Abstract
Understanding the way gene regulatory networks (complex systems of genes, proteins and other molecules) function and interact to carry out specific cell functions is currently one of the central goals in computational molecular biology. We propose an approach for inferring the complex causal relationships among genes from microarray experimental data based on a recurrent neuro-fuzzy method. The method derives information on the gene interactions in a highly interpretable form (fuzzy rules) and takes into account dynamical aspects of genes regulation through its recurrent structure. The gene interactions retrieved from a set of genes known to be highly regulated during the yeast cell-cycle are validated by biological studies, while our method surpasses previous computational techniques that attempted gene networks reconstruction, being able to retrieve significantly more biologically valid relationships among genes.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
DeRisi, J.L., Iyer, V.R., Brown, P.O.: Exploring the metabolic and genetic control of gene expression on a genomic scale. Science 278, 680–686 (1997)
Mavroudi, S., Papadimitriou, S., Bezerianos, A.: Gene expression data analysis with a dynamically extended self-organizing map that exploits class information. Bioinformatics 18, 1446–1453 (2002)
Eisen, M.B., Spellman, P.T., Brown, P.O., Botstein, D.: Cluster analysis and display if genome-wide expression patterns. Proc. Natl. Acad. Science 95, 14863–14868 (1998)
Golub, T.R., Slonim, D.K., Tamayo, P., et al.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999)
Golub, T.R., Slonim, D.K., Tamayo, P., et al.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999)
Liang, S., Fuhrman, S., Somogyi, R.: REVEAL, a general reverse engineering algorithm for inference of genetic network architectures. Pac. Symp. Biocomputing, 18–29 (2000)
Tegner, J., Yeung, M.K., Hasty, J., Collins, J.J.: Reverse engineering gene networks: integrating genetic perturbations with dynamical modeling. Proc. Natl. Acad. Science 100, 5944–5949 (2003)
Friedman, N., Linial, M., Nachman, I., Pe’er, D.: Using Bayesian networks to analyze expression data. J. Comp. Biology 7, 601–620 (2000)
Juang, C.-F., Lin, C.T.: A recurrent self-organizing neural fuzzy inference network. IEEE Trans. Neural Networks 10, 828–845 (1999)
Juang, C.-F., Lin, C.T.: A recurrent self-organizing neural fuzzy inference network. IEEE Trans. Neural Networks 10, 828–845 (1999)
Li, F., Long, T., Lu, Y., Ouyang, Q., Tang, C.: The yeast cell-cycle network is robustly designed. Proc. Natl. Acad. Science 101, 4781–4786 (2004)
Lin, C.T., Lee, C.S.G.: Reinforcement structure/parameter learning for neural-network-based fuzzy logic control systems. IEEE Trans. Fuzzy Syst. 2, 46–63 (1993)
Guthke, R., Moller, U., Hoffmann, M., Thies, F., Topfer, S.: Dynamic network reconstruction from gene expression data applied to immune response during bacterial infection. Bioinformatics 8, 1626–1634 (2005)
Spellman, P.T., Sherlock, G., Zhang, M.Q., Iver, V.R., Anders, K., Eisen, M.B., Brown, P.O., Botstein, D., Futcher, B.: Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridisation. Mol. Biol. Cell. 9, 3273–3297 (1998)
Oba, S., Sato, M., Takemasa, I., et al.: A Bayesian missing value estimation method for gene expression profile data. Bioinformatics 19, 2088–2096 (2003)
Futcher, B.: Transcriptional Regulatory Networks of the yeast cell-cycle. Curr. Opin. Cell Biol. 14, 676–683 (2002)
Soinov, L., Krestyaninova, M., Brazma, A.: Towards reconstruction of gene networrks from expression data by supervised learning. Genome Biology 4, R6.1–R6.10 (2003)
Sokhansanj, B., Fitch, P., Quong, J., Quong, A.: Linear fuzzy gene network models obtained from microarray data by exhaustive search. BMC Bioinformatics 5, 1–12 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Maraziotis, I., Dragomir, A., Bezerianos, A. (2005). Recurrent Neuro-fuzzy Network Models for Reverse Engineering Gene Regulatory Interactions. In: R. Berthold, M., Glen, R.C., Diederichs, K., Kohlbacher, O., Fischer, I. (eds) Computational Life Sciences. CompLife 2005. Lecture Notes in Computer Science(), vol 3695. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11560500_3
Download citation
DOI: https://doi.org/10.1007/11560500_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29104-6
Online ISBN: 978-3-540-31726-5
eBook Packages: Computer ScienceComputer Science (R0)