Repulsive parallel MCMC algorithm for discovering diverse motifs from large sequence sets-Reference-Cited by-同舟云学术

Repulsive parallel MCMC algorithm for discovering diverse motifs from large sequence sets

Published:2015-01-11 Issue:10 Volume:31 Page:1561-1568
ISSN:1367-4803
Container-title:Bioinformatics
language:en
Short-container-title:

Author:

Ikebata Hisaki¹,Yoshida Ryo¹²³⁴⁵

Affiliation:

1. Department of Statistical Science, The Graduate University for Advanced Studies (Sokendai), 10-3 Midori-cho, Tachikawa, Tokyo, Japan

2. Department of Statistical Modeling, The Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo, Japan

3. JST-CREST, 10-3 Midori-cho, Tachikawa, Tokyo, Japan

4. JST-ERATO Sato Live Bio-Forecasting Project, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Khoto-fu, Japan

5. The Thomas N. Sato BioMEC-X Laboratories, Advanced Telecommunications Research Institute International, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Khoto-fu, Japan

Funder

Japan Science and Technology Agency

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Link

http://academic.oup.com/bioinformatics/article-pdf/31/10/1561/25115055/btv017.pdf

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4. Markov chain Monte Carlo convergence diagnostics: a comparative review;Cowles;J. Am. Stat. Assoc.,1996

5. Efficient representation and P-value computation for high-order Markov motifs;da Fonseca;Bioinformatics,2008

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