Abstract
In the large collections of genomic information accumulated in recent years there is potentially significant knowledge for exploitation in medicine and in the pharmaceutical industry. One interesting approach to the distillation of such knowledge is to detect strings in DNA sequences which are very repetitive within a given sequence (eg for a particular patient) or across sequences (eg from different patients who have been classified in some way eg as sharing a particular medical diagnosis). Motifs are strings that occur relatively frequently.
In this paper we present basic theory and algorithms for finding such frequent and common strings. We are particularly interested in strings which are maximally frequent and, having discovered very frequent motifs we show how to mine association rules by an existing rough sets based technique. Further work and applications are in process.
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Bell, D.A., Guan, J.W. (2003). Data Mining for Motifs in DNA Sequences. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2003. Lecture Notes in Computer Science(), vol 2639. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39205-X_85
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DOI: https://doi.org/10.1007/3-540-39205-X_85
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