Rule Generation Using NN and GA for SARS-CoV Cleavage Site Prediction

Cleavage site prediction is an important issue in molecular biology. We present a new method that generates prediction rules for SARS-CoV protease cleavage sites. Our method includes rule extraction from a trained neural network and then enhancing the extracted rules by genetic evolution to improve...

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Detalles Bibliográficos
Autores principales: Cho, Yeon-Jin, Kim, Hyeoncheol
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2005
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7122303/
http://dx.doi.org/10.1007/11553939_111
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author Cho, Yeon-Jin
Kim, Hyeoncheol
author_facet Cho, Yeon-Jin
Kim, Hyeoncheol
author_sort Cho, Yeon-Jin
collection PubMed
description Cleavage site prediction is an important issue in molecular biology. We present a new method that generates prediction rules for SARS-CoV protease cleavage sites. Our method includes rule extraction from a trained neural network and then enhancing the extracted rules by genetic evolution to improve its quality. Experimental results show that the method could generate new rules for cleavage site prediction, which are more general and accurate than consensus patterns.
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spelling pubmed-71223032020-04-06 Rule Generation Using NN and GA for SARS-CoV Cleavage Site Prediction Cho, Yeon-Jin Kim, Hyeoncheol Knowledge-Based Intelligent Information and Engineering Systems Article Cleavage site prediction is an important issue in molecular biology. We present a new method that generates prediction rules for SARS-CoV protease cleavage sites. Our method includes rule extraction from a trained neural network and then enhancing the extracted rules by genetic evolution to improve its quality. Experimental results show that the method could generate new rules for cleavage site prediction, which are more general and accurate than consensus patterns. 2005 /pmc/articles/PMC7122303/ http://dx.doi.org/10.1007/11553939_111 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, Yeon-Jin
Kim, Hyeoncheol
Rule Generation Using NN and GA for SARS-CoV Cleavage Site Prediction
title Rule Generation Using NN and GA for SARS-CoV Cleavage Site Prediction
title_full Rule Generation Using NN and GA for SARS-CoV Cleavage Site Prediction
title_fullStr Rule Generation Using NN and GA for SARS-CoV Cleavage Site Prediction
title_full_unstemmed Rule Generation Using NN and GA for SARS-CoV Cleavage Site Prediction
title_short Rule Generation Using NN and GA for SARS-CoV Cleavage Site Prediction
title_sort rule generation using nn and ga for sars-cov cleavage site prediction
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7122303/
http://dx.doi.org/10.1007/11553939_111
work_keys_str_mv AT choyeonjin rulegenerationusingnnandgaforsarscovcleavagesiteprediction
AT kimhyeoncheol rulegenerationusingnnandgaforsarscovcleavagesiteprediction

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