Bayesian learning of sparse gene regulatory networks

BioSystems

Volumn 87, Issue 2-3, 2007, Pages 299-306

  Chan, Zeke S H ^a   Collins, Lesley ^b   Kasabov, N ^a  

^a AUCKLAND UNIVERSITY OF TECHNOLOGY   (New Zealand)

^b MASSEY UNIVERSITY   (New Zealand)

Author keywords

Differential equations; Gene regulatory networks; Sparse Bayesian learning

Indexed keywords

BAYESIAN ANALYSIS; GENE EXPRESSION; PARAMETERIZATION; PARSIMONY ANALYSIS; YEAST;

ANALYTICAL PARAMETERS; ARTICLE; BAYESIAN LEARNING; CELL CYCLE G1 PHASE; CELL CYCLE S PHASE; GENE EXPRESSION; GENE EXPRESSION REGULATION; GENETIC MODEL; NONHUMAN; RELIABILITY; SACCHAROMYCES CEREVISIAE; TIME SERIES ANALYSIS; YEAST;

SACCHAROMYCES CEREVISIAE;

EID: 33846097712     PISSN: 03032647     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.biosystems.2006.09.026     Document Type: Article

References (16)

1. Arnone M.I., and Davidson E.H. The hardwiring of development: organization and function of genomic regulatory systems. Development 124 (1997) 1851-1864
2. Baldi P., and Brunak S. Bioinformarics-A machine learning approach (2001), The MIT Press
3. In: Bower J., and Bolouri H. (Eds). Computational Modelling of Genetic and Biochemical Networks (2001), The MIT Press
4. de Jong H., Page M., Hernandez C., and Geiselmann J. Qualitative simulation of genetic regulatory networks: method and application. Paper Presented at the 17th International Joint Conference of Artificial Intelligence. San Mateo, CA (2001)
5. D'haeseleer P. Reconstructing Gene Networks from Large Scale Gene Expression Data. (2000), The University of New Mexico, Albuquerque, New Mexico
6. Flick K., and Wittenberg C. Multiple pathways for suppression of mutants affecting G1-specific transcription in Saccharomyces cerevisiae. Genetics 169 1 (2005) 37-49
7. Kashyap R.L. A linear predictive HMM for vector-valued observations with applications to speech recognition. IEEE Transact. Autom. Contr. AC-15 (1970) 25-34
8. MacKay D.J.C. A practical Bayesian framework for backprop networks. In: Moody J.E., Hanson S.J., and Lippmann R.P. (Eds). Advances in Neural Information Processing Systems vol. 4 (1992) 839-846
9. MacKay D.J.C. Probable networks and plausible predictions-a review of practical Bayesian methods for supervised neural networks. Computat. Neural Syst. 6 (1995) 469-505
10. McCullagh P., and Nelder J. Generalized linear models (1999), CRC Press
11. Neal R.M. Bayesian learning for neural networks (1996), Springer-Verlag/New York, Inc.
12. Perrin B., Ralaivola L., Mazurie A., Bottani S., Mallet J., and d'Alche-Buc F. Gene networks inference using dynamic Bayesian networks. Bioinformatics 19 (2003) ii138-ii148
13. Sawyer S.L., Cheng I.H., Chai W., and Tye B.K. Mcm10 and Cdc45 cooperate in origin activation in Saccharomyces cerevisiae. J. Mol. Biol. 340 2 (2004) 195-202
14. Shumway R.H. Applied Statistical Time Series Analysis (1988), Prentice Hall, Englewood Cliffs, New Jersey
15. Spellman P., Sherlock G., Zhang M., and Iyer V. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol. Biol. Cell 9 (1998) 3273-3297
16. Tipping M.E. Sparse Bayesian learning and the relevance vector machine. J. Mach. Learn. Res. 1 (2001) 211-244