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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.
Pubmed ID: 31379598
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A database covering eight category functional interactions between noncoding RNAs (except tRNAs and rRNAs) and proteins related biomacromolecules (proteins, mRNAs and genomic DNAs) in six model organisms. Functional interactions imply both physical interactions between the ncRNA and protein, and other forms of interaction where the combination of an ncRNA and an mRNA or a genomic DNA sequence elicits a cellular reaction. This database is distinguished from other biomolecular interaction database by: 1. The data of NPInter is novel, in the sense that no earlier database has especially cataloged this type of data (ncRNA-protein interactions). The database now contains more than 700 published functional interactions from the six organisms E. coli, yeast, worm, fly, mouse and human in which functional interactions experiments have been concentrated. The amount of data is not large, but the NPInter covers almost all experimentally verified ncRNA functional interaction data which had been published before the end of last year. 2. The ncRNA functional interaction data are entered into NPInter only following publication in books or peer-reviewed journals. Entry is done manually by a curator, and thereafter double-checked by a second curator. 3. We introduce a classification of the functional interaction data, which is based on the functional interaction process the ncRNA takes part in. 4. NPInter also provides an efficient search option, allowing recovery of interactions, related publications and other information.
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