Author: Cho, Yeon-Jin; Kim, Hyeoncheol
Title: Rule Generation Using NN and GA for SARS-CoV Cleavage Site Prediction Cord-id: my2l85bn Document date: 2005_1_1
ID: my2l85bn
Snippet: 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.
Document: 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.
Search related documents:
Co phrase search for related documents- accuracy coverage and machine learning: 1
- accurate complete and acute respiratory syndrome: 1, 2, 3, 4, 5, 6, 7
- accurate complete and machine learning: 1, 2, 3
- accurate complete and machine learning method: 1
- acute respiratory syndrome and machine learning: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25
- acute respiratory syndrome and machine learning method: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
Co phrase search for related documents, hyperlinks ordered by date