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Review on the Usage of Swarm Intelligence in Gene Expression Data

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Book cover 2nd International Conference for Innovation in Biomedical Engineering and Life Sciences (ICIBEL 2017)

Part of the book series: IFMBE Proceedings ((IFMBE,volume 67))

Abstract

This paper presents a review of the recent usage of swarm intelligence for optimizing feature selection in microarray data focusing on its application for cancer detection and classification. The feature selection technique is used in the analysis of microarray so that only useful data is trained for further analysis and prediction. The process of feature selection would affect the effectiveness of the classification. This is due to the enormous quantity of genes being expressed at the same time. An optimized feature selection would ensure a high accuracy of classification. Swarm intelligence has been effective in solving feature selection and classification problems. This paper also gives overview on the sources of microarray data which are used in the literature.

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References

  1. Mutingi, M., Mbohwa, C.: A Fuzzy-based particle swarm optimization algorithm for nurse scheduling. In: Proceedings of the World Congress on Engineering and Computer Science, pp. 22–24. (2014)

    Google Scholar 

  2. Chuang, L., Lin, Y., Yang, C., Swarm, A.P., Pso, O.: An improved particle swarm optimization for data clustering. In: Proceedings of the International MultiConference of Engineers and Computer Scientist, pp. 1–6. (2012)

    Google Scholar 

  3. Ali, R.S., Almousawi, A.K.: Design an optimal PID controller using artificial bee colony and genetic algorithm for autonomous mobile robot. Int. J. Comput. Appl. 100(16), 8–16 (2014)

    Google Scholar 

  4. Larran, P., Saeys, Y.: Gene expression—a review of feature selection techniques in bioinformatics. Bioinformatics 23(9), 2507–2517 (2007)

    Google Scholar 

  5. Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J.P., Coller, H., Loh, M.L., Downing, J.R., Caligiuri, M.A., Bloomfield, C.D., Lander, E.S.: Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science AAAS 286(5439), 531–537 (1999)

    Google Scholar 

  6. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)

    Google Scholar 

  7. Christian Blum, D.M.: Swarm Intelligence—Introduction and Applications. Springer, Berlin (2008)

    Book  MATH  Google Scholar 

  8. Jérôme, L.J.D., Onwunalu, E.: Application of a particle swarm optimization algorithm for determining optimum well location and type. Comput. Geosci. 14(1), 183–198 (2010)

    Article  MATH  Google Scholar 

  9. Matekovits, L., Mussetta, M., Pirinoli, P., Selleri, S., Zich, R.E.: Improved PSO algorithms for electromagnetic optimization. In: IEEE antennas and propagation society international symposium, pp. 33–36. (2005)

    Google Scholar 

  10. Cristian, D., Barbulescu, C., Kilyeni, S., Popescu, V.: Particle swarm optimization techniques. Power systems applications. In: 2013 6th international conference on human system interactions (HSI), Sopot, pp. 312–319. (2013)

    Google Scholar 

  11. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Global Optim. 39, 459–471 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  12. Bai, Q.: Analysis of particle swarm optimization algorithm. Comput. Inf. Sci. 3(1), 180–184 (1998)

    MathSciNet  Google Scholar 

  13. Sahu, B., Mishra, D.: A novel feature selection algorithm using particle swarm optimization for cancer microarray data. Procedia Eng. 38, 27–31 (2012)

    Article  Google Scholar 

  14. Kar, S., Das Sharma, K., 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)

    Article  Google Scholar 

  15. Banka, H., Dara, S.: A Hamming distance based binary particle swarm optimization (HDBPSO) algorithm for high dimensional feature selection, classification and validation. Pattern Recogn. Lett. 52, 94–100 (2015)

    Article  Google Scholar 

  16. Sekhara, C., Annavarapu, R., Dara, S., Banka, H.: Original article: cancer microarray data feature selection using multi-objective binary particle swarm optimization algorithm. EXCLI J. 15, 460–473 (2016)

    Google Scholar 

  17. Chen, K., Wang, K.-J., Tsai, M.-L., Wang, K.-M., Adrian, A.M., Cheng, W.-C., Yang, T.-S., Teng, N.-C., Tan, K.-P., Chang, K.-S.: Gene selection for cancer identification: A decision tree model empowered by particle swarm optimization algorithm. BMC Bioinform. 15, 0–9 (2014)

    Google Scholar 

  18. Yasodha, P., Anathanarayanan, N.R.: Analysing big data to build knowledge based system for early detection of ovarian cancer. Indian J. Sci. Technol. 8 (2015)

    Google Scholar 

  19. Garro, B.A., Rodríguez, K., Vázquez, R.A.: Classification of DNA microarrays using artificial neural networks and ABC algorithm. Appl. Soft Comput. 38, 548–560 (2016)

    Article  Google Scholar 

  20. Alshamlan, H.M., Badr, G.H., Alohali, Y.A.: Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification. Comput. Biol. Chem. 56, 49–60 (2015)

    Article  Google Scholar 

  21. Alshamlan, H., Al-Ohali, Y., Badr, G.: mRMR-ABC: A hybrid gene selection algorithm for cancer classification using microarray gene expression profiling. Hindawi 2015, (2015)

    Google Scholar 

  22. Ganesh kumar, P., Rani, C., Devaraj, D., Victoire, T.A.A.: Hybrid ant bee algorithm for fuzzy expert system based sample classification. IEE Trans. Comput. Biol. Bioinform. 11(2), 347–360 (2014)

    Article  Google Scholar 

  23. Pal, N.S.: Robot path planning using swarm intelligence: A survey. Int. J. Comput. Appl. 83(12), 5–12 (2013)

    Google Scholar 

  24. Manoharan, G.V., Shanmugalakshmi, R.: Multi-objective firefly algorithm for multi-class gene selection. Indian J. Sci. Technol. 8, 27–34 (2015)

    Article  Google Scholar 

  25. Bolon-Canedo, F.H.V., Sanchez-Marono, N., Alonso-Betanzos, A., Benitez, J.M.: A review of microarray datasets and applied feature selection methods. Inf. Sci. 282, 111–135 (2014)

    Article  Google Scholar 

  26. GEDatasets. http://sdmc.lit.org.sg/GEDatasets/. Last accessed 11 Jan 2017

  27. Microarray cancer datasets. http://www.gems-system.org. Last accessed 11 Jan 2017

  28. Gene Expression Datasets. http://research.nhgri.nih.gov/microarray/Supplement/. Last accessed 11 Jan 2017

  29. Normalized gene expression data. http://tcga-data.nci.nih.gov/. Last accessed 11 Jan 2017

  30. Leukaemia. http://www.genome.wi.mit.edu/MPR. Last accessed 11 Jan 2017

  31. Lymphoma. http://llmpp.nih.gov/lymphoma/data/figure1/figure1.cdt. Last accessed 11 Jan 2017

  32. Colon Cancer. http://microarray.princeton.edu/oncology. Last accessed 11 Jan 2017

  33. Bloomfield, C.D., Lander, E.S., Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J.P., Coller, H., Loh, M.L., Downing, J.R., Caligiuri, M.A.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999)

    Article  Google Scholar 

  34. Rosenwald, L.M.S.A., Wright, G., Chan, W.C., Connors, J.M., Campo, E., Fisher, R.I., Gas-coyne, R.D., Muller-Hermelink, H.K., Smeland, E.B., Giltnane, J.M., Hurt, E.M., Zhao, H., Averett, L., Yang, L., Wilson, W.H., Jaffe, E.S., Simon, R., Klausner, R.D., Powell, J., Duffey, P.L.: The use of molecular profiling to predict survival after chemotherapy for diffuse large-b-cell lymphoma. N. Engl. J. Med. 346, 1937–1947 (2002)

    Article  Google Scholar 

  35. van de Vijver, M.J., He, Y.D., van ‘t Veer, L.J., Dai, H., Hart, A.A., Voskuil, D.W., Schreiber, G.J., Peterse, J.L., Roberts, C., Marton, M.J., Parrish, M., Atsma, D., Witteveen, A., Rutgers, E.T., Glash, A., Delahaye, L., van der Velde, T., Bartelink, H., Rodenhuis, S., Bernards, R., Rutgers, E.T., Friend, S.H.: A gene-expression signature as a predictor of survival in breast cancer. N. Engl. J. Med. 347, 1999–2009 (2002)

    Article  Google Scholar 

  36. van ‘t Veer, L.J., Dai, H., van de Vijver, M.J., van de Vijver, M.J., He, Y.D., Hart, A.A., Mao, M., Peterse, H.L., van der Kooy, K., Marton, M.J., Witteveen, A.T., Schreiber, G.J., Kerkhoven, R.M., Roberts, C., Linsley, P.S., Bernards, R., Friend, S.H.: Gene expression profiling predicts clinical outcome of breast cancer. Nature 415, 530–536 (2002)

    Article  Google Scholar 

  37. Singh, D., Febbo, P.G., Ross, K., Jackson, D.G., Manola, J., Ladd, C., Tamayo, P., Renshaw, A.A., D’Amico, A.V., Richie, J.P., Lander, E.S., Loda, M., Kantoff, P.W., Golub, T.R., Sellers, W.R.: Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 1, 203–209 (2002)

    Article  Google Scholar 

  38. Alon, U., Barkai, N., Notterman, D.A., Gish, K., Ybarra, S., Mack, D., Levine, A.J.: Broad patterns of gene expression revealed by clustering of tumor and normal colon tissues probed by oligoneuclotide arrays. Proc. Nat’l Acad. Sci. USA 96, 6745–6750 (1999)

    Article  Google Scholar 

  39. Alizadeh, A.A., Eisen, M.B., Davis, R.E., Ma, C., Lossos, I.S., Rosenwald, A., Boldrick, J.C., Sabet, H., Tran, T., Yu, X., Powell, J.I., Yang, L., Marti, G.E., Moore, T., Hudson Jr., J., Lu, L., Lewis, D.B., Tibshirani, R., Sherlock, G., Chan, W.C., Greiner, T.C., Weisenburger, D.D., Armitage, J.O., Warnke, R., Levy, R., Wilson, W., Grever, M.R., Byrd, J.C., Botstein, D., Brown, P.O., Staudt, L.M.: Distinct types of diffuse large b-cell lymphoma identified by gene expression profiling. Nature 403, 503–511 (2000)

    Article  Google Scholar 

  40. National Cancer Institute Homepage. https://www.cancer.gov/. Last accessed 11 Jan 2017

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Acknowledgements

We would like to thank Multimedia University for their assistance and this work is supported by FRGS grant (FRGS/1/2015/TK04/MMU/03/2).

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Authors and Affiliations

  1. Faculty of Engineering & Technology, Multimedia University, Jalan Ayer Keroh Lama, 75450, Melaka, Malaysia

    Nurhawani Ahmad Zamri, Bhuvaneswari Thangavel, Nor Azlina Ab Aziz & Nor Hidayati Abdul Aziz

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  1. Nurhawani Ahmad Zamri
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  2. Bhuvaneswari Thangavel
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  3. Nor Azlina Ab Aziz
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  4. Nor Hidayati Abdul Aziz
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Corresponding author

Correspondence to Bhuvaneswari Thangavel .

Editor information

Editors and Affiliations

  1. Department of Biomedical Engineering, Faculty of Engineering, Centre for Innovation in Medical Engineering, University of Malaya, Kuala Lumpur, Malaysia

    Fatimah Ibrahim

  2. Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia

    Juliana Usman

  3. Department of Biomedical Engineering, Faculty of Engineering, Centre for Innovation in Medical Engineering, University of Malaya, Kuala Lumpur, Malaysia

    Mohd Yazed Ahmad

  4. Department of Rehabilitation Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia

    Norhamizan Hamzah

  5. Department of Biomedical Engineering, Faculty of Engineering, Centre for Innovation in Medical Engineering, University of Malaya, Kuala Lumpur, Malaysia

    Swe Jyan Teh

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Ahmad Zamri, N., Thangavel, B., Ab Aziz, N.A., Abdul Aziz, N.H. (2018). Review on the Usage of Swarm Intelligence in Gene Expression Data. In: Ibrahim, F., Usman, J., Ahmad, M., Hamzah, N., Teh, S. (eds) 2nd International Conference for Innovation in Biomedical Engineering and Life Sciences. ICIBEL 2017. IFMBE Proceedings, vol 67. Springer, Singapore. https://doi.org/10.1007/978-981-10-7554-4_27

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  • DOI: https://doi.org/10.1007/978-981-10-7554-4_27

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7553-7

  • Online ISBN: 978-981-10-7554-4

  • eBook Packages: EngineeringEngineering (R0)

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