Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.18632/oncotarget.19588 http://scihub22266oqcxt.onion/10.18632/oncotarget.19588 C5601150!5601150!28947982     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Oncotarget 2017 ; 8 (36): 60429-46 Nephropedia Template TP {{tp|p=28947982|t=2017. BRWLDA: bi-random walks for predicting lncRNA-disease associations |pdf=|usr=}}{{28947982}} gab.com Text BRWLDA: bi-random walks for predicting lncRNA-disease associations JO: Oncotarget http://www.kidney.de/pget.php?&tw=1&pmid=28947982 #FOAvip Increasing efforts have been done to figure out the association between lncRNAs and complex diseases. Many computational models construct various lncRNA similarity networks, disease similarity networks, along with known lncRNA-disease associations to infer novel associations. However, most of them neglect the structural difference between lncRNAs network and diseases network, hierarchical relationships between diseases and pattern of newly discovered associations. In this study, we developed a model that performs Bi-Random Walks to predict novel LncRNA-Disease Associations (BRWLDA in short). This model utilizes multiple heterogeneous data to construct the lncRNA functional similarity network, and Disease Ontology to construct a disease network. It then constructs a directed bi-relational network based on these two networks and available lncRNAs-disease associations. Next, it applies bi-random walks on the network to predict potential associations. BRWLDA achieves reliable and better performance than other comparing methods not only on experiment verified associations, but also on the simulated experiments with masked associations. Case studies further demonstrate the feasibility of BRWLDA in identifying new lncRNA-disease associations. Twit Text FOAVip BRWLDA: bi-random walks for predicting lncRNA-disease associations ????Oncotarget http://www.kidney.de/pget.php?&tw=1&pmid=28947982 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28947982 #FOAvip English Wikipedia <ref>{{Cite pmid|28947982}}</ref> BRWLDA: bi-random walks for predicting lncRNA-disease associations #MMPMID28947982 Yu G; Fu G; Lu C; Ren Y; Wang J Oncotarget 2017[Sep]; 8 (36): 60429-46 PMID28947982show ga Increasing efforts have been done to figure out the association between lncRNAs and complex diseases. Many computational models construct various lncRNA similarity networks, disease similarity networks, along with known lncRNA-disease associations to infer novel associations. However, most of them neglect the structural difference between lncRNAs network and diseases network, hierarchical relationships between diseases and pattern of newly discovered associations. In this study, we developed a model that performs Bi-Random Walks to predict novel LncRNA-Disease Associations (BRWLDA in short). This model utilizes multiple heterogeneous data to construct the lncRNA functional similarity network, and Disease Ontology to construct a disease network. It then constructs a directed bi-relational network based on these two networks and available lncRNAs-disease associations. Next, it applies bi-random walks on the network to predict potential associations. BRWLDA achieves reliable and better performance than other comparing methods not only on experiment verified associations, but also on the simulated experiments with masked associations. Case studies further demonstrate the feasibility of BRWLDA in identifying new lncRNA-disease associations. äBRWLDA: bi-random walks for predicting lncRNA-disease associations (epmc).pdf DeepDyve Pubget Overpricing