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10.1038/s41598-022-08574-6 http://scihub22266oqcxt.onion/10.1038/s41598-022-08574-6 35393450!8988119!35393450     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 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 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Sci+Rep 2022 ; 12 (1): 5867 Nephropedia Template TP {{tp|p=35393450|t=2022. Comparing protein-protein interaction networks of SARS-CoV-2 and (H1N1) influenza using topological features |pdf=|usr=}}{{35393450}} gab.com Text Comparing protein-protein interaction networks of SARS-CoV-2 and (H1N1) influenza using topological features JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=35393450 #FOAvip SARS-CoV-2 pandemic first emerged in late 2019 in China. It has since infected more than 298 million individuals and caused over 5 million deaths globally. The identification of essential proteins in a protein-protein interaction network (PPIN) is not only crucial in understanding the process of cellular life but also useful in drug discovery. There are many centrality measures to detect influential nodes in complex networks. Since SARS-CoV-2 and (H1N1) influenza PPINs pose 553 common human proteins. Analyzing influential proteins and comparing these networks together can be an effective step in helping biologists for drug-target prediction. We used 21 centrality measures on SARS-CoV-2 and (H1N1) influenza PPINs to identify essential proteins. We applied principal component analysis and unsupervised machine learning methods to reveal the most informative measures. Appealingly, some measures had a high level of contribution in comparison to others in both PPINs, namely Decay, Residual closeness, Markov, Degree, closeness (Latora), Barycenter, Closeness (Freeman), and Lin centralities. We also investigated some graph theory-based properties like the power law, exponential distribution, and robustness. Both PPINs tended to properties of scale-free networks that expose their nature of heterogeneity. Dimensionality reduction and unsupervised learning methods were so effective to uncover appropriate centrality measures. Twit Text FOAVip Comparing protein-protein interaction networks of SARS-CoV-2 and (H1N1) influenza using topological features ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=35393450 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=35393450 #FOAvip English Wikipedia <ref>{{Cite pmid|35393450}}</ref> Comparing protein-protein interaction networks of SARS-CoV-2 and (H1N1) influenza using topological features #MMPMID35393450 Khojasteh H; Khanteymoori A; Olyaee MH Sci Rep 2022[Apr]; 12 (1): 5867 PMID35393450show ga SARS-CoV-2 pandemic first emerged in late 2019 in China. It has since infected more than 298 million individuals and caused over 5 million deaths globally. The identification of essential proteins in a protein-protein interaction network (PPIN) is not only crucial in understanding the process of cellular life but also useful in drug discovery. There are many centrality measures to detect influential nodes in complex networks. Since SARS-CoV-2 and (H1N1) influenza PPINs pose 553 common human proteins. Analyzing influential proteins and comparing these networks together can be an effective step in helping biologists for drug-target prediction. We used 21 centrality measures on SARS-CoV-2 and (H1N1) influenza PPINs to identify essential proteins. We applied principal component analysis and unsupervised machine learning methods to reveal the most informative measures. Appealingly, some measures had a high level of contribution in comparison to others in both PPINs, namely Decay, Residual closeness, Markov, Degree, closeness (Latora), Barycenter, Closeness (Freeman), and Lin centralities. We also investigated some graph theory-based properties like the power law, exponential distribution, and robustness. Both PPINs tended to properties of scale-free networks that expose their nature of heterogeneity. Dimensionality reduction and unsupervised learning methods were so effective to uncover appropriate centrality measures. |*COVID-19[MESH] |*Influenza A Virus, H1N1 Subtype/metabolism[MESH] |*Influenza, Human[MESH] |Humans[MESH] |Protein Interaction Maps[MESH] |Proteins/metabolism[MESH]Comparing protein-protein interaction networks of SARS-CoV-2 and (H1N1(epmc).pdf DeepDyve Pubget Overpricing