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Cleavage Site Analysis Using Rule Extraction from Neural Networks
In this paper, we demonstrate that the machine learning approach of rule extraction from a trained neural network can be successfully applied to SARS-coronavirus cleavage site analysis. The extracted rules predict cleavage sites better than consensus patterns. Empirical experiments are also shown.
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2005
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7114972/ http://dx.doi.org/10.1007/11539087_132 |
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author | Cho, Yeun-Jin Kim, Hyeoncheol |
author_facet | Cho, Yeun-Jin Kim, Hyeoncheol |
author_sort | Cho, Yeun-Jin |
collection | PubMed |
description | In this paper, we demonstrate that the machine learning approach of rule extraction from a trained neural network can be successfully applied to SARS-coronavirus cleavage site analysis. The extracted rules predict cleavage sites better than consensus patterns. Empirical experiments are also shown. |
format | Online Article Text |
id | pubmed-7114972 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2005 |
record_format | MEDLINE/PubMed |
spelling | pubmed-71149722020-04-02 Cleavage Site Analysis Using Rule Extraction from Neural Networks Cho, Yeun-Jin Kim, Hyeoncheol Advances in Natural Computation Article In this paper, we demonstrate that the machine learning approach of rule extraction from a trained neural network can be successfully applied to SARS-coronavirus cleavage site analysis. The extracted rules predict cleavage sites better than consensus patterns. Empirical experiments are also shown. 2005 /pmc/articles/PMC7114972/ http://dx.doi.org/10.1007/11539087_132 Text en © Springer-Verlag Berlin Heidelberg 2005 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 | Article Cho, Yeun-Jin Kim, Hyeoncheol Cleavage Site Analysis Using Rule Extraction from Neural Networks |
title | Cleavage Site Analysis Using Rule Extraction from Neural Networks |
title_full | Cleavage Site Analysis Using Rule Extraction from Neural Networks |
title_fullStr | Cleavage Site Analysis Using Rule Extraction from Neural Networks |
title_full_unstemmed | Cleavage Site Analysis Using Rule Extraction from Neural Networks |
title_short | Cleavage Site Analysis Using Rule Extraction from Neural Networks |
title_sort | cleavage site analysis using rule extraction from neural networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7114972/ http://dx.doi.org/10.1007/11539087_132 |
work_keys_str_mv | AT choyeunjin cleavagesiteanalysisusingruleextractionfromneuralnetworks AT kimhyeoncheol cleavagesiteanalysisusingruleextractionfromneuralnetworks |