Cleavage site analysis using rule extraction from neural networks

Yeun Jin Cho, Hyeoncheol Kim

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

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • General Computer Science

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