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10.1038/s41598-021-90556-1 http://scihub22266oqcxt.onion/10.1038/s41598-021-90556-1 34045524!8160150!34045524     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       Sci+Rep 2021 ; 11 (1): 11241 Nephropedia Template TP {{tp|p=34045524|t=2021. Dynamic changes in gene-to-gene regulatory networks in response to SARS-CoV-2 infection |pdf=|usr=}}{{34045524}} gab.com Text Dynamic changes in gene-to-gene regulatory networks in response to SARS-CoV-2 infection JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=34045524 #FOAvip The current pandemic of SARS-CoV-2 has caused extensive damage to society. The characterization of SARS-CoV-2 profiles has been addressed by researchers globally with the aim of resolving this disruptive crisis. This investigation process is indispensable to understand how SARS-CoV-2 behaves in human host cells. However, little is known about the systematic molecular mechanisms involved in the effects of SARS-CoV-2 infection on human host cells. Here, we present gene-to-gene regulatory networks in response to SARS-CoV-2 using a Bayesian network. We examined the dynamic changes in the SARS-CoV-2-purturbated networks established by our proposed framework for gene network analysis, thus revealing that interferon signaling gradually switched to the subsequent inflammatory cytokine signaling cascades. Furthermore, we succeeded in capturing a COVID-19 patient-specific network in which transduction of these signals was concurrently induced. This enabled us to explore the local regulatory systems influenced by SARS-CoV-2 in host cells more precisely at an individual level. Our panel of network analyses has provided new insights into SARS-CoV-2 research from the perspective of cellular systems. Twit Text FOAVip Dynamic changes in gene-to-gene regulatory networks in response to SARS-CoV-2 infection ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=34045524 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34045524 #FOAvip English Wikipedia <ref>{{Cite pmid|34045524}}</ref> Dynamic changes in gene-to-gene regulatory networks in response to SARS-CoV-2 infection #MMPMID34045524 Tanaka Y; Higashihara K; Nakazawa MA; Yamashita F; Tamada Y; Okuno Y Sci Rep 2021[May]; 11 (1): 11241 PMID34045524show ga The current pandemic of SARS-CoV-2 has caused extensive damage to society. The characterization of SARS-CoV-2 profiles has been addressed by researchers globally with the aim of resolving this disruptive crisis. This investigation process is indispensable to understand how SARS-CoV-2 behaves in human host cells. However, little is known about the systematic molecular mechanisms involved in the effects of SARS-CoV-2 infection on human host cells. Here, we present gene-to-gene regulatory networks in response to SARS-CoV-2 using a Bayesian network. We examined the dynamic changes in the SARS-CoV-2-purturbated networks established by our proposed framework for gene network analysis, thus revealing that interferon signaling gradually switched to the subsequent inflammatory cytokine signaling cascades. Furthermore, we succeeded in capturing a COVID-19 patient-specific network in which transduction of these signals was concurrently induced. This enabled us to explore the local regulatory systems influenced by SARS-CoV-2 in host cells more precisely at an individual level. Our panel of network analyses has provided new insights into SARS-CoV-2 research from the perspective of cellular systems. |*Gene Regulatory Networks[MESH] |Bayes Theorem[MESH] |COVID-19/genetics/*metabolism/virology[MESH] |Cell Line[MESH] |Computational Biology[MESH] |Databases, Genetic[MESH] |Humans[MESH] |RNA-Seq[MESH] |SARS-CoV-2/genetics/*metabolism[MESH] |Signal Transduction/*genetics[MESH]Dynamic changes in gene-to-gene regulatory networks in response to SAR(epmc).pdf DeepDyve Pubget Overpricing