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BiRWLGO: A global network-based strategy for lncRNA function annotation using bi-random walk

A large number of long non-coding RNAs (lncRNAs) have been identified over the past decades. Accumulating evidence proves that lncRNAs play key roles in various biological processes. However, the majority of the lncRNAs have not been functionally characterized. The annotation of lncRNA functions has become an area of focus in the fields of biology and bioinformatics. In this paper, we develop a global network-based strategy, BiRWLGO, to predict probable functions for lncRNAs at large scale. In BiRWLGO, we first build a global network consisting of three networks: lncRNA-lncRNA similarity network, lncRNA-protein interaction network and protein-protein interaction network. Then the bi-random walk algorithm is applied to explore similarities between lncRNAs and proteins. The functions of a query lncRNA can be obtained according to the Gene Ontology (GO) terms of its neighboring proteins. We compare the performance of BiRWLGO with other state-of-the-art approaches on a manually annotated lncRNA benchmark with known GO terms. As a result, BiRWLGO achieves the best predictive performance in terms of both maximum F-measure (Fmax) and coverage. Moreover, we demonstrate that integrating the protein-protein interactions can help improve the predictive performance of lncRNA functions.