More and more evidences demonstrate that the long non-coding RNAs (lncRNAs) play many key
roles in diverse biological processes. There is a critical need to annotate the functions
of increasing available lncRNAs. In this article, we try to apply a global network-based
strategy to tackle this issue for the first time. We develop a bi-colored network based
global function predictor, long non-coding RNA global function predictor
(‘lnc-GFP’), to predict probable functions for lncRNAs at large scale by
integrating gene expression data and protein interaction data. The performance of lnc-GFP
is evaluated on protein-coding and lncRNA genes. Cross-validation tests on protein-coding
genes with known function annotations indicate that our method can achieve a precision up
to 95%, with a suitable parameter setting. Among the 1713 lncRNAs in the bi-colored
network, the 1625 (94.9%) lncRNAs in the maximum connected component are all
functionally characterized. For the lncRNAs expressed in mouse embryo stem cells and
neuronal cells, the inferred putative functions by our method highly match those in the
known literature.
Increasing evidence has indicated that long non-coding RNAs (lncRNAs) are implicated in and associated with many complex human diseases. Despite of the accumulation of lncRNA-disease associations, only a few studies had studied the roles of these associations in pathogenesis. In this paper, we investigated lncRNA-disease associations from a network view to understand the contribution of these lncRNAs to complex diseases. Specifically, we studied both the properties of the diseases in which the lncRNAs were implicated, and that of the lncRNAs associated with complex diseases. Regarding the fact that protein coding genes and lncRNAs are involved in human diseases, we constructed a coding-non-coding gene-disease bipartite network based on known associations between diseases and disease-causing genes. We then applied a propagation algorithm to uncover the hidden lncRNA-disease associations in this network. The algorithm was evaluated by leave-one-out cross validation on 103 diseases in which at least two genes were known to be involved, and achieved an AUC of 0.7881. Our algorithm successfully predicted 768 potential lncRNA-disease associations between 66 lncRNAs and 193 diseases. Furthermore, our results for Alzheimer's disease, pancreatic cancer, and gastric cancer were verified by other independent studies.
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