Abstract
The important research objective of identifying genes with similar behavior with respect to different conditions has recently been tackled with biclustering techniques. In this paper we introduce a new approach to the biclustering problem using the Possibilistic Clustering paradigm. The proposed Possibilistic Biclustering algorithm finds one bicluster at a time, assigning a membership to the bicluster for each gene and for each condition. The biclustering problem, in which one would maximize the size of the bicluster and minimizing the residual, is faced as the optimization of a proper functional. We applied the algorithm to the Yeast database, obtaining fast convergence and good quality solutions. We discuss the effects of parameter tuning and the sensitivity of the method to parameter values. Comparisons with other methods from the literature are also presented.
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.
Similar content being viewed by others
References
Madeira, S.C., Oliveira, A.L.: Biclustering algorithms for biological data analysis: A survey. IEEE Transactions on Computational Biology and Bioinformatics 1, 24–45 (2004)
Cheng, Y., Church, G.M.: Biclustering of expression data. In: Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology, pp. 93–103. AAAI Press, Menlo Park (2000)
Hartigan, J.A.: Direct clustering of a data matrix. Journal of American Statistical Association 67(337), 123–129 (1972)
Kung, S.Y., Mak, M.W., Tagkopoulos, I.: Multi-metric and multi-substructure biclustering analysis for gene expression data. In: Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference (CSB 2005) (2005)
Turner, H., Bailey, T., Krzanowski, W.: Improved biclustering of microarray data demonstrated through systematic performance tests. Computational Statistics and Data Analysis 48(2), 235–254 (2005)
Peeters, R.: The maximum edge biclique problem is NP-Complete. Discrete Applied Mathematics 131, 651–654 (2003)
Yang, J., Wang, H., Wang, W., Yu, P.: Enhanced biclustering on expression data. In: Proceedings of the Third IEEE Symposium on BioInformatics and Bioengineering (BIBE 2003), pp. 1–7 (2003)
Tanay, A., Sharan, R., Shamir, R.: Discovering statistically significant biclusters in gene expression data. Bioinformatics 18, S136–S144 (2002)
Zhang, Z., Teo, A., Ooi, B.C.a.: Mining deterministic biclusters in gene expression data. In: Proceedings of the Fourth IEEE Symposium on Bioinformatics and Bioengineering (BIBE 2004), pp. 283–292 (2004)
Mitra, S., Banka, H.: Multi-objective evolutionary biclustering of gene expression data (to appear, 2006)
Bryan, K., Cunningham, P., Bolshakova, N.: Biclustering of expression data using simulated annealing. In: 18th IEEE Symposium on Computer-Based Medical Systems (CBMS 2005), pp. 383–388 (2005)
Krishnapuram, R., Keller, J.M.: A possibilistic approach to clustering. IEEE Transactions on Fuzzy Systems 1(2), 98–110 (1993)
Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. Wiley, Chichester (1973)
Kohonen, T.: Self-Organizing Maps. Springer, New York (2001)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Kluwer Academic Publishers, Norwell (1981)
Rose, K., Gurewwitz, E., Fox, G.: A deterministic annealing approach to clustering. Pattern Recogn. Lett. 11(9), 589–594 (1990)
Runkler, T.A., Bezdek, J.C.: Alternating cluster estimation: a new tool for clustering and function approximation. IEEE Transactions on Fuzzy Systems 7(4), 377–393 (1999)
Krishnapuram, R., Keller, J.M.: The possibilistic c-means algorithm: insights and recommendations. IEEE Transactions on Fuzzy Systems 4(3), 385–393 (1996)
Masulli, F., Schenone, A.: A fuzzy clustering based segmentation system as support to diagnosis in medical imaging. Artificial Intelligence in Medicine 16(2), 129–147 (1999)
Nasraoui, O., Krishnapuram, R.: Crisp interpretations of fuzzy and possibilistic clustering algorithms, Aachen, Germany, vol. 3, pp. 1312–1318 (1995)
Tavazoie, S., Hughes, J.D., Campbell, M.J., Cho, R.J., Church, G.M.: Systematic determination of genetic network architecture. Nature Genetics 22(3) (1999)
Ball, C.A., Dolinski, K., Dwight, S.S., Harris, M.A., Tarver, L.I., Kasarskis, A., Scafe, C.R., Sherlock, G., Binkley, G., Jin, H., Kaloper, M., Orr, S.D., Schroeder, M., Weng, S., Zhu, Y., Botstein, D., Cherry, M.J.: Integrating functional genomic information into the saccharomyces genome database. Nucleic Acids Research 28(1), 77–80 (2000)
Aach, J., Rindone, W., Church, G.: Systematic management and analysis of yeast gene expression data (2000)
R Foundation for Statistical Computing Vienna, Austria: R: A language and environment for statistical computing (2005)
Bleuler, S., Prelić, A., Zitzler, E.: An EA framework for biclustering of gene expression data. In: Congress on Evolutionary Computation (CEC 2004), Piscataway, NJ, pp. 166–173. IEEE, Los Alamitos (2004)
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
Filippone, M., Masulli, F., Rovetta, S., Mitra, S., Banka, H. (2006). Possibilistic Approach to Biclustering: An Application to Oligonucleotide Microarray Data Analysis. In: Priami, C. (eds) Computational Methods in Systems Biology. CMSB 2006. Lecture Notes in Computer Science(), vol 4210. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11885191_22
Download citation
DOI: https://doi.org/10.1007/11885191_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46166-1
Online ISBN: 978-3-540-46167-8
eBook Packages: Computer ScienceComputer Science (R0)