Abstract
Advance of high-throughput technologies, such as the microarray and mass spectrometry, has provided an effective approach for the development of systems biology, which aims at understanding the complex functions and properties of biological systems and processes. Revealing the functional correlated genes with co-expression pattern from microarray data allows us to infer the transcriptional regulatory networks and perform functional annotation of genes, and has become one vital step towards the implementation of integrative systems biology. Clustering is particularly useful and preliminary methodology for the discovery of co-expressed genes, for which many conventional clustering algorithms developed in the literature can be potentially useful. However, due to existing large amount of noise and a variety of uncertainties in the microarray data, it is vital important to develop techniques which are robust to noise and effective to incorporate user-specified objectives and preference. For this particular purpose, this paper presented a Genetic Algorithm (GA) based hybrid method for the co-expression gene discovery, which intends to extract the gene groups that have maximal dissimilarity between groups and maximal similarity within a group. The experimental results show that the proposed algorithm is able to extract more meaningful, sensible and significant co-expression gene groups than the traditional clustering methods such as the K-means algorithm. Besides presenting the proposed hybrid GA-based clustering algorithm for co-expression gene discovery, this paper introduces a new framework of integrative systems biology employed in our current research.
Keywords
- Hybrid Genetic Algorithm
- Gene Coexpression Network
- Gene Expression Data Analysis
- Sufficient Similarity
- Functional Related Gene
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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
Tanay, A., Steinfeld, I., Kupiec, M., Shamir, R.: Integrative analysis of genome-wide experiments in the context of a large high-throughput data compendium. Molecular Systems Biology (published online March 29, 2005)
Imbeaud, S., Auffray, C.: Functional Annotation: Extracting functional and regulatory order from microarrays. Molecular Systems Biology (published online May 25, 2005)
Zhou, X.J., Kao, M.C., et al.: Functional annotation and network reconstruction through cross-platform integration of microarray data. Nat. Biotechnol. 23, 238–243 (2005)
Jordan, I.K., et al.: Conservation and co-evolution in the scale-free human gene coexpression network. Mol. Biol. Evol. 21, 2058–2070 (2004)
Grigorov, M.G.: Global properties of biological networks. Drug Discov. Today 10, 365–372 (2005)
Famili, A.F., Liu, G., Liu, Z.: Evaluation and Optimization of Clustering in Gene Expression Data Analysis. Bioinformatics 20, 1535–1545 (2004)
Jiang, D., Tang, C., Zhang, A.: Cluster Analysis for Gene Expression Data: A Survey. IEEE Trans. Knowledge and Data Engineering 16, 1370–1386 (2004)
Bandyopadhyay, S., Maulik, U.: Genetic Clustering for Automatic Evolution of Clusters and Application to Image Classification. Pattern Recognition 35, 1197–1208 (2002)
Povinelli, R.J., Feng, X.: A New Temporal Pattern Identification Method for Characterization and Prediction of Complex Time Series Events. IEEE Transactions on Knowledge and Data Engineering 15, 339–352 (2003)
Golub, T.R., Slonim, D.K.: Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring. Science 286, 531–537 (1999)
Maulik, U., Bandyopadhyay, S.: Genetic Algorithm Based Clustering Technique. Pattern Recognition 33, 1455–1465 (2000)
Maulik, U.: Performance Evaluation of Some Clustering Algorithms and Validity Indices. IEEE trans. Pattern Analysis and Machine Intelligence 24, 1650–1654 (2002)
Famili, A.F., Liu, G., Liu, Z.: Evaluation and Optimization of Clustering in Gene Expression Data Analysis. Bioinformatics 20, 1535–1545 (2004)
Lukashin, A.V., Fuchs, R.: Analysis of Temporal Gene Expression Profiles: Clustering by Simulated Annealing and Determining The Optimal Number of Clusters. Bioinformatics 17, 405–414 (2001)
Arima, C., Hanai, T.: Gene Expression Analysis Using Fuzzy K-Means Clustering. Genome Informatics 14, 334–335 (2003)
Eisen, M.B., Spellman, P.T., Brown, P.O.: David Boststein, Cluster Analysis and Display of Genome-wide Expression Patterns. Proceedings of the National Academy of Sciences of the United States of America 95, 14863–14868 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ma, Y., Peng, Y. (2006). Co-expression Gene Discovery from Microarray for Integrative Systems Biology. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_88
Download citation
DOI: https://doi.org/10.1007/11811305_88
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37025-3
Online ISBN: 978-3-540-37026-0
eBook Packages: Computer ScienceComputer Science (R0)