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Critical Gene Selection by a Modified Particle Swarm Optimization Approach

  • Biswajit Jana
  • Sriyankar AcharyaaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11942)

Abstract

Gene selection is an eminent area of research in computational biology. Identifying disease critical genes analysing large gene expression dataset is a challenging problem in computational biology. Application of meta-heuristics is considered to be efficient to attempt such problem. Particle Swarm Optimization (PSO) is a meta-heuristic technique based on swarm intelligence. An improved version of PSO, namely, New Variant Repository and Mutation based PSO (NVRMPSO) combined with kNN (NVRMPSO-kNN) has been applied here to find the subset of genes (disease critical) from gene expression dataset. The modification has been done on a recent PSO version, referred as, Repository and Mutation based PSO (RMPSO). The kNN algorithm has been used for sample classification. The performance of proposed NVRMPSO-kNN has been compared with RMPSO incorporated with kNN (RMPSO-kNN) in gene selection problem. The RMPSO-kNN and proposed NVRMPSO-kNN have been applied to three different sizes of datasets, numbered as, E-MEXP-1050, GSE60438 and GSE10588 of preeclampsia disease. The experimental results reveal that the efficacy of NVRMPSO-kNN is better than that of RMPSO-kNN when Classification Accuracy is considered. Furthermore, the performance of proposed NVRMPSO-kNN has been validated with the state-of-the-art observations.

Keywords

Gene selection Gene expression PSO RMPSO kNN 

References

  1. 1.
    Heller, M.J.: DNA microarray technology: devices, systems, and applications. Annu. Rev. Biomed. Eng. 4(1), 129–153 (2002)CrossRefGoogle Scholar
  2. 2.
    Biswas, S., Dutta, S., Acharyya, S.: Identification of disease critical genes using collective meta-heuristic approaches: an application to preeclampsia. Interdisc. Sci. Comput. Life Sci. 1–16 (2017)Google Scholar
  3. 3.
    Saha, S., Biswas, S., Acharyya, S.: Gene selection by sample classification using k nearest neighbor and meta-heuristic algorithms. In: 2016 IEEE 6th International Conference on Advanced Computing (IACC), pp. 250–255. IEEE, February 2016Google Scholar
  4. 4.
    Chen, X.W.: Gene selection for cancer classification using bootstrapped genetic algorithms and support vector machines. In: Proceedings of the 2003 IEEE Bioinformatics Conference. CSB 2003 on Computational Systems Bioinformatics, CSB 2003, pp. 504–505. IEEE, August 2003Google Scholar
  5. 5.
    Alba, E., Garcia-Nieto, J., Jourdan, L., Talbi, E.G.: Gene selection in cancer classification using PSO/SVM and GA/SVM hybrid algorithms. In: 2007 IEEE Congress on Evolutionary Computation, pp. 284–290. IEEE, September 2007Google Scholar
  6. 6.
    Alomari, O.A., Khader, A.T., Al-Betar, M.A., Abualigah, L.M.: Gene selection for cancer classification by combining minimum redundancy maximum relevancy and bat-inspired algorithm. Int. J. Data Min. Bioinform. 19(1), 32–51 (2017)CrossRefGoogle Scholar
  7. 7.
    Moosa, J.M., Shakur, R., Kaykobad, M., Rahman, M.S.: Gene selection for cancer classification with the help of bees. BMC Med. Genomics 9(2), 47 (2016)CrossRefGoogle Scholar
  8. 8.
    Kar, S., Sharma, K.D., Maitra, M.: Gene selection from microarray gene expression data for classification of cancer subgroups employing PSO and adaptive K-nearest neighborhood technique. Expert Syst. Appl. 42(1), 612–627 (2015)CrossRefGoogle Scholar
  9. 9.
    Dashtban, M., Balafar, M., Suravajhala, P.: Gene selection for tumor classification using a novel bio-inspired multi-objective approach. Genomics 110(1), 10–17 (2018)CrossRefGoogle Scholar
  10. 10.
    Pyingkodi, M., Thangarajan, R.: Informative gene selection for cancer classification with microarray data using a metaheuristic framework. Asian Pac. J. Cancer Prev. APJCP 19(2), 561 (2018)Google Scholar
  11. 11.
    Jana, B., Mitra, S., Acharyya, S.: Repository and Mutation based Particle Swarm Optimization (RMPSO): a new PSO variant applied to reconstruction of Gene Regulatory Network. Appl. Soft Comput. 74, 330–355 (2019)CrossRefGoogle Scholar
  12. 12.
    Ching, T., et al.: Genome-wide hypermethylation coupled with promoter hypomethylation in the chorioamniotic membranes of early onset pre-eclampsia. Mol. Hum. Reprod. 20(9), 885–904 (2014)CrossRefGoogle Scholar
  13. 13.
    Vaiman, D., Miralles, F.: An integrative analysis of preeclampsia based on the construction of an extended composite network featuring protein-protein physical interactions and transcriptional relationships. PLoS ONE 11(11), e0165849 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Maulana Abul Kalam Azad University of TechnologyKolkataIndia

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