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10.1016/j.compbiomed.2022.105428 http://scihub22266oqcxt.onion/10.1016/j.compbiomed.2022.105428 35339845!8940269!35339845     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 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\35339845.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Comput+Biol+Med 2022 ; 145 (ä): 105428 Nephropedia Template TP {{tp|p=35339845|t=2022. Integration of omics data to generate and analyse COVID-19 specific genome-scale metabolic models |pdf=|usr=}}{{35339845}} gab.com Text Integration of omics data to generate and analyse COVID-19 specific genome-scale metabolic models JO: Comput Biol Med http://www.kidney.de/pget.php?&tw=1&pmid=35339845 #FOAvip COVID-19 presents a complex disease that needs to be addressed using systems medicine approaches that include genome-scale metabolic models (GEMs). Previous studies have used a single model extraction method (MEM) and/or a single transcriptomic dataset to reconstruct context-specific models, which proved to be insufficient for the broader biological contexts. We have applied four MEMs in combination with five COVID-19 datasets. Models produced by GIMME were separated by infection, while tINIT preserved the biological variability in the data and enabled the best prediction of the enrichment of metabolic subsystems. Vitamin D3 metabolism was predicted to be down-regulated in one dataset by GIMME, and in all by tINIT. Models generated by tINIT and GIMME predicted downregulation of retinol metabolism in different datasets, while downregulated cholesterol metabolism was predicted only by tINIT-generated models. Predictions are in line with the observations in COVID-19 patients. Our data indicated that GIMME and tINIT models provided the most biologically relevant results and should have a larger emphasis in further analyses. Particularly tINIT models identified the metabolic pathways that are a part of the host response and are potential antiviral targets. The code and the results of the analyses are available to download from https://github.com/CompBioLj/COVID_GEMs_and_MEMs. Twit Text FOAVip Integration of omics data to generate and analyse COVID-19 specific genome-scale metabolic models ????Comput Biol Med http://www.kidney.de/pget.php?&tw=1&pmid=35339845 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=35339845 #FOAvip English Wikipedia <ref>{{Cite pmid|35339845}}</ref> Integration of omics data to generate and analyse COVID-19 specific genome-scale metabolic models #MMPMID35339845 Rezen T; Martins A; Mraz M; Zimic N; Rozman D; Moskon M Comput Biol Med 2022[Jun]; 145 (ä): 105428 PMID35339845show ga COVID-19 presents a complex disease that needs to be addressed using systems medicine approaches that include genome-scale metabolic models (GEMs). Previous studies have used a single model extraction method (MEM) and/or a single transcriptomic dataset to reconstruct context-specific models, which proved to be insufficient for the broader biological contexts. We have applied four MEMs in combination with five COVID-19 datasets. Models produced by GIMME were separated by infection, while tINIT preserved the biological variability in the data and enabled the best prediction of the enrichment of metabolic subsystems. Vitamin D3 metabolism was predicted to be down-regulated in one dataset by GIMME, and in all by tINIT. Models generated by tINIT and GIMME predicted downregulation of retinol metabolism in different datasets, while downregulated cholesterol metabolism was predicted only by tINIT-generated models. Predictions are in line with the observations in COVID-19 patients. Our data indicated that GIMME and tINIT models provided the most biologically relevant results and should have a larger emphasis in further analyses. Particularly tINIT models identified the metabolic pathways that are a part of the host response and are potential antiviral targets. The code and the results of the analyses are available to download from https://github.com/CompBioLj/COVID_GEMs_and_MEMs. |*COVID-19/genetics[MESH] |Genome[MESH] |Humans[MESH] |Metabolic Networks and Pathways[MESH] |Models, Biological[MESH]Integration of omics data to generate and analyse COVID-19 specific ge(epmc).pdf DeepDyve Pubget Overpricing