Abstract
Researchers often have several different hypothesises on the possible structures of the gene regulatory network (GRN) underlying the biological model they study. It would be very helpful to be able to rank the hypothesises using existing data. Microarray technologies enable us to monitor the expression levels of tens of thousands of genes simultaneously. Given the expression levels of almost all of the well-substantiated genes in an organism under many experimental conditions, it is possible to evaluate the hypothetical gene regulatory networks with statistical methods. We present RankGRN, a web-based tool for ranking hypothetical gene regulatory networks. RankGRN scores the gene regulatory network models against microarray data using Bayesian Network methods. The score reflects how well a gene network model explains the microarray data. A posterior probability is calculated for each network based on the scores. The networks are then ranked by their posterior probabilities. RankGRN is available online at [http://GeneNet.org/bn]. RankGRN is a useful tool for evaluating the hypothetical gene network models’ capability of explaining the observational gene expression data (i.e. the microarray data). Users can select the gene network model that best explains the microarray data.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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., Zeitlinger, J., Jennings, E.G., Murray, H.L., Gordon, D.B., Ren, B., Wyrick, J.J., Tagne, J.B., Volkert, T.L., Fraenkel, E., Gifford, D.K., Young, R.A.: Transcriptional Regulatory Networks in Saccharomyces cerevisiae. Science 298, 799–804 (2002)
Guelzim, N., Bottani, S., Bourgine, P., Kepes, F.: Topological and causal structure of the yeast transcriptional regulatory network. Nat. Genet. 31, 60–63 (2002)
Repsilber, D., Liljenströmb, H., Anderson, S.G.E.: Reverse engineering of regulatory networks: simulation studies on a genetic algorithm approach for ranking hypotheses. Biosystems 66(1-2), 31–41 (2002)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann Publishers, San Francisco (1988)
Freidman, N., Linial, M., Nachman, I., Peer, D.: Using Bayesian Networks to Analyze Expression Data. J. Comput. Biol. 7, 601–620 (2000)
Spirtes, P., Glymour, C., Scheines., R., Kauffman, S., Aimale, V., Wimberly, F.: Constructing Bayesian Network Models of Gene Expression Networks from Microarray Data. In: Proceedings of the Atlantic Symposium on Computational Biology, Genome Information Systems and Technology (2001)
Peer, D., Regev, A., Elidan, G., Friedman, N.: Inferring subnetworks from perturbed expression profiles. Bioinformatics 17, S215–S224 (2001)
Chu, T., Glymour, C., Scheines, R., Spirtes, P.: A Statistical Problem for Inference to Regulatory Structure from Associations of Gene Expression Measurement with Microarrays. Bioinformatics 19, 1147–1152 (2003)
Gene Expression Omnibus, http://www.ncbi.nlm.nih.gov/geo/
Stanford Microarray Database , http://genome-www5.stanford.edu/MicroArray/SMD/
Gollub, J., Ball, C.A., Binkley, G., Demeter, J., Finkelstein, D.B., Hebert, J.M., Hernandez-Boussard, T., Jin, H., Kaloper, M., Matese, J.C., Schroeder, M., Brown, P.O., Botstein, D., Sherlock, G.: The Stanford Microarray Database: data access and quality assessment tools. Nucleic Acids Res 31(1), 94–96 (2003)
ArrayExpress at EBI, http://www.ebi.ac.uk/arrayexpress/
ExpressDB, http://arep.med.harvard.edu/ExpressDB/
Aach, J., Rindone, W., Church, G.M.: Systematic management and analysis of yeast gene expression data. Genome Res. 10(4), 431–445 (2000)
Hartemink, A.J.: Principled Computational Methods for the Validation and Discovery of Genetic Regulatory Networks. PhD thesis, MIT (2001)
Saccharomyces Genome Database, http://www.yeastgenome.org/
BIOBASE, GmbH Databases Transfac Professional Suite, http://www.cognia.com/
Heckerman, D.: A tutorial on learning with Bayesian networks. In: Jordan, M.I. (ed.) Learning in Graphical Models, pp. 301–354 (1998)
de Jong, H.: Modeling and Simulating of Genetic Regulatory Systems: A Literature Review. J. Comput. Biol. 9, 67–103 (2002)
Pearl, J.: Causality: Models, Reasoning, and Inference. Cambridge University Press, Cambridge (2000)
Segal, E., Shapira, M., Regev, A., Pe’er, D., Botstein, D., Koller, D., Friedman, N.: Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat. Genet. 34(2), 166–176 (2003)
Bockhorst, J., Craven, M., Page, D., Shavlik, J., Glasner, J.: A Bayesian network approach to operon prediction. Bioinformatics 19(10), 1227–1235 (2003)
Sabatti, C., Rohlin, L., Oh, M.K., Liao, J.C.: Co-expression pattern from DNA microarray experiments as a tool for operon prediction. Nucleic Acids Res. 30(13), 2886–2893 (2002)
Savoie, C.J., Aburatani, S., Watanabe, S., Eguchi, Y., Muta, S., Imoto, S., Miyano, S., Kuhara, S., Tashiro, K.: Use of gene networks from full genome microarray libraries to identify functionally relevant drug-affected genes and gene regulation cascades. DNA Res. 10(1), 19–25 (2003)
Smith, V.A., Jarvis, E.D., Hartemink, A.J.: Influence of network topology and data collection on network inference. In: Pac. Symp. Biocomput., pp. 164–175 (2003)
Ong, I.M., Glasner, J.D., Page, D.: Modelling regulatory pathways in E. coli from time series expression profiles. Bioinformatics 18(suppl. 1), S241–S248 (2002)
Imoto, S., Goto, T., Miyano, S.: Estimation of genetic networks and functional structures between genes by using Bayesian networks and nonparametric regression. In: Pac. Symp. Biocomput., pp. 175–186 (2002)
Shmulevich, I., Dougherty, E.R., Kim, S., Zhang, W.: Probabilistic Boolean Networks: a rule-based uncertainty model for gene regulatory networks. Bioinformatics 18(2), 261–274 (2002)
Heckerman, D., Geiger, D., Chickering, D.M.: Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning 20(3), 197–243 (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, H., Zhou, M., Cui, Y. (2004). Ranking Gene Regulatory Network Models with Microarray Data and Bayesian Network. In: Shi, Y., Xu, W., Chen, Z. (eds) Data Mining and Knowledge Management. CASDMKM 2004. Lecture Notes in Computer Science(), vol 3327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30537-8_12
Download citation
DOI: https://doi.org/10.1007/978-3-540-30537-8_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23987-1
Online ISBN: 978-3-540-30537-8
eBook Packages: Computer ScienceComputer Science (R0)