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    Applying hybrid reasoning to mine for associative features in biological data

    Galitsky, Boris A. and Kuznetsov, S.O. and Vinogradov, D.V. (2007) Applying hybrid reasoning to mine for associative features in biological data. Journal of Biomedical Informatics 40 (3), 203 - 220. ISSN 1532-0464.

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    We develop the means to mine for associative features in biological data. The hybrid reasoning schema for deterministic machine learning and its implementation via logic programming is presented. The methodology of mining for correlation between features is illustrated by the prediction tasks for protein secondary structure and phylogenetic profiles. The suggested methodology leads to a clearer approach to hierarchical classification of proteins and a novel way to represent evolutionary relationships. Comparative analysis of Jasmine and other statistical and deterministic systems (including Explanation-Based Learning and Inductive Logic Programming) are outlined. Advantages of using deterministic versus statistical data mining approaches for high-level exploration of correlation structure are analyzed.


    Item Type: Article
    Keyword(s) / Subject(s): Deterministic machine learning, plausible reasoning, causal link, protein secondary structure prediction, phylogenetic profile analysis
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Depositing User: Administrator
    Date Deposited: 04 Aug 2011 12:50
    Last Modified: 09 Aug 2023 12:30


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