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10.1093/bib/bbaa259 http://scihub22266oqcxt.onion/10.1093/bib/bbaa259 33034338!7665369!33034338     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Brief+Bioinform 2021 ; 22 (2): 936-945 Nephropedia Template TP {{tp|p=33034338|t=2021. Computer-aided prediction and design of IL-6 inducing peptides: IL-6 plays a crucial role in COVID-19 |pdf=|usr=}}{{33034338}} gab.com Text Computer-aided prediction and design of IL-6 inducing peptides: IL-6 plays a crucial role in COVID-19 JO: Brief Bioinform http://www.kidney.de/pget.php?&tw=1&pmid=33034338 #FOAvip Interleukin 6 (IL-6) is a pro-inflammatory cytokine that stimulates acute phase responses, hematopoiesis and specific immune reactions. Recently, it was found that the IL-6 plays a vital role in the progression of COVID-19, which is responsible for the high mortality rate. In order to facilitate the scientific community to fight against COVID-19, we have developed a method for predicting IL-6 inducing peptides/epitopes. The models were trained and tested on experimentally validated 365 IL-6 inducing and 2991 non-inducing peptides extracted from the immune epitope database. Initially, 9149 features of each peptide were computed using Pfeature, which were reduced to 186 features using the SVC-L1 technique. These features were ranked based on their classification ability, and the top 10 features were used for developing prediction models. A wide range of machine learning techniques has been deployed to develop models. Random Forest-based model achieves a maximum AUROC of 0.84 and 0.83 on training and independent validation dataset, respectively. We have also identified IL-6 inducing peptides in different proteins of SARS-CoV-2, using our best models to design vaccine against COVID-19. A web server named as IL-6Pred and a standalone package has been developed for predicting, designing and screening of IL-6 inducing peptides (https://webs.iiitd.edu.in/raghava/il6pred/). Twit Text FOAVip Computer-aided prediction and design of IL-6 inducing peptides: IL-6 plays a crucial role in COVID-19 ????Brief Bioinform http://www.kidney.de/pget.php?&tw=1&pmid=33034338 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33034338 #FOAvip English Wikipedia <ref>{{Cite pmid|33034338}}</ref> Computer-aided prediction and design of IL-6 inducing peptides: IL-6 plays a crucial role in COVID-19 #MMPMID33034338 Dhall A; Patiyal S; Sharma N; Usmani SS; Raghava GPS Brief Bioinform 2021[Mar]; 22 (2): 936-945 PMID33034338show ga Interleukin 6 (IL-6) is a pro-inflammatory cytokine that stimulates acute phase responses, hematopoiesis and specific immune reactions. Recently, it was found that the IL-6 plays a vital role in the progression of COVID-19, which is responsible for the high mortality rate. In order to facilitate the scientific community to fight against COVID-19, we have developed a method for predicting IL-6 inducing peptides/epitopes. The models were trained and tested on experimentally validated 365 IL-6 inducing and 2991 non-inducing peptides extracted from the immune epitope database. Initially, 9149 features of each peptide were computed using Pfeature, which were reduced to 186 features using the SVC-L1 technique. These features were ranked based on their classification ability, and the top 10 features were used for developing prediction models. A wide range of machine learning techniques has been deployed to develop models. Random Forest-based model achieves a maximum AUROC of 0.84 and 0.83 on training and independent validation dataset, respectively. We have also identified IL-6 inducing peptides in different proteins of SARS-CoV-2, using our best models to design vaccine against COVID-19. A web server named as IL-6Pred and a standalone package has been developed for predicting, designing and screening of IL-6 inducing peptides (https://webs.iiitd.edu.in/raghava/il6pred/). |*Computer Simulation[MESH] |COVID-19/*physiopathology/virology[MESH] |Databases, Protein[MESH] |Datasets as Topic[MESH] |Humans[MESH] |Interleukin-6/*biosynthesis/physiology[MESH] |Machine Learning[MESH] |Peptides/*metabolism[MESH]Computer-aided prediction and design of IL-6 inducing peptides: IL-6 p(epmc).pdf DeepDyve Pubget Overpricing