As one large class of non-coding RNAs （ncRNAs）, long ncRNAs （IneRNAs） have gained considerable attention in recent years. Mutations and dysfunction of lncRNAs have been implicated in human disorders. Many lncRNAs ...As one large class of non-coding RNAs （ncRNAs）, long ncRNAs （IneRNAs） have gained considerable attention in recent years. Mutations and dysfunction of lncRNAs have been implicated in human disorders. Many lncRNAs exert their effects through interactions with the corresponding RNA-binding proteins. Several computational approaches have been developed, but only few are able to perform the prediction of these interactions from a network-based point of view. Here, we introduce a computational method named lncRN^protein bipartite network inference （LPBNI）. LPBNI aims to identify potential lncRNA-interacting proteins, by making full use of the known IncRNA-protein interactions. Leave-one-out cross validation （LOOCV） test shows that LPBNI significantly outperforms other network-based methods, including random walk （RWR） and protein-based collaborative filtering （ProCF）. Furthermore, a case study was performed to demonstrate the performance of LPBNI using real data in predicting potential lncRNA-interacting proteins.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.61571414 and 61471331)
文摘As one large class of non-coding RNAs （ncRNAs）, long ncRNAs （IneRNAs） have gained considerable attention in recent years. Mutations and dysfunction of lncRNAs have been implicated in human disorders. Many lncRNAs exert their effects through interactions with the corresponding RNA-binding proteins. Several computational approaches have been developed, but only few are able to perform the prediction of these interactions from a network-based point of view. Here, we introduce a computational method named lncRN^protein bipartite network inference （LPBNI）. LPBNI aims to identify potential lncRNA-interacting proteins, by making full use of the known IncRNA-protein interactions. Leave-one-out cross validation （LOOCV） test shows that LPBNI significantly outperforms other network-based methods, including random walk （RWR） and protein-based collaborative filtering （ProCF）. Furthermore, a case study was performed to demonstrate the performance of LPBNI using real data in predicting potential lncRNA-interacting proteins.
