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.
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.
|Keyword(s) / Subject(s):||Deterministic machine learning, plausible reasoning, causal link, protein secondary structure prediction, phylogenetic profile analysis|
|School:||Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems|
|Date Deposited:||04 Aug 2011 12:50|
|Last Modified:||17 Apr 2013 12:21|
Additional statistics are available via IRStats2.