Applying hybrid reasoning to mine for associative features in biological data.
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.
Galitsky
Boris A
BA
School of Computer Science and Information Systems, Birkbeck College, University of London, Malet Street, London WC1E 7HX, UK. galitsky@dcs.bbk.ac.uk
Kuznetsov
Sergey O
SO
Vinogradov
Dmitry V
DV
D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D019143:Evolution, Molecular; D006801:Humans; D007136:Immunoglobulins / Q000737:chemistry; D015233:Models, Statistical; D010802:Phylogeny; D011381:Programming Languages; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry; D012984:Software
D007136:Immunoglobulins; D011506:Proteins