Abstract
Bioinformatics is a new area of research in which many computer scientists are working to extract some useful information from genome sequences in a very less time, whereas traditional methods may take years to fetch this. One of the studies that belongs to the area of Bioinformatics is protein sequence analysis. In this study, we have considered the soybean protein sequence which does not have class information therefore clustering of these sequences is required. As these sequences are very complex and consist of overlapping sequences, therefore Fuzzy C-Means algorithm may work better than crisp clustering. However, the clustering of these sequences is a very time-consuming process also the results are not up to the mark by using existing crisp and fuzzy clustering algorithms. Therefore we propose here a quantum Fuzzy c-Means algorithm that uses the quantum computing concept to represent the dataset in the quantum form. The proposed approach also use the quantum superposition concept which fastens the process and also gives better result than the FCM algorithm.
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
Alawneh, L., Shehab, M.A., Al-Ayyoub, M., Jararweh, Y., Al-Sharif, Z.A.: A scalable multiple pairwise protein sequence alignment acceleration using hybrid CPU-GPU approach. Clust. Comput. 23, 2677–2688 (2020)
de Almeida Paiva, V .: Protein structural bioinformatics: an overview. Comput. Biol. Med., 105695 (2022)
Bystrof, C., Thorsson, V., Baker, D.: HMMSTR: a hidden Markov model for local sequence-structure correlations in proteins. J. Mol. Biol. 301(1), 173–190 (2000)
Jha, P., Tiwari, A., Bharill, N., Ratnaparkhe, M., Mounika, M., Nagendra, N.: Apache Spark based kernelized fuzzy clustering framework for single nucleotide polymorphism sequence analysis. Comput. Biol. Chem. 92, 107454 (2021)
Farhangi E., Ghadiri N., Asadi M., Nikbakht M.A., Pitre S.: Fast and scalable protein motif sequence clustering based on Hadoop framework. In: 3th International Conference on Web Research (ICWR), pp. 24–31. IEEE (2017)
Bezde J.C.: Fuzzy-Mathematics In Pattern Classification. Cornell University (1973)
Pakhira, M.K., Bandyopadhyay, S., Maulik, U.: A study of some fuzzy cluster validity indices, genetic clustering and application to pixel classification. Fuzzy Sets Syst. 155(2), 191–214 (2005)
Shelokar, P.S., Jayaraman, V.K., Kulkarni, B.D.: An ant colony approach for clustering. Anal. Chim. Acta 509(2), 187–195 (2004)
Kao, Y.T., Zahara, E., Kao, I.W.: A hybridized approach to data clustering. Expert Syst. Appli. 34(3), 1754–1762 (2008)
Zhang, C., Ouyang, D., Ning, J.: An artificial bee colony approach for clustering. Expert Syst. Appli. 37(7), 4761–4767 (2010)
Patel O.P., Bharill N., Tiwari A.: A Quantum-inspired fuzzy based evolutionary algorithm for data clustering. In: 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Istanbul, Turkey, 2015, pp. 1–8 (2015). https://doi.org/10.1109/FUZZ-IEEE.2015.7337861
Bolshakova, N., Azuaje, F.: Cluster validation techniques for genome expression data. Signal Process. 83(4), 825–833 (2003)
Coelho G.P., Barbante C.C., Boccato L., Attux R.R., Oliveira J.R., Von Zuben F.J.: Automatic feature selection for BCI: an analysis using the davies-bouldin index and extreme learning machines. In: The 2012 international joint conference on neural networks (IJCNN), vol. 2012, pp. 1–8. IEEE (2012)
Han, H.K., Kim, J.H.: Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. IEEE Trans. Evol. Comput. 6(6), 580–593 (2002)
Peter W.: Quantum machine learning: what quantum computing means to data mining, vol. 2014. Academic Press (2014)
Wysmierski, P.T., Vello, N.A.: The genetic base of Brazilian soybean cultivars: evolution over time and breeding implications. Genet. Mol. Biol. 36, 547–555 (2013)
Sedivy, E.J., Wu, F., Hanzawa, Y.: Soybean domestication: the origin, genetic architecture and molecular bases. New Phytol. 214(2), 539–553 (2017)
Lee J.D., Shannon J.G., Vuong T.D., Nguyen H.T.: Inheritance of salt tolerance in wild soybean (Glycine soja Sieb. and Zucc.) accession PI483463. J. Heredity, 100(6), 798–801 (2009)
Xie, M., et al.: A reference-grade wild soybean genome. Nat. Commun. 10(1), 1216 (2019)
Yeung, K.Y., Ruzzo, W.L.: Details of the adjusted rand index and clustering algorithms, supplement to the paper an empirical study on principal component analysis for clustering gene expression data. Bioinformatics 17(9), 763–774 (2001)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Rangoju, S.S.V.D., Garg, K., Dandi, R., Patel, O.P., Bharill, N. (2024). Soybean Genome Clustering Using Quantum-Based Fuzzy C-Means Algorithm. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14450. Springer, Singapore. https://doi.org/10.1007/978-981-99-8070-3_7
Download citation
DOI: https://doi.org/10.1007/978-981-99-8070-3_7
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8069-7
Online ISBN: 978-981-99-8070-3
eBook Packages: Computer ScienceComputer Science (R0)