Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Sanguinetti, Guido

6 Issues per year


IMPACT FACTOR 2017: 0.812
5-year IMPACT FACTOR: 1.104

CiteScore 2017: 0.86

SCImago Journal Rank (SJR) 2017: 0.456
Source Normalized Impact per Paper (SNIP) 2017: 0.527

Mathematical Citation Quotient (MCQ) 2017: 0.04

Online
ISSN
1544-6115
See all formats and pricing
More options …
Volume 8, Issue 1

Issues

Volume 10 (2011)

Volume 9 (2010)

Volume 6 (2007)

Volume 5 (2006)

Volume 4 (2005)

Volume 2 (2003)

Volume 1 (2002)

Inferring Dynamic Genetic Networks with Low Order Independencies

Sophie Lèbre
Published Online: 2009-02-04 | DOI: https://doi.org/10.2202/1544-6115.1294

In this paper, we introduce a novel inference method for dynamic genetic networks which makes it possible to face a number of time measurements n that is much smaller than the number of genes p. The approach is based on the concept of a low order conditional dependence graph that we extend here in the case of dynamic Bayesian networks. Most of our results are based on the theory of graphical models associated with the directed acyclic graphs (DAGs). In this way, we define a minimal DAG G which describes exactly the full order conditional dependencies given in the past of the process. Then, to face with the large p and small n estimation case, we propose to approximate DAG G by considering low order conditional independencies. We introduce partial qth order conditional dependence DAGs G(q) and analyze their probabilistic properties. In general, DAGs G(q) differ from DAG G but still reflect relevant dependence facts for sparse networks such as genetic networks. By using this approximation, we set out a non-Bayesian inference method and demonstrate the effectiveness of this approach on both simulated and real data analysis. The inference procedure is implemented in the R package 'G1DBN' freely available from the R archive (CRAN).

Keywords: dynamic Bayesian networks; graphical modeling; directed acyclic graphs; conditional independence; networks inference; time series modeling

About the article

Published Online: 2009-02-04


Citation Information: Statistical Applications in Genetics and Molecular Biology, Volume 8, Issue 1, Pages 1–38, ISSN (Online) 1544-6115, DOI: https://doi.org/10.2202/1544-6115.1294.

Export Citation

©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston.Get Permission

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

[2]
Genevieve Konopka
Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 2011, Volume 3, Number 6, Page 628
[3]
Satoru Koda, Yoshihiko Onda, Hidetoshi Matsui, Kotaro Takahagi, Yukiko Yamaguchi-Uehara, Minami Shimizu, Komaki Inoue, Takuhiro Yoshida, Tetsuya Sakurai, Hiroshi Honda, Shinto Eguchi, Ryuei Nishii, and Keiichi Mochida
Frontiers in Plant Science, 2017, Volume 8
[4]
Takanori Hasegawa, Tomoya Mori, Rui Yamaguchi, Teppei Shimamura, Satoru Miyano, Seiya Imoto, and Tatsuya Akutsu
BMC Systems Biology, 2015, Volume 9, Number 1, Page 14
[5]
Mingyi Wang, Vagner Augusto Benedito, Patrick Xuechun Zhao, and Michael Udvardi
Molecular BioSystems, 2010, Volume 6, Number 6, Page 988
[6]
Teppei Shimamura, Seiya Imoto, Rui Yamaguchi, Masao Nagasaki, and Satoru Miyano
Bioinformatics, 2010, Volume 26, Number 8, Page 1064
[7]
Steven M. Hill, Yiling Lu, Jennifer Molina, Laura M. Heiser, Paul T. Spellman, Terence P. Speed, Joe W. Gray, Gordon B. Mills, and Sach Mukherjee
Bioinformatics, 2012, Volume 28, Number 21, Page 2804
[8]
Vân Anh Huynh-Thu and Guido Sanguinetti
Bioinformatics, 2015, Volume 31, Number 10, Page 1614
[9]
Ying Wang, Christopher A. Penfold, David A. Hodgson, Miriam L. Gifford, and Nigel J. Burroughs
Bioinformatics, 2014, Volume 30, Number 19, Page 2779
[10]
Andrei S Rodin, Grigoriy Gogoshin, and Eric Boerwinkle
Pharmacogenomics, 2011, Volume 12, Number 9, Page 1349
[11]
Minzhe Guo, Hui Wang, S. Steven Potter, Jeffrey A. Whitsett, Yan Xu, and Andreas Prlic
PLOS Computational Biology, 2015, Volume 11, Number 11, Page e1004575
[12]
Thomas Thorne, Pietro Fratta, Michael G. Hanna, Andrea Cortese, Vincent Plagnol, Elizabeth M. Fisher, and Michael P. H. Stumpf
Molecular BioSystems, 2013, Volume 9, Number 7, Page 1736
[13]
Luis F. Iglesias-Martinez, Walter Kolch, and Tapesh Santra
Scientific Reports, 2016, Volume 6, Number 1
[14]
Yinyin Yuan, Chang-Tsun Li, Oliver Windram, and Diego Di Bernardo
PLoS ONE, 2011, Volume 6, Number 4, Page e16835
[16]
Takanori Hasegawa, Rui Yamaguchi, Masao Nagasaki, Satoru Miyano, Seiya Imoto, and Frank Emmert-Streib
PLoS ONE, 2014, Volume 9, Number 8, Page e105942
[17]
Alberto Roverato and Robert Castelo
International Journal of Approximate Reasoning, 2012, Volume 53, Number 9, Page 1326
[18]
Jian Fang, Dongdong Lin, S. Charles Schulz, Zongben Xu, Vince D. Calhoun, and Yu-Ping Wang
Bioinformatics, 2016, Page btw485
[19]
Yong Wang, Rui Jiang, and Wing Hung Wong
National Science Review, 2016, Volume 3, Number 2, Page 240
[20]
Lei Du, Heng Huang, Jingwen Yan, Sungeun Kim, Shannon L. Risacher, Mark Inlow, Jason H. Moore, Andrew J. Saykin, and Li Shen
Bioinformatics, 2016, Volume 32, Number 10, Page 1544
[21]
Shengxiang Yang, Arinze Akutekwe, and Huseyin Seker
IET Systems Biology, 2015, Volume 9, Number 6, Page 294
[22]
Hangzhou Wang, Bo Chen, Zongmei Lu, and Faisal Khan
IFAC-PapersOnLine, 2015, Volume 48, Number 21, Page 826
[24]
Benoît Liquet, Pierre Lafaye de Micheaux, Boris P. Hejblum, and Rodolphe Thiébaut
Bioinformatics, 2015, Page btv535
[25]
Néhémy Lim, Florence d’Alché-Buc, Cédric Auliac, and George Michailidis
Machine Learning, 2015, Volume 99, Number 3, Page 489
[26]
Spencer Angus Thomas and Yaochu Jin
Evolutionary Intelligence, 2014, Volume 7, Number 1, Page 29

Comments (0)

Please log in or register to comment.
Log in