Abstract
Knowledge extraction from gene expression data has been one of the main challenges in the bioinformatics field during the last few years. In this context, a particular kind of data, data retrieved in a temporal basis (also known as time series), provide information about the way a gene can be expressed during time. This work presents an exhaustive analysis of last proposals in this area, particularly focusing on those proposals using non–supervised machine learning techniques (i.e. clustering, biclustering and regulatory networks) to find relevant patterns in gene expression.
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
Ben-Dor, A., Chor, B., Karp, R., Yakhini, Z.: Discovering local structure in gene expresión data: the order-preserving submatrix problem. In: Proceedings of the 6th International Conference on Computational Biology, pp. 49–57 (2002)
Bezdek, J.C.: Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York (1981)
Kiddle, S.J., et al.: Temporal clustering by affinity propagation reveals transcriptional modules in Arabidopsis thaliana. Bioinformatics 26(3), 355–362 (2010)
Frey, B.J.J., Dueck, D.: Clustering by passing messages between data points. Science 315(5814), 972–976 (2007)
Granger, C.W.J.: Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37(3), 424–438 (1969)
Guo, S., Wu, J., Ding, M., Feng, J.: Uncovering Interactions in the Frequency Domain. PLoS Computational Biology 4(5), e1000087+ (2008)
Hecker, M., Lambeck, S., Toepfer, S., van Someren, E., Guthke, R.: Gene regulatory network inference: Data integration in dynamic models – a review. Biosystems 96(1), 86103 (2009)
Huang, W., Cao, X., Zhong, S.: Network-based comparison of temporal gene expression patterns. Bioinformatics 26(23), 2944–2951 (2010)
Jaqaman, K., Dorn, J.F., Marco, E., Sorger, P.K., Danuser, G.: Phenotypic clustering of yeast mutants based on kinetochore microtubule dynamics. Bioinformatics 23(13), 1666–1673 (2007)
Jiang, D., Pei, J., Zhang, A.: Interactive exploration of coherent patterns in time-series gene expression data. In: Proceedings of SIGKDD (2003)
Krishna, R., Li, C.T., Wollaston, V.B.: A temporal precedence based clustering method for gene expression microarray data. BMC Bioinformatics 11(1), 68+ (2010)
Li, C.T., Yuan, Y., Wilson, R.: An unsupervised conditional random fields approach for clustering gene expression time series. Bioinformatics 24(21), 2467–2473 (2008)
Madeira, S.C., Oliveira, A.L.: Biclustering algorithms for biological data analysis: A survey. IEEE Transactions on Computational Biology and Bioinformatics 1(1), 24–45 (2004)
Madeira, S.C., Oliveira, A.L.: A linear time biclustering algorithm for time series gene expression data. Technical Report: INESC-ID, pp. 1–8 (2005)
Madeira, S.C., Oliveira, A.L.: A polynomial time biclustering algorithm for finding approximate expression patterns in gene expression time series. Algorithms for Molecular Biology 4(8), 1–39 (2009)
Magni, P., Ferrazzi, F., Sacchi, L., Bellazzi, R.: TimeClust: a clustering tool for gene expression time series. Bioinformatics 24(3), 430–432 (2008)
Martin, S., Zhang, Z., Martino, A., Faulon, J.L.: Boolean dynamics of genetic regulatory networks inferred from microarray time series data. Bioinformatics 23(7), 866–874 (2007)
Mukhopadhyay, N.D., Chatterjee, S.: Causality and pathway search in microarray time series experiment. Bioinformatics 23(4), 442–449 (2007)
Nagarajan, R., Upreti, M.: Comment on causality and pathway search in microarray time series experiment. Bioinformatics 24(7), 1029–1032 (2008)
Nepomuceno-Chamorro, I.A., Aguilar-Ruiz, J.S., Riquelme, J.C.: Inferring gene regression networks with model trees. BMC Bioinformatics 11, 517 (2010)
Qu, J., Ng, M., Chen, A.L.: Constrained subspace clustering for time series gene expression data. In: The Fourth International Conference on Computational Systems Biology, pp. 323–330 (2010)
Rubio-Escudero, C., Martínez-Álvarez, F., Romero-Zaliz, R., Zwir, I.: Classification of gene expression profiles: Comparison of K-means and Expectation-Maximization algorithms. In: Prooceedings of the 8th International Conference on Hybrid Intelligent Systems, pp. 831–836 (2008)
Rubio-Escudero, C., Romero-Zaliz, R., Zwir, I., del Val, C.: Optimization of multi-classifiers for computational biology: application to gene finding and expression. Theoretical Chemistry Accounts: Theory, Computation, and Modeling 125(3), 599–611 (2010)
Segal, E., Wang, H., Koller, D.: Discovering molecular pathways from protein interaction and gene expression data. Bioinformatics 19(2), 264–272 (2003)
Shiraishi, Y., Kimura, S., Okada, M.: Inferring cluster-based networks from differently stimulated multiple time-course gene expression data. Bioinformatics 26(8), 1073–1081 (2010)
Xu, R., Wunsch II., D.C.: Survey of clustering algorithms. IEEE Transactions on Neural Networks 16(3), 645–678 (2005)
Zhang, Y., Zha, H., Chu, C.H.: A time-series biclustering algorithm for revealing co-regulated genes. Bioinformatics 18(3), 606–611 (2005)
Zhao, W., Serpedin, E., Dougherty, E.R.: Inferring gene regulatory networks from time series data using the minimum description length principle. Bioinformatics 22(17), 2129–2135 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gómez-Vela, F., Martínez-Álvarez, F., Barranco, C.D., Díaz-Díaz, N., Rodríguez-Baena, D.S., Aguilar-Ruiz, J.S. (2011). Pattern Recognition in Biological Time Series. In: Lozano, J.A., Gámez, J.A., Moreno, J.A. (eds) Advances in Artificial Intelligence. CAEPIA 2011. Lecture Notes in Computer Science(), vol 7023. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25274-7_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-25274-7_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25273-0
Online ISBN: 978-3-642-25274-7
eBook Packages: Computer ScienceComputer Science (R0)