Abstract
An essential step in the analysis of gene expression profiles is the identification of sets of co-regulated genes or genes tend to be active under only subsets of experimental conditions or participate in multiple cellular processes or functions. Biclustering is a non-supervised technique exceeds the traditional clustering techniques because it can find groups of both genes and conditions simultaneously. In this paper, we proposed a biclustering algorithm called Correlation-Based Dilated Biclusters CBDB to find sets of biclusters with correlated gene expression patterns. This algorithm has many phases starting with the preprocessing phase, determination of elementary biclusters, then the dilation phase depending on a heuristic searching approach with Pearson correlation coefficient as a measure of coherency, after that, the removal phase to exclude sets of genes and conditions that show low level of coherency, finally, the elimination of duplicated and overlapped biclusters phase. This approach showed reasonable results on both synthetic and real datasets compared with other correlation-based biclustering techniques.
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
References
Dziuda, D.: Data Mining for Genomics and Proteomics: Analysis of Gene and Protein Expression Data, 1st edn. Wiley, New York (2010)
Dumancas, G., Adrianto, I., Bello, G., Dozmorov, M.: Current developments in machine learning techniques in biological data mining. Bioinform. Biol. Insights. 11 (2017)
Chen, J., Lonardi, S.: Biological Data Mining. In: Chapman and Hall/CRC Data Mining and Knowledge Discovery Series, 1st edn. CRC Press (2017)
Iswarya Lakshmi, K., Chandran, C.: Biclustering approaches for prediction of class discovery from gene expression data. In: Proceeding of International Seminar on Emerging Trends and Innovative Technologies in Biological Sciences (2011)
Beatriz, P., Raúl, G., Aguilar-Ruiz, J.: Biclustering on expression data: a review. J. Biomed. Inform. 57, 163–180 (2015)
Mounir, M., Hamdy, M.: On biclustering of gene expression data. In: IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS), Cairo, Egypt, pp. 641–648 (2015)
Madeira, S., Oliveira, A.: Biclustering algorithms for biological data analysis: a survey. IEEE/ACM Comput. Biol. Bioinf. 1, 24–45 (2004)
Ben Saber, H., Elloumi, M.: A new study on biclustering tools, biclusters validation and evaluation functions. Int. J. Comput. Sci. Eng. Surv. (IJCSES) 6(1), 1–13 (2015)
Erten, C., Sözdinler, M.: Improving performances of suboptimal greedy iterative biclustering heuristics via localization. Bioinformatics 26, 2594–2600 (2010)
Denittoa, M., Farinellia, A., Figueiredob, M., Bicego, M.: A biclustering approach based on factor graphs and the max-sum algorithm. Pattern Recogn. 62, 114–124 (2017)
Aguilar-Ruiz, J.: Shifting and scaling patterns from gene expression data. Bioinformatics 21, 3840–3845 (2005)
Allocco, D., Kohane, I.S., Butte, A.J.: Quantifying the relationship between co-expression, co-regulation and gene function. BMC Bioinformatics 5, 18 (2004)
Bhattacharya, A., De, K.: Bi-correlation clustering algorithm for determining a set of co-regulated genes. Bioinformatics 25, 2795–2801 (2009)
Yun, T., Yi, G.S.: Biclustering for the comprehensive search of correlated gene expression patterns using clustered seed expansion. BMC Genomics 14(1), 144 (2013)
Zhang, Y., Xie, J., Yang, J., Fennell, A., Zhang, C., Ma, Q.: QUBIC: a bioconductor package for qualitative biclustering analysis of gene co-expression data. Bioinformatics 33(3), 450–452 (2017)
Bentham, R., Bryson, K., Szabadkai, G.: MCbiclust: a novel algorithm to discover large-scale functionally related gene sets from massive transcriptomics data collections. Nucleic Acids Res. 45(15), 8712–8730 (2017)
Henriques, R., Ferreira, F., Madeira, S.: BicPAMS: software for biological data analysis with pattern-based biclustering. BMC Bioinf. 18(1), 82 (2017)
Eren, K., Deveci, M., Küçüktunç, O., Çatalyürek, Ü.: A comparative analysis of biclustering algorithms for gene expression data. Brief. Bioinform. 14, 279–292 (2012)
Rodrigo, S., Luis, Q., Roberto, T.: Methods to bicluster validation and comparison in microarray data. In: The Proceeding of 8th International Conference in Intelligent Data Engineering and Automated Learning - IDEAL 2007, 16–19 December, Birmingham, UK, pp. 780–789 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Mounir, M., Hamdy, M., Khalifa, M.E. (2020). Identifying Different Types of Biclustering Patterns Using a Correlation-Based Dilated Biclusters Algorithm. In: Hassanien, A., Azar, A., Gaber, T., Bhatnagar, R., F. Tolba, M. (eds) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019). AMLTA 2019. Advances in Intelligent Systems and Computing, vol 921. Springer, Cham. https://doi.org/10.1007/978-3-030-14118-9_26
Download citation
DOI: https://doi.org/10.1007/978-3-030-14118-9_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-14117-2
Online ISBN: 978-3-030-14118-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)