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10.18632/oncotarget.15061 http://scihub22266oqcxt.onion/10.18632/oncotarget.15061 C5400576!5400576 !28177900     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=28177900 &cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 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 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\28177900 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Oncotarget 2017 ; 8 (13 ): 21187-21199 Nephropedia Template TP {{tp|p=28177900 |t=2017. MCMDA: Matrix completion for MiRNA-disease association prediction |pdf=|usr=}}{{28177900 }} gab.com Text MCMDA: Matrix completion for MiRNA-disease association prediction JO: Oncotarget http://www.kidney.de/pget.php?&tw=1&pmid=28177900 #FOAvip Nowadays, researchers have realized that microRNAs (miRNAs) are playing a significant role in many important biological processes and they are closely connected with various complex human diseases. However, since there are too many possible miRNA-disease associations to analyze, it remains difficult to predict the potential miRNAs related to human diseases without a systematic and effective method. In this study, we developed a Matrix Completion for MiRNA-Disease Association prediction model (MCMDA) based on the known miRNA-disease associations in HMDD database. MCMDA model utilized the matrix completion algorithm to update the adjacency matrix of known miRNA-disease associations and furthermore predict the potential associations. To evaluate the performance of MCMDA, we performed leave-one-out cross validation (LOOCV) and 5-fold cross validation to compare MCMDA with three previous classical computational models (RLSMDA, HDMP, and WBSMDA). As a result, MCMDA achieved AUCs of 0.8749 in global LOOCV, 0.7718 in local LOOCV and average AUC of 0.8767+/-0.0011 in 5-fold cross validation. Moreover, the prediction results associated with colon neoplasms, kidney neoplasms, lymphoma and prostate neoplasms were verified. As a consequence, 84%, 86%, 78% and 90% of the top 50 potential miRNAs for these four diseases were respectively confirmed by recent experimental discoveries. Therefore, MCMDA model is superior to the previous models in that it improves the prediction performance although it only depends on the known miRNA-disease associations. Twit Text FOAVip MCMDA: Matrix completion for MiRNA-disease association prediction ????Oncotarget http://www.kidney.de/pget.php?&tw=1&pmid=28177900 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28177900 #FOAvip English Wikipedia <ref>{{Cite pmid|28177900 }}</ref> MCMDA: Matrix completion for MiRNA-disease association prediction #MMPMID28177900 Li JQ ; Rong ZH ; Chen X ; Yan GY ; You ZH Oncotarget 2017[Mar]; 8 (13 ): 21187-21199 PMID28177900 show ga Nowadays, researchers have realized that microRNAs (miRNAs) are playing a significant role in many important biological processes and they are closely connected with various complex human diseases. However, since there are too many possible miRNA-disease associations to analyze, it remains difficult to predict the potential miRNAs related to human diseases without a systematic and effective method. In this study, we developed a Matrix Completion for MiRNA-Disease Association prediction model (MCMDA) based on the known miRNA-disease associations in HMDD database. MCMDA model utilized the matrix completion algorithm to update the adjacency matrix of known miRNA-disease associations and furthermore predict the potential associations. To evaluate the performance of MCMDA, we performed leave-one-out cross validation (LOOCV) and 5-fold cross validation to compare MCMDA with three previous classical computational models (RLSMDA, HDMP, and WBSMDA). As a result, MCMDA achieved AUCs of 0.8749 in global LOOCV, 0.7718 in local LOOCV and average AUC of 0.8767+/-0.0011 in 5-fold cross validation. Moreover, the prediction results associated with colon neoplasms, kidney neoplasms, lymphoma and prostate neoplasms were verified. As a consequence, 84%, 86%, 78% and 90% of the top 50 potential miRNAs for these four diseases were respectively confirmed by recent experimental discoveries. Therefore, MCMDA model is superior to the previous models in that it improves the prediction performance although it only depends on the known miRNA-disease associations. |*Algorithms [MESH] |Area Under Curve [MESH] |Colonic Neoplasms/genetics [MESH] |Computational Biology/*methods [MESH] |Computer Simulation [MESH] |Genetic Association Studies [MESH] |Genetic Predisposition to Disease/*genetics [MESH] |Humans [MESH] |Kidney Neoplasms/genetics [MESH] |Lymphoma/genetics [MESH] |MicroRNAs/*genetics [MESH] |ROC Curve [MESH]MCMDA: Matrix completion for MiRNA-disease association prediction (epmc).pdf DeepDyve Pubget Overpricing