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10.1016/j.gpb.2016.01.004 http://scihub22266oqcxt.onion/10.1016/j.gpb.2016.01.004 C4792848!4792848!26917505     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Genomics+Proteomics+Bioinformatics 2016 ; 14 (1): 62-71 Nephropedia Template TP {{tp|p=26917505|t=2016. A Bipartite Network-based Method for Prediction of Long Non-coding RNA?protein Interactions |pdf=|usr=}}{{26917505}} gab.com Text A Bipartite Network-based Method for Prediction of Long Non-coding RNA protein Interactions JO: Genomics Proteomics Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=26917505 #FOAvip As one large class of non-coding RNAs (ncRNAs), long ncRNAs (lncRNAs) 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 lncRNA?protein bipartite network inference (LPBNI). LPBNI aims to identify potential lncRNA?interacting proteins, by making full use of the known lncRNA?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. Twit Text FOAVip A Bipartite Network-based Method for Prediction of Long Non-coding RNA protein Interactions ????Genomics Proteomics Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=26917505 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26917505 #FOAvip English Wikipedia <ref>{{Cite pmid|26917505}}</ref> A Bipartite Network-based Method for Prediction of Long Non-coding RNA?protein Interactions #MMPMID26917505 Ge M; Li A; Wang M Genomics Proteomics Bioinformatics 2016[Feb]; 14 (1): 62-71 PMID26917505show ga As one large class of non-coding RNAs (ncRNAs), long ncRNAs (lncRNAs) 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 lncRNA?protein bipartite network inference (LPBNI). LPBNI aims to identify potential lncRNA?interacting proteins, by making full use of the known lncRNA?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. äA Bipartite Network-based Method for Prediction of Long Non-coding RNA(epmc).pdf DeepDyve Pubget Overpricing