Inference of gene regulatory networks from time course gene expression data using neural networks and swarm intelligence

Proceedings of the 2006 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'06

Volumn , Issue , 2006, Pages 435-442

  Ressom, H W ^a   Zhang, Y ^a,b   Xuan, J ^b   Wang, Y ^b   Clarke, R ^a  

^a GEORGETOWN UNIVERSITY   (United States)

^b State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; BIOACTIVITY; BIOCOMMUNICATIONS; BIOINFORMATICS; CELLULAR AUTOMATA; GENE EXPRESSION; INFORMATION SCIENCE; INTELLIGENT CONTROL; LOUDSPEAKERS; NEURAL NETWORKS; NUCLEIC ACIDS; OPTIMIZATION; ORGANIC ACIDS; RECURRENT NEURAL NETWORKS; VEGETATION;

ANT-COLONY OPTIMIZATION; COMPUTATIONAL BIOLOGY; COMPUTATIONAL INTELLIGENCE; DNA MICRO ARRAYS; GENE EXPRESSION DATA; GENE REGULATORY NETWORK; GENE REGULATORY NETWORKS; INFER NETWORKS; NOVEL ALGORITHMS; OPTIMAL ARCHITECTURES; PARTICLE-SWARM OPTIMIZATION; REAL-WORLD; RECURRENT NEURAL NETWORK (RNN); SWARM INTELLIGENCE; TIME-COURSE GENE EXPRESSION;

PARTICLE SWARM OPTIMIZATION (PSO);

EID: 50249154011     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CIBCB.2006.330969     Document Type: Conference Paper

References (22)

1. Shmulevich, I., Dougherty, E. R., Kim, S., and Zhang, W., "Probabilistic Boolean Networks: a rule-based uncertainty model for gene regulatory networks," Bioinformatics, vol. 18, No. 2, pp. 261-74, 2002.
2. Schmitt, W. J., Raab, R. M., and Stephanopoulos, G., "Elucidation of gene interaction networks through time-lagged correlation analysis of transcriptional data," Genome Research, vol. 14, pp. 1654-1663, 2004.
3. Friedman, N., Linial, M., Nachman, I., and Pe'er, D., "Using Bayesian networks to analyze expression data," J Comput Biol., vol. 7, pp. 601-620, 2000.
4. D'Haeseleer, P., Wen, X., Fuhrman, S., and Somogyi, R., "Linear modeling of mRNA expression levels during CNS development and injury," Pac Symp Biocomput, pp. 41-52, 1999.
5. Chen, T., He, H. L., and Church, G. M., "Modeling gene expression with differential equations," Pac Symp Biocomput, pp. 29-40, 1999.
6. Liang, S., Fuhrman, S., and Somogyi, R., "Reveal, a general reverse engineering algorithm for inference of genetic network architectures," Pac Symp Biocomput, pp. 18-29, 1998.
7. Mendes, P. and Kell, D., "On the analysis of the inverse problem of metabolic pathways using artificial neural networks," BioSystems, vol. 38, pp. 15-28, 1996.
8. Mendes, P., Sha, W., and Ye, K., "Artificial gene networks for objective comparison of analysis algorithms," Bioinformatics, vol. 19, pp. ii122-ii129, 2003.
9. Vohradsky, J., "Neural model of the genetic network," J Biol Chem, vol. 276, No. 39, pp. 36168-73, 2001.
10. Ueda, T., Ono, I., and Okamoto, M., "Development of system identification technique based on real-coded genetic algortihm," Genome Informatics, vol. 13, pp. 386-387, 2002.
11. Kikuchi, S., Tominaga, D., Arita, M., Takahashi, K., and Tomita, M., "Dynamic modeling of genetic networks using genetic algorithm and S-system," Bioinformatics, vol. 19, No. 5, pp. 643-50, 2003.
12. Shin, A. and Iba, H., "Construction of genetic network using evolutionary algorithm and combined fitness function," Genome Informatics vol. 14, pp. 94-103, 2003.
13. Engelbrecht, A. P., Computational Intelligence: An Introduction: John Wiley, New York, 2003.
14. Keedwell, E. and Narayanan, A., "Discovering gene regulatory networks with a neural-genetic hybrid," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 2, No. 3, pp. 231-243, 2005.
15. Middendorf, M., Kundaje, A., Wiggins, C., Freund, Y., and Leslie, C., "Predicting genetic regulatory response using classification," Bioinformatics, vol. 20 Suppl 1, pp. I232-I240, 2004.
16. Soinov, L. A., Krestyaninova, M. A., and Brazma, A., "Towards reconstruction of gene networks from expression data by supervised learning," Genome Biol, vol. 4, No. 1, pp. R6, 2003.
17. Woolf, P. J. and Wang, Y., "A fuzzy logic approach to analyzing gene expression data," Physiol Genomics, vol. 3, No. 1, pp. 9-15, 2000.
18. Ressom, H., Reynolds, R., and Varghese, R. S., "Increasing the efficiency of fuzzy logic-based gene expression data analysis," Physiol Genomics, vol. 13, No. 2, pp. 107-17, 2003.
19. Sokhansanj, B. A., Fitch, J. P., Quong, J. N., and Quong, A. A., "Linear fuzzy gene network models obtained from microarray data by exhaustive search," BMC Bioinformatics, vol. 5, pp. 108, 2004.
20. Spellman, P. T., Sherlock, G., Zhang, M. Q., Iyer, V. R., Anders, K., Eisen, M. B., Brown, P. O., Botstein, D., and Futcher, B., "Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization," Mol Biol Cell, vol. 9, No. 12, pp. 3273-97, 1998.
21. Stuart, D. and Wittenberg, C., "CLB5 and CLB6 are required for premeiotic DNA replication and activation of the meiotic S/M checkpoint," Genes Dev, vol. 12, No. 17, pp. 2698-710, 1998.
22. Poloumienko, A., Dershowitz, A., De, J., and Newlon, C. S., "Completion of replication map of Saccharomyces cerevisiae chromosome III," Mol Biol Cell, vol. 12, No. 11, pp. 3317-27, 2001.