Abstract
Sdiscover is a tool capable of finding subsequences, possibly separated by arbitrarily long gaps, in a set of sequences. These subsequences are referred to as motifs. This paper proposes a method to evaluate the significance of the sequence motifs found by Sdiscover. The method is based on the minimum description length principle and Shannon's coding theory. The equivalence of the proposed method to the Bayesian inference is also discussed.
Original language | English (US) |
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Title of host publication | Proceedings of the Fifth Joint Conference on Information Sciences, JCIS 2000, Volume 2 |
Editors | P.P. Wang, P.P. Wang |
Pages | 798-801 |
Number of pages | 4 |
Volume | 5 |
Edition | 2 |
State | Published - Dec 1 2000 |
Event | Proceedings of the Fifth Joint Conference on Information Sciences, JCIS 2000 - Atlantic City, NJ, United States Duration: Feb 27 2000 → Mar 3 2000 |
Other
Other | Proceedings of the Fifth Joint Conference on Information Sciences, JCIS 2000 |
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Country/Territory | United States |
City | Atlantic City, NJ |
Period | 2/27/00 → 3/3/00 |
All Science Journal Classification (ASJC) codes
- Computer Science(all)