Cleavage site analysis using rule extraction from neural networks

Yeun Jin Cho; Hyeoncheol Kim

Cleavage site analysis using rule extraction from neural networks

Research output: Contribution to journal › Conference article

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
Publication status	Published - 2005 Oct 24
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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Cite this

Cho, Y. J., & Kim, H. (2005). Cleavage site analysis using rule extraction from neural networks. Lecture Notes in Computer Science, 3610(PART I), 1002-1008.

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