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10.1016/j.bj.2020.08.003 http://scihub22266oqcxt.onion/10.1016/j.bj.2020.08.003 33036956!7470713!33036956     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 245.2 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 Deprecated: Implicit conversion from float 245.2 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       Biomed+J 2020 ; 43 (5): 438-450 Nephropedia Template TP {{tp|p=33036956|t=2020. Machine learning techniques for sequence-based prediction of viral-host interactions between SARS-CoV-2 and human proteins |pdf=|usr=}}{{33036956}} gab.com Text Machine learning techniques for sequence-based prediction of viral-host interactions between SARS-CoV-2 and human proteins JO: Biomed J http://www.kidney.de/pget.php?&tw=1&pmid=33036956 #FOAvip BACKGROUND: COVID-19 (Coronavirus Disease-19), a disease caused by the SARS-CoV-2 virus, has been declared as a pandemic by the World Health Organization on March 11, 2020. Over 15 million people have already been affected worldwide by COVID-19, resulting in more than 0.6 million deaths. Protein-protein interactions (PPIs) play a key role in the cellular process of SARS-CoV-2 virus infection in the human body. Recently a study has reported some SARS-CoV-2 proteins that interact with several human proteins while many potential interactions remain to be identified. METHOD: In this article, various machine learning models are built to predict the PPIs between the virus and human proteins that are further validated using biological experiments. The classification models are prepared based on different sequence-based features of human proteins like amino acid composition, pseudo amino acid composition, and conjoint triad. RESULT: We have built an ensemble voting classifier using SVM(Radial), SVM(Polynomial), and Random Forest technique that gives a greater accuracy, precision, specificity, recall, and F1 score compared to all other models used in the work. A total of 1326 potential human target proteins of SARS-CoV-2 have been predicted by the proposed ensemble model and validated using gene ontology and KEGG pathway enrichment analysis. Several repurposable drugs targeting the predicted interactions are also reported. CONCLUSION: This study may encourage the identification of potential targets for more effective anti-COVID drug discovery. Twit Text FOAVip Machine learning techniques for sequence-based prediction of viral-host interactions between SARS-CoV-2 and human proteins ????Biomed J http://www.kidney.de/pget.php?&tw=1&pmid=33036956 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33036956 #FOAvip English Wikipedia <ref>{{Cite pmid|33036956}}</ref> Machine learning techniques for sequence-based prediction of viral-host interactions between SARS-CoV-2 and human proteins #MMPMID33036956 Dey L; Chakraborty S; Mukhopadhyay A Biomed J 2020[Oct]; 43 (5): 438-450 PMID33036956show ga BACKGROUND: COVID-19 (Coronavirus Disease-19), a disease caused by the SARS-CoV-2 virus, has been declared as a pandemic by the World Health Organization on March 11, 2020. Over 15 million people have already been affected worldwide by COVID-19, resulting in more than 0.6 million deaths. Protein-protein interactions (PPIs) play a key role in the cellular process of SARS-CoV-2 virus infection in the human body. Recently a study has reported some SARS-CoV-2 proteins that interact with several human proteins while many potential interactions remain to be identified. METHOD: In this article, various machine learning models are built to predict the PPIs between the virus and human proteins that are further validated using biological experiments. The classification models are prepared based on different sequence-based features of human proteins like amino acid composition, pseudo amino acid composition, and conjoint triad. RESULT: We have built an ensemble voting classifier using SVM(Radial), SVM(Polynomial), and Random Forest technique that gives a greater accuracy, precision, specificity, recall, and F1 score compared to all other models used in the work. A total of 1326 potential human target proteins of SARS-CoV-2 have been predicted by the proposed ensemble model and validated using gene ontology and KEGG pathway enrichment analysis. Several repurposable drugs targeting the predicted interactions are also reported. CONCLUSION: This study may encourage the identification of potential targets for more effective anti-COVID drug discovery. |*Host Microbial Interactions[MESH] |*Machine Learning[MESH] |COVID-19/diagnosis/*virology[MESH] |Humans[MESH] |Proteins/*metabolism[MESH] |SARS-CoV-2[MESH]Machine learning techniques for sequence-based prediction of viral-hos(epmc).pdf DeepDyve Pubget Overpricing