An efficient top-down search algorithm for learning Boolean networks of gene expression

Machine Learning

Volumn 65, Issue 1, 2006, Pages 229-245

  Nam, Dougu ^a   Seo, Seunghyun ^b   Kim, Sangsoo ^a,c  

^a Korea Research Institute of Bioscience and Biotechnology (KRIBB)   (South Korea)

^b Seoul National University   (South Korea)

^c Soongsil University   (South Korea)

Author keywords

Boolean network; Core search; Coupon collection problem; Data consistency; Random superset selection

Indexed keywords

COMPUTATIONAL COMPLEXITY; DATA PROCESSING; NEURAL NETWORKS; ONLINE SEARCHING; YEAST;

BOOLEAN NETWORKS; CORE SEARCH; COUPON COLLECTION PROBLEMS; DATA CONSISTENCY; RANDOM SUPERSET SELECTIONS;

ALGORITHMS;

EID: 33749005690     PISSN: 08856125     EISSN: 15730565     Source Type: Journal    
DOI: 10.1007/s10994-006-9014-z     Document Type: Article

References (15)

1. Akutsu, T., Kuhara, S., & Miyano, S. (1999a). Identification of genetic networks from a small number of gene expression patterns under the Boolean network model. Proc. Pacific Symp. on Biocomputing '99, World Scientific, (pp. 17-28).
2. Akutsu, T., Miyano, S., & Kuhara, S. (1999b). Fast identification of Boolean networks. Technical Report 99-AL-66, Information Processing Society of Japan: (pp. 25-32).
3. Akutsu, T., Miyano, S., & Kuhara, S. (2000a). Algorithms for identifying Boolean networks and related biological networks based on matrix multiplication and fingerprint function. J. Comp. Biol., 7(3/4), 331-343.
4. Akutsu, T., Miyano, S., & Kuhara, S. (2000b). Inferring qualitative relations in genetic networks and metabolic pathways. Bioinformatics, 16(8), 727-734.
5. Akutsu, T., Miyano, S., & Kuhara, S. (2003). A simple greedy algorithm for finding functional relations: efficient implementation and average case analysis. Theoretical Computer Science, 292(2), 481-495.
6. Fukagawa, D., & Akutsu, T. (2003). Performance analysis of a greedy algorithm for inferring Boolean functions Discovery Science.
7. Friedman, N., Linial, M., Nachman, I., & Pe'er, D. (2000). Using Bayesian networks to analyze expression data. J. Comp. Biol., 9, 601-620.
8. Kauffman, S.A. (1969). Metabolic stability and epigenesis in randomly constructed genetic nets. J. Theor. Biol., 22, 437-466.
9. Lee, T.I., Rinaldi, N.J., Robert, F., Odom, D.T., Bar-Joseph, Z., Gerber, G.K., Hannett, N.M., Harbison, C.T., Thompson, C.M., Simon, I., et al. (2002). Transcriptional regulatory networks in Saccharomyces cerevisiae, Science, 298, 799-804.
10. Liang, S., Fuhrman, S., & Somogy, R. (1998). REVEAL, a general reverse engineering algorithm for inference of genetic network architectures. Proc. Pacific Symp. on Biocomputing '98, World Scientific, (pp. 18-29).
11. Shmulevich, I., Dougherty, E. R., Kim, S. & Zhang, W. (2002). Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks. Bioinformatics, 18, 261-274.
12. Boros, E., Ibaraki, T., & Makino, K. (1998). Error-Free and Best-Fit Extensions of partially defined Boolean functions. Information and Computation, 140, 254-283.
13. Lähdesmäki, H., Shmulevich, I., & Yli-Haria, O. (2003). On learning gene regulatory networks under the Boolean network model. Machine Learning, 52, 147-167.
14. Stanley, R. P. (1997). Enumerative Combinatorics (vol. 1). Cambridge Press.
15. Wilf, H. S. (1992). Generatingfunctionology. Academic Press.