Abstract
An evolutionary methodology using multiple sequence alignment technique with optimal search algorithms particle swarm optimization and differential evaluation is proposed in this paper. Proposed methodology algorithms are termed as Multiple Sequence Alignment and Particle Swarm Optimization (MSAPSO) and Multiple Sequence Alignment and Differential Evaluation (MSADE). These techniques are developed to categorize gene sequences based on optimal result produced for each generation. These evolutionary techniques encompasses of two phases in designing. In first phase MSA is pragmatic on pair wise sequences to generate aligned sequence as output. The sequence generated as output in the first level will be given as input to second phase. In the second segment optimal search algorithms PSO or DE are applied on sequences which generate optimal value and generation best value for each generation. These values are considered for further categorization. This paper presents analysis of MSAPSO and MSADE on gene sequences.
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
J. Ferlay, I. Soerjomataram, M. Ervik, R. Dikshit, S. Eser, C. Mathers, Cancer incidence and mortality worldwide international agency for research on cancer. IARC CancerBase No. 11. Lyon; France (2013). Accessed 1 Jan 2016. GLOBOCAN 2012 v1.0, http://globocan.iarc.fr
World Cancer Report, International Agency for Research on Cancer (2008). Retrieved 26 Feb 2011
Top 100 Cited Classic Articles in Breast Cancer Research, Eur. J. Breast Health 13(3), 129–137 (2017). Published online 2017 Jul 1. https://doi.org/10.5152/ejbh.2017.3480
O. Hoffman, L. Fejerman, D. Hu, S. Huntsman, M. Li, E.M. John, G. Torres-Mejia, E. ZivEmail, Identification of novel common breast cancer risk variants at the 6q25 locus among Latinas. ORCID, profile Breast Cancer Research 201921:3 https://doi.org/10.1186/s13058-018-1085-9 © 4 December 2018. Published: 14 Jan 2019
P. Apostolou, F. Fostira, Hereditary breast cancer: the era of new susceptibility genes. Biomed. Res. Int. 747318 (2013). Published online 2013 Mar 21. https://doi.org/10.1155/2013/747318. PMCID: PMC3618918PMID: 23586058
T. Walsh, M.K. Lee, S. Casadei, A.M. Thornton, S.M. Stray, C. Pennil, A.S. Nord, J.B. Mandell, E.M. Swisher, M.-C. Kinga, Detection of inherited mutations for breast and ovarian cancer using genomic capture and massively parallel sequencing. Proc. Nat. Acad. Sci. USA 107(28), 12629–12633 (2010). Published online 2010 https://doi.org/10.1073/pnas.1007983107. PMCID: PMC2906584 PMID: 20616022
S. Shiovitz, L.A. Korde, Genetics of breast cancer: a topic in evolution. Ann. Oncol. 26(7), 1291–1299. Published online 2015 Jan 20. https://doi.org/10.1093/annonc/mdv022. PMCID: PMC4478970 PMID: 25605744
S. Mitra, Y. Hayashi, Bioinformatics with soft computing. 36(5), 616–635 (2006). Date of Publication: 21 Aug 2006 ISSN Information: INSPEC Accession Number: 9049333 https://doi.org/10.1109/tsmcc.2006.879384
R.K. Jena, M.M. Aqel, P. Srivastava, P.K. Mahanti, Soft computing methodologies in bioinformatics. Eur. J. Sci. Res. 26(2), 189–203 (2009). http://www.eurojournals.com/ejsr.html, ISSN 1450-216X
L.N.J. Gavarraju, P. Jeevana Jyothi, K. Karteeka Pawan, A literature survey on multiple sequence alignment algorithms. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 6(3) (2016). ISSN: 2277 128X
K. Lohitha Lakshmi, P. Bhargavi, S. Jyothi, Soft computing techniques for gene annotation. Int. J. Latest Eng. Manag. Res. (IJLEMR) 03(02), 26–34 (2018). www.ijlemr.com, ISSN: 2455–4847
K. Bhargavi, S. Jyothi, Classification of DNA sequence using soft computing techniques: a survey. Indian J. Sci. Technol. 9(47). https://doi.org/10.17485/ijst/2016/v9i47/89343, ISSN (Print): 0974-6846, ISSN (Online): 0974-5645
K. Lohitha Lakshmi, P. Bhargavi, S. Jyothi, An analysis of breast cancer DNA sequences using particle swam optimization. Int. J. Eng. Technol. 7(4.7), 335–338 (2018). www.sciencepubco.com/index.php/IJET/Researchpaper
S. Cheng, Y. Shi, Q. Qin, Population diversity based study on search information propagation in particle swarm optimization, in Proceedings of 2012 IEEE Congress on Evolutionary Computation, (CEC 2012) (2012). IEEE, Brisbane, Australia, pp. 1272–1279
A. Kamal, A. TarekSayed, M. Mahroos, Sequence Alignment Using Parallel Particle Swarm Optimization. Content uploaded by Mohsen Mahroos (2012)
Z. Ibrahim, L.K. Sheng, F. Naim, M.F.M. Jusof, N.W. Arshad, An analysis of archive update for vector evaluated particle swarm optimization. Int. J. Intell. Syst. Appl. Eng. Adv. Technol. Sci. www.atscience.org/IJISAE,Accepted 15th Aug 2014, https://doi.org/10.18201/ijisae.48588, ISSN: 2147-67992147-6799
D.R. Penas, J.R. Banga, P. Gonz´alez, R. Doallo, A parallel differential evolution algorithm for parameter estimation in dynamic models of biological systems, ed. by J. S´aez-Rodr´ıguez et al. 8th International Conference on Practical Application of Computer 173 Biology and Bioinformation (PACBB 2014), Advances in Intelligent Systems and Computing 294. https://doi.org/10.1007/978-3-319-07581-5_21, © Springer International Publishing Switzerland 2014
K. Lohitha Lakshmi, P. Bhargavi, S. Jyothi, An Analysis of Breast Cancer Gene Sequences using Differential Evaluation. https://doi.org/10.5013/ijssst.a.20.01.35.35.1, ISSN: 1473-804x online, 1473-8031 print
M. Chatzou, C. Magis, J.M. Chang, C. Kemena, G. Bussott, Multiple sequence alignment modelling: methods and applications. Brief. Bioinf. (6) (2015). https://doi.org/10.1093/bib/bbv099
F. Sviatopolk-MirskyPais, P. de CássiaRuy, G. Oliveira, R.S. Coimbra, Assessing the efficiency of multiple sequence alignment programs. Algorithms Mol. Biol. 9, 4 (2014). Published online 2014 Mar 6. https://doi.org/10.1186/1748-7188-9-4
T.O. Ting, X.-S. Yang, S. Cheng, K. Huang, Hybrid Meta Heuristics Algorithms: Past, Present, and Future. First Online: 28 Dec 2014
S. Lalwani, R. Kumar, N. Gupta, A review on particle swarm optimization variants and their applications to multiple sequence alignment. J. Appl. Math. Bioinf. 3(2), 87–124 (2013). ISSN: 1792-6602 (print), 1792-6939 (online)
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
Lohitha Lakshmi, K., Bhargavi, P., Jyothi, S. (2020). An Evolutionary Optimization Methodology for Analyzing Breast Cancer Gene Sequences Using MSAPSO and MSADE. In: Jyothi, S., Mamatha, D., Satapathy, S., Raju, K., Favorskaya, M. (eds) Advances in Computational and Bio-Engineering. CBE 2019. Learning and Analytics in Intelligent Systems, vol 15. Springer, Cham. https://doi.org/10.1007/978-3-030-46939-9_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-46939-9_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-46938-2
Online ISBN: 978-3-030-46939-9
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)