Affiliations: College of Computer Science and Technology, Jilin
University, 130012, Changchun, P.R. China | School of Computer Science, The Queen's University of
Belfast, Belfast BT7 1NN, Northern Ireland, U.K.
Abstract: Large collections of genomic information have been accumulated in
recent years, and embedded latently in them is potentially significant
knowledge for exploitation in medicine and in the pharmaceutical industry. The
approach taken here to the distillation of such knowledge is to detect strings
in DNA sequences which appear frequently, either within a given sequence (e.g.,
for a particular patient) or across sequences (e.g., from different patients
sharing a particular medical diagnosis). Motifs are strings that occur very
frequently. We present basic theory and algorithms for finding very frequent
and common strings. Strings which are maximally frequent are of particular
interest and, having discovered such motifs, we show briefly how to mine
association rules by an existing rough sets based technique. Further work and
applications are in progress.
Keywords: rough sets, data mining, knowledge discovery in databases, DNA, bioinformatics
