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

Yeun Jin Cho^*
, Hyeoncheol Kim

^*Corresponding author for this work

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
General Computer Science

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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",

year = "2005",

doi = "10.1007/11539087\_132",

language = "English",

volume = "3610",

pages = "1002--1008",

journal = "Lecture Notes in Computer Science",

issn = "0302-9743",

publisher = "Springer Science and Business Media Deutschland GmbH",

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note = "First International Conference on Natural Computation, ICNC 2005 ; Conference date: 27-08-2005 Through 29-08-2005",

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T1 - Cleavage site analysis using rule extraction from neural networks

AU - Cho, Yeun Jin

AU - Kim, Hyeoncheol

PY - 2005

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AB - 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.

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U2 - 10.1007/11539087_132

DO - 10.1007/11539087_132

M3 - Conference article

AN - SCOPUS:26844465130

SN - 0302-9743

VL - 3610

SP - 1002

EP - 1008

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

IS - PART I

T2 - First International Conference on Natural Computation, ICNC 2005

Y2 - 27 August 2005 through 29 August 2005

ER -

Cleavage site analysis using rule extraction from neural networks

Abstract

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