Cleavage site analysis using rule extraction from neural networks

Yeun Jin Cho; Hyeoncheol Kim

doi:10.1007/11539087_132

Cleavage site analysis using rule extraction from neural networks

Research output: Contribution to journal › Conference article › peer-review

Abstract

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.

Original language	English
Pages (from-to)	1002-1008
Number of pages	7
Journal	Lecture Notes in Computer Science
Volume	3610
Issue number	PART I
DOIs	https://doi.org/10.1007/11539087_132
Publication status	Published - 2005
Event	First International Conference on Natural Computation, ICNC 2005 - Changsha, China Duration: 2005 Aug 27 → 2005 Aug 29

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

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10.1007/11539087_132

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title = "Cleavage site analysis using rule extraction from neural networks",

abstract = "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.",

author = "Cho, {Yeun Jin} and Hyeoncheol Kim",

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JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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