Evaluating the Significance of Sequence Motifs by the Minimum Description Length Principle

Qicheng Ma; Jason Wang

Evaluating the Significance of Sequence Motifs by the Minimum Description Length Principle

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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)
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
Country/Territory	United States
City	Atlantic City, NJ
Period	2/27/00 → 3/3/00

All Science Journal Classification (ASJC) codes

Computer Science(all)

Cite this

@inproceedings{a4cbf4bd682c44ad97b5a78959282d8c,

title = "Evaluating the Significance of Sequence Motifs by the Minimum Description Length Principle",

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.",

author = "Qicheng Ma and Jason Wang",

year = "2000",

month = dec,

day = "1",

language = "English (US)",

isbn = "0964345692",

volume = "5",

pages = "798--801",

editor = "P.P. Wang and P.P. Wang",

booktitle = "Proceedings of the Fifth Joint Conference on Information Sciences, JCIS 2000, Volume 2",

edition = "2",

note = "Proceedings of the Fifth Joint Conference on Information Sciences, JCIS 2000 ; Conference date: 27-02-2000 Through 03-03-2000",

}

Ma, Q & Wang, J 2000, Evaluating the Significance of Sequence Motifs by the Minimum Description Length Principle. in PP Wang & PP Wang (eds), Proceedings of the Fifth Joint Conference on Information Sciences, JCIS 2000, Volume 2. 2 edn, vol. 5, pp. 798-801, Proceedings of the Fifth Joint Conference on Information Sciences, JCIS 2000, Atlantic City, NJ, United States, 2/27/00.

TY - GEN

T1 - Evaluating the Significance of Sequence Motifs by the Minimum Description Length Principle

AU - Ma, Qicheng

AU - Wang, Jason

PY - 2000/12/1

Y1 - 2000/12/1

N2 - 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.

AB - 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.

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UR - http://www.scopus.com/inward/citedby.url?scp=1642283251&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:1642283251

SN - 0964345692

SN - 9780964345690

VL - 5

SP - 798

EP - 801

BT - Proceedings of the Fifth Joint Conference on Information Sciences, JCIS 2000, Volume 2

A2 - Wang, P.P.

T2 - Proceedings of the Fifth Joint Conference on Information Sciences, JCIS 2000

Y2 - 27 February 2000 through 3 March 2000

ER -

Evaluating the Significance of Sequence Motifs by the Minimum Description Length Principle

Abstract

Other

All Science Journal Classification (ASJC) codes

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