Applying hybrid reasoning to mine for associative features in biological data

Authors:
Boris A. Galitsky;Sergey O. Kuznetsov;Dmitry V. Vinogradov
Affiliations:
School of Computer Science and Information Systems, Birkbeck College, University of London, Malet Street, London WC1E 7HX, UK;All-Russian Institute for Scientific and Technical Information (VINITI), Usievicha 20, Moscow 125190, Russia;All-Russian Institute for Scientific and Technical Information (VINITI), Usievicha 20, Moscow 125190, Russia
Venue:
Journal of Biomedical Informatics
Year:
2007

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Abstract

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.