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10.3389/fimmu.2020.603615 http://scihub22266oqcxt.onion/10.3389/fimmu.2020.603615 33584672!7878370!33584672     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Front+Immunol 2020 ; 11 (ä): 603615 Nephropedia Template TP {{tp|p=33584672|t=2020. Indicator Regularized Non-Negative Matrix Factorization Method-Based Drug Repurposing for COVID-19 |pdf=|usr=}}{{33584672}} gab.com Text Indicator Regularized Non-Negative Matrix Factorization Method-Based Drug Repurposing for COVID-19 JO: Front Immunol http://www.kidney.de/pget.php?&tw=1&pmid=33584672 #FOAvip A novel coronavirus, named COVID-19, has become one of the most prevalent and severe infectious diseases in human history. Currently, there are only very few vaccines and therapeutic drugs against COVID-19, and their efficacies are yet to be tested. Drug repurposing aims to explore new applications of approved drugs, which can significantly reduce time and cost compared with de novo drug discovery. In this study, we built a virus-drug dataset, which included 34 viruses, 210 drugs, and 437 confirmed related virus-drug pairs from existing literature. Besides, we developed an Indicator Regularized non-negative Matrix Factorization (IRNMF) method, which introduced the indicator matrix and Karush-Kuhn-Tucker condition into the non-negative matrix factorization algorithm. According to the 5-fold cross-validation on the virus-drug dataset, the performance of IRNMF was better than other methods, and its Area Under receiver operating characteristic Curve (AUC) value was 0.8127. Additionally, we analyzed the case on COVID-19 infection, and our results suggested that the IRNMF algorithm could prioritize unknown virus-drug associations. Twit Text FOAVip Indicator Regularized Non-Negative Matrix Factorization Method-Based Drug Repurposing for COVID-19 ????Front Immunol http://www.kidney.de/pget.php?&tw=1&pmid=33584672 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33584672 #FOAvip English Wikipedia <ref>{{Cite pmid|33584672}}</ref> Indicator Regularized Non-Negative Matrix Factorization Method-Based Drug Repurposing for COVID-19 #MMPMID33584672 Tang X; Cai L; Meng Y; Xu J; Lu C; Yang J Front Immunol 2020[]; 11 (ä): 603615 PMID33584672show ga A novel coronavirus, named COVID-19, has become one of the most prevalent and severe infectious diseases in human history. Currently, there are only very few vaccines and therapeutic drugs against COVID-19, and their efficacies are yet to be tested. Drug repurposing aims to explore new applications of approved drugs, which can significantly reduce time and cost compared with de novo drug discovery. In this study, we built a virus-drug dataset, which included 34 viruses, 210 drugs, and 437 confirmed related virus-drug pairs from existing literature. Besides, we developed an Indicator Regularized non-negative Matrix Factorization (IRNMF) method, which introduced the indicator matrix and Karush-Kuhn-Tucker condition into the non-negative matrix factorization algorithm. According to the 5-fold cross-validation on the virus-drug dataset, the performance of IRNMF was better than other methods, and its Area Under receiver operating characteristic Curve (AUC) value was 0.8127. Additionally, we analyzed the case on COVID-19 infection, and our results suggested that the IRNMF algorithm could prioritize unknown virus-drug associations. |*Algorithms[MESH] |*Antiviral Agents[MESH] |*COVID-19 Drug Treatment[MESH] |*Drug Repositioning/methods[MESH] |Datasets as Topic[MESH] |Drug Discovery/*methods[MESH] |Humans[MESH]Indicator Regularized Non-Negative Matrix Factorization Method-Based D(epmc).pdf DeepDyve Pubget Overpricing