Discovery of regulatory connections in microarray data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3202, Issue , 2004, Pages 149-160

  Egmont Petersen, Michael ^a   De Jonge, Wim ^a   Siebes, Arno ^a  

^a UTRECHT UNIVERSITY   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

MARKOV PROCESSES; MONTE CARLO METHODS; TIME SERIES;

CONTINUOUS MEASUREMENTS; MARKOV CHAIN MONTE CARLO SAMPLINGS; MICROARRAY DATA; NEW APPROACHES; PRE-PROCESSING STEP; PROBABILISTIC NETWORK; REGULATORY INTERACTIONS; SAMPLING SCHEMES;

GENES;

EID: 35048852763     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-30116-5_16     Document Type: Article

References (15)

1. Datta, S., Datta, S.: Comparisons and validation of statistical clustering techniques for microarray gene expression data. Bioinformatics 19 (2003) 459-466
2. Spellman, P., Sherlock, G., M.Q., Z., Iyer, V., Anders, K., Eisen, M., Brown, P., Botstein, D., Futcher, B.: Comprehensive identification of cell cycle-regulated genes of the yeast saccharomyces cerevisiae by microarray hybridization. Molecular Biology of the Cell 9 (1998) 3273-3297
3. Husmeier, D.: Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic bayesian networks. Bioinformatics 19 (2003) 2271-2282
4. Smith, V., Jarvis, E., Hartemink, A.: Evaluating functional network inference using simulations of complex biological systems. Bioinformatics 18 (2002) S216-S224
5. Verbeek, P., Vrooman, H., van Vliet, L.: Low-level image-processing by max min filters. Signal Processing 15 (1988) 249-258
6. Florack, L., ter Haar Romeny, B., Koenderink, J., Viergever, M.: Scale and the differential structure of images. Image and Vision Computing 10 (1992) 376-388
7. Lindeberg, T.: Scale-space for discrete signals. IEEE Transactions on Pattern Analysis and Machine Intelligence 12 (1990) 234-254
8. Lindeberg, T., ter Haar Romeny, B. In: Linear scale-space II: Early visual operations. Kluwer Academic Publishers, Dordrecht (1994) 39-72
9. Friedman, N., Linial, M., Nachman, I., Pe'er, D.: Using bayesian networks to analyze expression data. Journal of Computational Biology 7 (2000) 601-620
10. Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Machine learning 29 (1997) 131-163
11. Madigan, D., York, J.: Bayesian graphical models for discrete-data. International statistical review 63 (1995) 215-232
12. Green, P.: Reversible jump markov chain monte carlo computation and bayesian model determination. Biometrika 82 (1995) 711-732
13. Duda, R., Hart, P.: Pattern classification and scene analysis. John Wiley & Sons, New York (1973)
14. Egmont-Petersen, M., Feelders, A., Baesens, B.: Confidence intervals for probabilistic network classifiers. To appear in Computational Statistics and Data Analysis (2004)
15. Mendenhall, M., Hodge, A.: Regulation of cdc28 cyclin-dependent protein kinase activity during the cell cycle of the yeast saccharomyces cerevisiae. Microbiology and Molecular Biology Reviews 62 (1998) 1191-1243