Identifying gene regulatory networks from experimental data

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback
Take a look at the new version of this page: [ beta version ]. Tell us what you think.

Identifying gene regulatory networks from experimental data

Full text

Pdf (1.11 MB)

Source

Annual Conference on Research in Computational Molecular Biology archive
Proceedings of the third annual international conference on Computational molecular biology table of contents

Lyon, France

Pages: 94 - 103

Year of Publication: 1999

ISBN:1-58113-069-4

Authors

Ting Chen	Department of Genetics, Harvard Medical School, 200 Longwood Avenue, Boston, MA
Vladimir Filkov	Department of Computer Science, SUNY Stony Brook, Stony Brook, NY
Steven S. Skiena	Department of Computer Science, SUNY Stony Brook, Stony Brook, NY

Sponsors

INRIA : Institut Natl de Recherche en Info et en Automatique
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 3, Downloads (12 Months): 47, Citation Count: 10

Additional Information:

references cited by index terms collaborative colleagues

Tools and Actions:	Request Permissions Review this Article Save this Article to a Binder Display Formats: BibTeX EndNote ACM Ref

DOI Bookmark:	Use this link to bookmark this Article: http://doi.acm.org/10.1145/299432.299462 What is a DOI?

Warning: The download time has expired please click on the item to try again.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Tatsuya Akutsu , Satoru Kuhara , Osamu Maruyama , Satoru Miyano, Identification of gene regulatory networks by strategic gene disruptions and gene overexpressions, Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms, p.695-702, January 25-27, 1998, San Francisco, California, United States
	2	T. Chen, H. L. He, and G. M. Church. Modeling gene expression with differential equations. In Pacific Symposium Biocomputing '99, pages 29-40, 1999.
	3	R. Cho, M. Campbell, E. Winzeler, L. Steinmetz, A. Conway, L. Wodicka, T. Wolfsberg, A. Gabrielian, D. Landsman, D. Lockhart, and R. Davis. A genome-wide transcriptional analysis of the mitotic cell cycle. Molecular Ceil, 2:65-73, July 1998.
	4	J. DeRisi, V. Iyer, and P. Brown. Exploring the metabolic and genetic control of gene expression on a genomic scale. Science, 278:680-686, Oct. 24, 1997.
	5	Michael R. Garey , David S. Johnson, Computers and Intractability: A Guide to the Theory of NP-Completeness, W. H. Freeman & Co., New York, NY, 1979
	6	Michel X. Goemans , David P. Williamson, New ${\bf \frac{3}{4}}$-Approximation Algorithms for the Maximum Satisfiability Problem, SIAM Journal on Discrete Mathematics, v.7 n.4, p.656-666, Nov. 1994 [doi>10.1137/S0895480192243516]
	7	Michel X. Goemans , David P. Williamson, Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming, Journal of the ACM (JACM), v.42 n.6, p.1115-1145, Nov. 1995 [doi>10.1145/227683.227684]
	8	Anil K. Jain , Richard C. Dubes, Algorithms for clustering data, Prentice-Hall, Inc., Upper Saddle River, NJ, 1988
	9	Rajeev Kohli , Ramesh Krishnamurti , Prakash Mirchandani, The Minimum Satisfiability Problem, SIAM Journal on Discrete Mathematics, v.7 n.2, p.275-283, May 1994 [doi>10.1137/S0895480191220836]
	10	S. Liang, S. Fuhrman, and R. Somogyi. Reveal: a general reverse engineering algorithm for inference of genetic network architectures. In Pacific Symposium Biocomputin~ '98, pages 18-29, 1998.
	11	D. Lockhart, H. Dong, M. Byrne, M. Follettie, M. Gallo, M. Chee, M. Mittmann, C. Wang, M. Kobayashi, H. Horton, and E. Brown. Expression monitoring by hybrdiziation to highdensity oligonucleotide arrays. Nature Biotechnology, 14:1675-1680, December 1996.
	12	S. Micali and V. Vazirani. An o(Ix/~l~l) algorithm for finding maximum matchings in general graphs. In Proc. l~lst. Syrup. Foundations of Computing, pages 17-27, 1980.
	13	D. Thiei~y and R. Thomas. Qualitative analysis of gene networks. In Pacific Symposium Biocomputing 98, pages 77-87, 1998.
	14	Luca Trevisan, Positive Linear Programming, Parallel Approximation and PCP's, Proceedings of the Fourth Annual European Symposium on Algorithms, p.62-75, September 25-27, 1996
	15	Luca Trevisan, Approximating Satisfiable Satisfiability Problems (Extended Abstract), Proceedings of the 5th Annual European Symposium on Algorithms, p.472-485, September 15-17, 1997
	16	X. Wen, S. Fuhrman, G. Michaels, D. Carl S. Smith, J. Barker, and R. Somogyi. Large-scaJe temporal gene expression mapping of central nervous system development. Proc. Natl. Acad. $ci., 95:334-339, 1998.

CITED BY 10

	Tatsuya Akutsu , Satoru Kuhara , Osamu Maruyama , Satoru Miyano, Identification of genetic networks by strategic gene disruptions and gene overexpressions under a boolean model, Theoretical Computer Science, v.298 n.1, p.235-251, 4 April 2003
	Chris Bailey-Kellogg , Alik Widge , John J. Kelley, III , Marcelo J. Berardi , John H. Bushweller , Bruce Randall Donald, The NOESY jigsaw: automated protein secondary structure and main-chain assignment from sparse, unassigned NMR data, Proceedings of the fourth annual international conference on Computational molecular biology, p.33-44, April 2000, Tokyo, Japan
	Alan Wee-Chung Liew , Lap Keung Szeto , Sy-Sen Tang , Hong Yan , Mengsu Yang, A Computational Approach to Gene Expression Data Extraction and Analysis, Journal of VLSI Signal Processing Systems, v.38 n.3, p.237-258, November 2004
	Tatsuya Akutsu , Satoru Miyano , Satoru Kuhara, Algorithms for identifying Boolean networks and related biological networks based on matrix multiplication and fingerprint function, Proceedings of the fourth annual international conference on Computational molecular biology, p.8-14, April 2000, Tokyo, Japan
	Vladmir Filkov , Steven Skiena , Jizu Zhi, Analysis techniques for microarray time-series data, Proceedings of the fifth annual international conference on Computational biology, p.124-131, April 22-25, 2001, Montreal, Quebec, Canada
	Nir Friedman , Michal Linial , Iftach Nachman , Dana Pe'er, Using Bayesian networks to analyze expression data, Proceedings of the fourth annual international conference on Computational molecular biology, p.127-135, April 2000, Tokyo, Japan
	Edward Keedwell , Ajit Narayanan, Discovering Gene Networks with a Neural-Genetic Hybrid, IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), v.2 n.3, p.231-242, July 2005
	Xiaobo Zhou , Xiaodong Wang , Edward R. Dougherty, Construction of genomic networks using mutual-information clustering and reversible-jump Markov-chain-Monte-Carlo predictor design, Signal Processing, v.83 n.4, p.745-761, April 2003
	Patrick C. H. Ma , Keith C. C. Chan, A novel data mining algorithm for reconstructing gene regulatory networks from microarray data, Proceedings of the 2006 ACM symposium on Applied computing, April 23-27, 2006, Dijon, France
	Zan Huang , Jiexun Li , Hua Su , George S. Watts , Hsinchun Chen, Large-scale regulatory network analysis from microarray data: modified Bayesian network learning and association rule mining, Decision Support Systems, v.43 n.4, p.1207-1225, August, 2007