Summary
Gene expression microarrays have become a popular high-throughput technique in functional genomics. By enabling the monitoring of thousands of genes simultaneously, this technique holds enormous potential to extend our understanding of various biological processes. However, the large amount of data poses a challenge when interpreting the results. Moreover, microarray data often contain frequent missing values, which may drastically affect the performance of different data analysis methods. Therefore, it is essential to effectively exploit additional biological information when analyzing and interpreting the data. In the present study, we investigate the relationship between gene expression profile and promoter sequence profile in the context of missing value imputation. In particular, we demonstrate that the selection of predictive genes for expression value estimation can be considerably improved by the incorporation of transcription factor binding information.
This is a preview of subscription content, log in via an institution to check access.
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
Allocco, D.J., Kohane, I.S., Butte, A.J.: Quantifying the relationship between coexpression, co-regulation and gene function. BMC Bioinformatics, 5, 18 (2004).
Altman, R.B., Raychaudhuri, S.: Whole-genome expression analysis: challenges beyond clustering. Curr. Opin. Struct. Biol., 11, 340–347 (2001).
Bar-Joseph, Z., Gerber, G.K., Lee, T.I., Rinaldi, N.J., You, J.Y., Robert, F., et al.: Computational discovery of gene modules and regulatory networks. Nat. Biotechnol., 21, 1337–1342 (2003).
De Brevern, A.G., Hazout, S., Malpertuy, A.: Influence of microarrays experiments missing values on the stability of gene groups by hierarchical clustering. BMC Bioinformatics, 5, 114 (2004).
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).
Gasch, A.P., Spellman, P.T., Kao, C.M., Carmel-Harel, O., Eisen, M.B., Storz, G., et al.: Genomic expression program in the response of yeast cells to environmental changes. Mol. Biol. Cell, 11, 4241–4257 (2000).
Glenisson, P., Mathys, J., De Moor, B.: Meta-clustering of gene expression data and literature-based information. SIGKDD Explorations, 5, 101–112 (2003).
Hanisch, D., Zien, A., Zimmer, R., Lengauer, T.: Co-clustering of biological networks and gene expression data. Bioinformatics, 18, S145–S154 (2002).
Kim, H., Golub, G.H., Park, H.: Missing value estimation for DNA microarray gene expression data: local least squares imputation. Bioinformatics, 21, 187–198 (2005).
Lapidot, M., Pilpel, Y.: Comprehensive quantitative analyses of the effects of promoter sequence elements on mRNA transcription. Nucleic Acids Res., 31, 3824–3828 (2003).
Lee, T.I., Rinaldi, N.J., Robert, F., Odom, D.T., Bar-Joseph, Z., Gerber, G.K., et al.: Transcriptional regulatory networks in Saccharomyces cerevisiae. Science, 298, 799–804 (2002).
Spellman, P.T., Sherlock, G., Zhang, M.Q., Iyer, V.R., Anders, K., Eisen, M.B., et al.: Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol. Biol. Cell, 9, 3273–3297 (1998).
Tavazoie, S., Hughes, J.D., Campbell, M.J., Cho, R.J., Church, G.M.: Systematic determination of genetic network architecture. Nat. Genet., 22, 281–285 (1999).
Troyanskaya, O., Cantor, M., Sherlock, G., Brown, P., Hastie, T., Tibshirani, R., et al.: Missing value estimation methods for DNA microarrays. Bioinformatics, 17, 520–525 (2001).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 springer
About this chapter
Cite this chapter
Elo, L.L., Johannes, T., Nevalainen, O., Aittokallio, T. (2007). Predicting Gene Expression from Combined Expression and Promoter Profile Similarity with Application to Missing Value Imputation. In: Deutsch, A., Brusch, L., Byrne, H., Vries, G.d., Herzel, H. (eds) Mathematical Modeling of Biological Systems, Volume I. Modeling and Simulation in Science, Engineering and Technology. Birkhäuser Boston. https://doi.org/10.1007/978-0-8176-4558-8_9
Download citation
DOI: https://doi.org/10.1007/978-0-8176-4558-8_9
Publisher Name: Birkhäuser Boston
Print ISBN: 978-0-8176-4557-1
Online ISBN: 978-0-8176-4558-8
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)