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

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About: Rule Generation Using NN and GA for SARS-CoV Cleavage Site Prediction Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Rule Generation Using NN and GA for SARS-CoV Cleavage Site Prediction
Creator	Cho, Yeon-Jin Kim, Hyeoncheol
Source	PMC
abstract	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.
has issue date	2005-01-01(xsd:dateTime)
bibo:doi	10.1007/11553939_111
has license	no-cc
sha1sum (hex)	0ccf161ca222319eceb8fdb800f43faf16cb08dd
schema:url	https://doi.org/10.1007/11553939_111
resource representing a document's title	Rule Generation Using NN and GA for SARS-CoV Cleavage Site Prediction
has PubMed Central identifier	PMC7122303
schema:publication	Knowledge-Based Intelligent Information and Engineering Systems
resource representing a document's body	covid:0ccf161ca222319eceb8fdb800f43faf16cb08dd#body_text
is schema:about of	named entity 'SITE' named entity 'PREDICTION' named entity 'generate' named entity 'IMPORTANT' named entity 'GENERATE' named entity 'ACCURATE' named entity 'EXTRACTED' named entity 'GENERAL' named entity 'GENETIC' named entity 'GENERATION' named entity 'CLEAVAGE' named entity 'RULE' named entity 'SARS-COV' named entity 'CLEAVAGE' named entity 'METHOD' named entity 'SITE' named entity 'ITS' named entity 'CONSENSUS' named entity 'RULE' named entity 'USING' named entity 'TRAINED' named entity 'EXPERIMENTAL RESULTS' named entity 'EVOLUTION' named entity 'PROTEASE CLEAVAGE' named entity 'NEURAL NETWORK' named entity 'EXTRACTION' named entity 'INCLUDES' named entity 'NEW' named entity 'ISSUE' named entity 'OUR' named entity 'PRESENT' named entity 'MOLECULAR BIOLOGY' named entity 'RULES' named entity 'QUALITY' named entity 'PATTERNS' named entity 'PREDICTION' named entity 'SARS-COV' named entity 'ENHANCING' covid:arg/0ccf161ca222319eceb8fdb800f43faf16cb08dd named entity 'genetic evolution' named entity 'improve' named entity 'prediction' named entity 'Cleavage site' named entity 'molecular biology' named entity 'SARS-CoV' named entity 'SARS-CoV' named entity 'amino acids' named entity 'S/A' named entity 'genetic algorithms' named entity 'chromosomes' named entity 'Decision Tree' named entity 'neural network' named entity 'Alanine' named entity 'SARS' named entity 'neural network' named entity 'Mutation' named entity 'neural network training' named entity 'World Health Organization' named entity 'viruses' named entity 'decision tree' named entity 'pathology' named entity 'decision tree' named entity 'neural network' named entity 'neural network' named entity 'Alanine' named entity 'machine learning' named entity 'neural networks' named entity 'virus' named entity 'consensus patterns' named entity 'consensus patterns' named entity 'chromosome' named entity 'coronaviruses' named entity 'neural networks' named entity 'Genetic Algorithm' named entity 'evolution' named entity 'consensus patterns' named entity 'SARS-CoV' named entity 'rule extraction' named entity 'S/A' named entity 'protease' named entity 'genome' named entity 'neural network'

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