential LncRNA–Disease Association, Inorganic Chemistry, Organic Chemistry, Physical and Theoretical Chemistry, Computer Science Applications, Spectroscopy, Molecular Biology, General Medicine, Catalysis |
| 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 | a novel network-based computational model for prediction of potential lncrna–disease association |
| title_unstemmed | A Novel Network-Based Computational Model for Prediction of Potential LncRNA–Disease Association |
| topic | Inorganic Chemistry, Organic Chemistry, Physical and Theoretical Chemistry, Computer Science Applications, Spectroscopy, Molecular Biology, General Medicine, Catalysis |
| url | http://dx.doi.org/10.3390/ijms20071549 |