Abstract
In this paper, we address the concept mining of binary gene expression data. To deal with this problem, we first compute the left and right singular vector matrices from the input binary gene expression matrix, and then information entropy is employed to determine whether column-clustering or row-clustering is performed first. Finally, the column-clustering and the row-clustering are repeated iteratively until the stopping criterion is satisfied. Experimental results show that our algorithm can identify the non-overlapping biclusters effectively.
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
Amela, P., Stefan, B., Phillip, Z., et al.: A Systematic Comparison and Evaluation of Biclustering Methods for Gene Expression Data. Bioinformatics 22(9), 1122–1129 (2006)
Mehmet, K., Wojciech, S., Ananth, G.: Biclustering Gene-Feature Matrices for Statistically Significant Dense Patterns. In: IEEE Comput. Sys. Bio. Coference (CSB 2004), pp. 480–484 (2004)
Uitert, M., Meuleman, W., Wessels, L.: Biclustering sparse Binary Genomic Data. Journal of Comput. Bio. 15, 1329–1345 (2008)
Michael, B., Douglas, S.: A Tabu-Search Heuristic for Deterministic Two-Mode Blockmodeling of Binary Network Matrices. Psyhometrika 76(4), 612–633 (2011)
Ruggero, G.P., Celine, R., Jean-Francois, B.: Constraint-driven co-clustering of 0/1 data. CRC Press LLC (2008)
Domingo, S.R., Antonio, J.P., Jesus, S.A.: A Biclustering Algorithm for Extracting Bit-Pattern from Binary Datasets. Bioinformatics 27(19), 2733–2745 (2011)
Lee, S., Huang, J.Z.: A biclustering algorithm for binary matrices based on penalized Bernoulli likelihood. Statistics and Computing (2013)
Chen, H.C., Zou, W., Tien, Y.J., Chen, J.J.: Identification of Bicluster Regions in a Binary Matrix and Its Applications. PLoS ONEÂ 8(8), e71680
Jeremyn, B., Celine, R., Jean-Francois, B., Sophie, R.: Constraint-based concept mining and its application to microarray data analysis. Intelligent Data Analysis 9(1), 59–82 (2005)
Wille, R.: Restructuring lattice theory: an approach based on hierarchies of concepts. In: Rival, I. (ed.) Ordered Sets, pp. 445–470 (1982)
Ng, R., Lakshmanan, L., Han, J., Pang, A.: Exploratory mining and pruning optimizations of constrained associations rules. In: Proceedings ACM SIGMOD 1998, pp. 13–24. ACM Press (1998)
Alter, O., Brown, P.O., Botstein, D.: Singular value decomposition for genome-wide expression data processing and modeling. PNAS 97(18), 10101–10106 (2000)
Bagyamani, J., Hangavel, K., Rathipriya, T.R.: Biological Significance of Gene Expression Data using Similarity based Biclustering Algorithm. International Journal of Biometrics and Bioinformatics 4(6), 201–216 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
He, P., Xu, X., Ju, Y., Lu, L., Xi, Y. (2014). Concept Mining of Binary Gene Expression Data. In: Huang, DS., Han, K., Gromiha, M. (eds) Intelligent Computing in Bioinformatics. ICIC 2014. Lecture Notes in Computer Science(), vol 8590. Springer, Cham. https://doi.org/10.1007/978-3-319-09330-7_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-09330-7_16
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09329-1
Online ISBN: 978-3-319-09330-7
eBook Packages: Computer ScienceComputer Science (R0)