Cleavage site analysis using rule extraction from neural networks

Yeun Jin Cho, Hyeoncheol Kim

Research output: Contribution to journalConference 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 languageEnglish
Pages (from-to)1002-1008
Number of pages7
JournalLecture Notes in Computer Science
Volume3610
Issue numberPART I
Publication statusPublished - 2005 Oct 24
EventFirst International Conference on Natural Computation, ICNC 2005 - Changsha, China
Duration: 2005 Aug 272005 Aug 29

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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