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Identification of encoding proteins related to SARS-CoV
By sampling 100 encoding proteins from SARS-coronavirus (SARS-CoV, NC 004718) and other six coronaviruses and selecting 23 variables through stepwise multiple regression (SMR) from 172 variables, the multiple linear regression (MLR) model was established with good results of the quantitative modelli...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Science in China Press
2004
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7089014/ https://www.ncbi.nlm.nih.gov/pubmed/32214714 http://dx.doi.org/10.1360/03wb0198 |
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author | Mei, Hu Sun, Lili Zhou, Yuan Xiong, Qing Li, Zhiliang |
author_facet | Mei, Hu Sun, Lili Zhou, Yuan Xiong, Qing Li, Zhiliang |
author_sort | Mei, Hu |
collection | PubMed |
description | By sampling 100 encoding proteins from SARS-coronavirus (SARS-CoV, NC 004718) and other six coronaviruses and selecting 23 variables through stepwise multiple regression (SMR) from 172 variables, the multiple linear regression (MLR) model was established with good results of the quantitative modelling correlation coefficient R (2) = 0.645 and the cross-validation correlation coefficient R (CV)(2) = 0.375. After removing 4 outliers, the quantitative modelling and cross-validation correlation coefficients were R (2)= 0.743 and R (CV)(2) = 0.543, respectively. |
format | Online Article Text |
id | pubmed-7089014 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2004 |
publisher | Science in China Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-70890142020-03-23 Identification of encoding proteins related to SARS-CoV Mei, Hu Sun, Lili Zhou, Yuan Xiong, Qing Li, Zhiliang Chin Sci Bull Articles By sampling 100 encoding proteins from SARS-coronavirus (SARS-CoV, NC 004718) and other six coronaviruses and selecting 23 variables through stepwise multiple regression (SMR) from 172 variables, the multiple linear regression (MLR) model was established with good results of the quantitative modelling correlation coefficient R (2) = 0.645 and the cross-validation correlation coefficient R (CV)(2) = 0.375. After removing 4 outliers, the quantitative modelling and cross-validation correlation coefficients were R (2)= 0.743 and R (CV)(2) = 0.543, respectively. Science in China Press 2004 /pmc/articles/PMC7089014/ /pubmed/32214714 http://dx.doi.org/10.1360/03wb0198 Text en © Science in China Press 2004 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Articles Mei, Hu Sun, Lili Zhou, Yuan Xiong, Qing Li, Zhiliang Identification of encoding proteins related to SARS-CoV |
title | Identification of encoding proteins related to SARS-CoV |
title_full | Identification of encoding proteins related to SARS-CoV |
title_fullStr | Identification of encoding proteins related to SARS-CoV |
title_full_unstemmed | Identification of encoding proteins related to SARS-CoV |
title_short | Identification of encoding proteins related to SARS-CoV |
title_sort | identification of encoding proteins related to sars-cov |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7089014/ https://www.ncbi.nlm.nih.gov/pubmed/32214714 http://dx.doi.org/10.1360/03wb0198 |
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