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Vol. 2 No. 01 (2021)

Articles

Swarm Intelligence Algorithms in Gene Selection Profile Based on Classification of Microarray Data: A Review

https://doi.org/10.38094/jastt20161

Published 2021-02-07

  • Alan Jahwar
    • ,
  • Nawzat Sadiq Ahmed

Alan Jahwar
Duhok Polytechnic University

Nawzat Sadiq Ahmed
Dept. of Information Technology Management, Technical College of Administration, Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq

Abstract

Microarray data plays a major role in diagnosing and treating cancer. In several microarray data sets, many gene fragments are not associated with the target diseases. A solution to the gene selection problem might become important when analyzing large gene datasets. The key task is to better represent genes through optimum accuracy in classifying the samples. Different gene classification algorithms have been provided in past studies; after all, they suffered due to the selection of several genes mostly in high-dimensional microarray data. This paper aims to review classification and feature selection with different microarray datasets focused on swarm intelligence algorithms. We explain microarray data and its types in this paper briefly. Moreover, our paper presents an introduction to most common swarm intelligence algorithms. A review on swarm intelligence algorithms in gene selection profile based on classification of Microarray Data is presented in this paper.

Keywords

Microarray data, Gene Selection, Classification, Feature Selection, Swarm Intelligence Optimization

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References

  1. Zebari, D. A., Zeebaree, D. Q., Abdulazeez, A. M., Haron, H., & Hamed, H. N. A. (2020). Improved Threshold Based and Trainable Fully Automated Segmentation for Breast Cancer Boundary and Pectoral Muscle in Mammogram Images. IEEE Access, 8, 203097-203116.
  2. Hameed, I., Masoodi, S. R., Malik, P. A., Mir, S. A., Ghazanfar, K., & Ganai, B. A. (2018). Genetic variations in key inflammatory cytokines exacerbates the risk of diabetic nephropathy by influencing the gene expression. Gene, 661, 51-59.
  3. Tang, J., Alelyani, S., & Liu, H. (2014). Feature selection for classification: A review. Data classification: Algorithms and applications, 37.
  4. Petre, I. O., & Buiu, C. (2017, June). A colon cancer microarray analysis technique. In 2017 E-Health and Bioengineering Conference (EHB) (pp. 265-268). IEEE.
  5. Wu, P., & Wang, D. (2018). Classification of a DNA microarray for diagnosing cancer using a complex network-based method. IEEE/ACM transactions on computational biology and bioinformatics, 16(3), 801-808.
  6. Deng, S. P., Zhu, L., & Huang, D. S. (2015, December). Mining the bladder cancer-associated genes by an integrated strategy for the construction and analysis of differential co-expression networks. In BMC genomics (Vol. 16, No. 3, pp. 1-10). BioMed Central.
  7. Deng, S. P., & Huang, D. S. (2014). SFAPS: An R package for structure/function analysis of protein sequences based on informational spectrum method. Methods, 69(3), 207-212.
  8. Zeebaree, D. Q., Haron, H., & Abdulazeez, A. M. (2018, October). Gene selection and classification of microarray data using a convolutional neural network. In 2018 International Conference on Advanced Science and Engineering (ICOASE) (pp. 145-150). IEEE.
  9. Ahmed, O., & Brifcani, A. (2019, April). Gene Expression Classification Based on Deep Learning. In 2019 4th Scientific International Conference Najaf (SICN) (pp. 145-149). IEEE
  10. Zebari, D. A., Haron, H., Zeebaree, S. R., & Zeebaree, D. Q. (2019, April). Enhance the Mammogram Images for Both Segmentation and Feature Extraction Using Wavelet Transform. In 2019 International Conference on Advanced Science and Engineering (ICOASE) (pp. 100-105). IEEE.
  11. Leung, Y. Y., Chang, C. Q., Hung, Y. S., & Fung, P. C. W. (2006, August). Gene selection for brain cancer classification. In 2006 International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 5846-5849). IEEE.
  12. Zeebaree, D. Q., Haron, H., Abdulazeez, A. M., & Zebari, D. A. (2019, April). Machine learning and Region Growing for Breast Cancer Segmentation. In 2019 International Conference on Advanced Science and Engineering (ICOASE) (pp. 88-93). IEEE.
  13. Tell-Marti, G., Sarda, S. P., & Puig-Butille, J. A. (2019). Gene Expression Microarray: Technical Fundamentals and Data Analysis.
  14. Zeebaree, D. Q., Haron, H., Abdulazeez, A. M., & Zebari, D. A. (2019, April). Trainable Model Based on New Uniform LBP Feature to Identify the Risk of the Breast Cancer. In 2019 International Conference on Advanced Science and Engineering (ICOASE) (pp. 106-111). IEEE.
  15. Zhang, R., Gao, H., Liu, Y., Lu, Y., & Cui, Y. (2018, November). Biclustering of Gene Expression Data Based on Binary Artificial Fish Swarm Algorithm. In 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS) (pp. 247-251). IEEE.
  16. Bolon-Canedo, V., Sanchez-Marono, N., Alonso-Betanzos, A., Benitez, J. M., & Herrera, F. (2014). A review of microarray datasets and applied feature selection methods. Information Sciences, 282, 111-135.
  17. Tomasev, N., Radovanovic, M., Mladenic, D., & Ivanovic, M. (2013). The role of hubness in clustering high-dimensional data. IEEE Transactions on Knowledge & Data Engineering, (1), 1.
  18. Zebari, N. A., Zebari, D. A., Zeebaree, D. Q., & Saeed, J. N. Significant features for steganography techniques using deoxyribonucleic acid: a review.
  19. Ahmad, F. K., Norwawi, N. M., Deris, S., & Othman, N. H. (2008, August). A review of feature selection techniques via gene expression profiles. In 2008 International Symposium on Information Technology (Vol. 2, pp. 1-7). IEEE.
  20. Mischel, P. S., Cloughesy, T. F., & Nelson, S. F. (2004). DNA-microarray analysis of brain cancer: molecular classification for therapy. Nature Reviews Neuroscience, 5(10), 782.
  21. Zeebaree, D. Q., Haron, H., & Abdulazeez, A. M. (2018, October). Gene selection and classification of microarray data using convolutional neural network. In 2018 International Conference on Advanced Science and Engineering (ICOASE) (pp. 145-150). IEEE.
  22. Rich, J. N., Hans, C., Jones, B., Iversen, E. S., McLendon, R. E., Rasheed, B. A., ... & West, M. (2005). Gene expression profiling and genetic markers in glioblastoma survival. Cancer research, 65(10), 4051-4058.
  23. Jahwar, A. F., & Duhok, A. M. A. (2020). META-HEURISTIC ALGORITHMS FOR K-MEANS CLUSTERING: A REVIEW. PalArch's Journal of Archaeology of Egypt/Egyptology, 17(7), 12002-12020.
  24. D. Q. Zeebaree, A. M. Abdulazeez, D. A. Zebari, H. Haron and H. Nuzly, "Multi-level fusion in ultrasound for cancer detection based on uniform lbp features," Computers, Materials & Continua, vol. 66, no.3, pp. 3363–3382, 2021.
  25. Zebari, D. A., Haron, H., Zeebaree, D. Q., & Zain, A. M. (2019, August). A Simultaneous Approach for Compression and Encryption Techniques Using Deoxyribonucleic Acid. In 2019 13th International Conference on Software, Knowledge, Information Management and Applications (SKIMA) (pp. 1-6). IEEE.
  26. Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of machine learning research, 3(Mar), 1157-1182.
  27. Othman, G., & Zeebaree, D. Q. (2020). The Applications of Discrete Wavelet Transform in Image Processing: A Review. Journal of Soft Computing and Data Mining, 1(2), 31-43.
  28. Dadaneh, B. Z., Markid, H. Y., & Zakerolhosseini, A. (2016). Unsupervised probabilistic feature selection using ant colony optimization. Expert Systems with Applications, 53, 27-42.
  29. Abdel-Basset, M., Fakhry, A. E., El-Henawy, I., Qiu, T., & Sangaiah, A. K. (2017). Feature and intensity based medical image registration using particle swarm optimization. Journal of medical systems, 41(12), 197.
  30. Su, H. (2019). Siting and sizing of distributed generators based on improved simulated annealing particle swarm optimization. Environmental Science and Pollution Research, 26(18), 17927-17938.
  31. Mandal, S. (2017). A modified particle swarm optimization algorithm based on self-adaptive acceleration constants. International Journal of Modern Education and Computer Science, 9(8), 49.
  32. Zebari, D. A., Zeebaree, D. Q., Saeed, J. N., Zebari, N. A., & Adel, A. Z. (2020). Image steganography based on swarm intelligence algorithms: A survey. people, 7(8), 9.
  33. Petre, I. O., & Buiu, C. (2017, June). A colon cancer microarray analysis technique. In 2017 E-Health and Bioengineering Conference (EHB) (pp. 265-268). IEEE.
  34. Zhang, X., He, T., Ouyang, L., Xu, X., & Chen, S. (2018, December). A Survey of Gene Selection and Classification Techniques Based on Cancer Microarray Data Analysis. In 2018 IEEE 4th International Conference on Computer and Communications (ICCC) (pp. 1809-1813). IEEE.
  35. Brazma, Alvis, and Jaak Vilo. "Gene expression data analysis." FEBS Letters 480.1 (2000): 17-24.
  36. Sherlock, Gavin. "Analysis of large-scale gene expression data." Current opinion in immunology 12.2 (2000): 201-205.
  37. Othman, M. S., Kumaran, S. R., & Yusuf, L. M. (2020). Gene Selection Using Hybrid Multi-Objective Cuckoo Search Algorithm with Evolutionary Operators for Cancer Microarray Data. IEEE Access, 8, 186348-186361.
  38. Lai, C. M., & Huang, H. P. (2020). A gene selection algorithm using simplified swarm optimization with multi-filter ensemble technique. Applied Soft Computing, 106994.
  39. Pino Angulo, A. (2018). Gene selection for microarray cancer data classification by a novel rule-based algorithm. Information, 9(1), 6.
  40. Zhai, Y., Yin, S., & Zhang, D. (2016). Association between antipsychotic drugs and mortality in older persons with Alzheimer’s disease: A systematic review and meta-analysis. Journal of Alzheimer's Disease, 52(2), 631-639.
  41. Lin, S.W., Ying, K.C., Chen, S.C., Lee, Z.J.: Particle swarm optimization for parameter determination and feature selection of support vector machines. Expert Syst.Appl. 35(4), 1817–1824 (2008).
  42. Perez-Grijalba, V., Pesini, P., Monleon, I., Boada, M., Tarraga, L., Ruiz-Laza, A., & Sarasa, M. (2013). Several direct and calculated biomarkers from the amyloid-B pool in blood are associated with an increased likelihood of suffering from mild cognitive impairment. Journal of Alzheimer's Disease, 36(1), 211-219.
  43. Chan, S. C., Wu, H. C., Lin, J. Q., & Zhang, Z. G. (2018, November). A Partial least squares-based regression approach for analysis of frontotemporal dementia gene markers in human brain gene microarray data. In 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP) (pp. 1-5). IEEE.
  44. Hegde, R. B., Prasad, K., Hebbar, H., Singh, B. M. K., & Sandhya, I. (2019). Automated Decision Support System for Detection of Leukemia from Peripheral Blood Smear Images. Journal of Digital Imaging, 1-14.
  45. Harun, N. H., Bakar, J. A., Abd Wahab, Z., Osman, M. K., & Harun, H. (2020, April). Color Image Enhancement of Acute Leukemia Cells in Blood Microscopic Image for Leukemia Detection Sample. In 2020 IEEE 10th Symposium on Computer Applications & Industrial Electronics (SCAIE) (pp. 24-29). IEEE.
  46. Asl, A. A. S., & Zarandi, M. H. F. (2017, October). A type-2 fuzzy expert system for diagnosis of leukemia. In North American Fuzzy Information Processing Society Annual Conference (pp. 52-60). Springer, Cham.
  47. Hasri, N.N.M., et al., Improved Support Vector Machine Using Multiple SVM-RFE for Cancer Classification. International Journal on Advanced Science, Engineering and Information Technology, 2017. 7(4-2): p. 1589-1594.
  48. Banga, J. R. (2008). Optimization in computational systems biology. BMC systems biology, 2(1), 1-7.
  49. Krause, J., Ruxton, G. D., & Krause, S. (2010). Swarm intelligence in animals and humans. Trends in ecology & evolution, 25(1), 28-34.
  50. Murray, J. D. (2007). Mathematical biology: I. An introduction (Vol. 17). Springer Science & Business Media.
  51. Bonabeau, E., Dorigo, M., Marco, D. D. R. D. F., Theraulaz, G., & Theraulaz, G. (1999). Swarm intelligence: from natural to artificial systems (No. 1). Oxford university press.
  52. Yan-fei Zhu and Xiong-min Tang, "Overview of swarm intelligence," 2010 International Conference on Computer Application and System Modeling (ICCASM 2010), Taiyuan, 2010, pp. V9-400-V9-403, doi: 10.1109/ICCASM.2010.5623005.
  53. Prabha, K. A., & Visalakshi, N. K. (2014, March). Improved particle swarm optimization-based k-means clustering. In 2014 International Conference on Intelligent Computing Applications (pp. 59-63). IEEE.
  54. Coelho, R.A., d. R. N. Guimares, F., Esmin, A.A.A., 2014. Applying swarm ensemble clustering technique for fault prediction using software metrics. In: 2014 13th International Conference on Machine Learning and Applications. pp. 356–361.
  55. Hossain, M.S., Moniruzzaman, M., Muhammad, G., Ghoneim, A., Alamri, A., 2016. Big data-driven service composition using parallel clustered particle swarm optimization in mobile environment. IEEE Trans. Serv. Comput. 9 (5), 806–817.
  56. Esmin, A.A.A., Coelho, R.A., 2013. Consensus clustering based on particle swarm optimization algorithm. In: Proceedings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics. IEEE Computer Society, pp. 2280–2285.
  57. Nanda, S.J., Panda, G., 2014. A survey on nature inspired metaheuristic algorithms for partitional clustering. Swarm Evol. Comput. 16, 1–18.
  58. Figueiredo, E., Macedo, M., Siqueira, H. V., Santana Jr, C. J., Gokhale, A., & Bastos-Filho, C. J. (2019). Swarm intelligence for clustering—A systematic review with new perspectives on data mining. Engineering Applications of Artificial Intelligence, 82, 313-329.
  59. Han, S. Y., Zhang, C. H., & Tang, G. Y. (2017). Approximation optimal vibration for networked nonlinear vehicle active suspension with actuator time delay. Asian Journal of Control, 19(3), 983-995.
  60. Hu, Z., Su, Q., & Xia, X. (2016). Multiobjective image color quantization algorithm based on self-adaptive hybrid differential evolution. Computational intelligence and neuroscience, 2016.
  61. Ng, W. S., Neoh, S. C., Htike, K. K., & Wang, S. L. (2017). Particle Swarm Feature Selection for Microarray Leukemia Classification. Progress in Energy and Environment, 2, 1-8.
  62. Premalatha, K., & Natarajan, A. M. (2010). Hybrid PSO and GA models for document clustering. Int. J. Advance. Soft Comput. Appl, 2(3), 302-320.
  63. Rajasekhar, A., Lynn, N., Das, S., Suganthan, P., 2017. Computing with the collective intelligence of honey bees - a survey. Swarm Evol. Comput. 32, 25–48.
  64. Karaboga, D., & Akay, B. (2009). A survey: algorithms simulating bee swarm intelligence. Artificial intelligence review, 31(1-4), 61.
  65. Ghanbarzadeh, T. (2007). Multi-objective optimization using the bees algorithm. In Proceedings of International Virtual Conference on Intelligent Production Machines and Systems (IPROMS), Cardiff, UK.
  66. Karaboga, D., & Basturk, B. (2008). On the performance of artificial bee colony (ABC) algorithm. Applied soft computing, 8(1), 687-697.
  67. Liyan, D., Sainan, Z., Geng, T., Yongli, L., & Guanyan, C. (2013, November). Ant colony clustering algorithm based on swarm intelligence. In 2013 6th International Conference on Intelligent Networks and Intelligent Systems (ICINIS) (pp. 123-126). IEEE.
  68. Shreem, S. S., Abdullah, S., & Nazri, M. Z. A. (2016). Hybrid feature selection algorithm using symmetrical uncertainty and a harmony search algorithm. International Journal of Systems Science, 47(6), 1312-1329.
  69. Chen, K. H., Wang, K. J., Wang, K. M., & Angelia, M. A. (2014). Applying particle swarm optimization-based decision tree classifier for cancer classification on gene expression data. Applied Soft Computing, 24, 773-780.
  70. Nagpal, A., & Singh, V. (2018). A feature selection algorithm based on qualitative mutual information for cancer microarray data. Procedia computer science, 132, 244-252.
  71. Sudha, M. N., & Selvarajan, S. (2016). Feature selection based on enhanced cuckoo search for breast cancer classification in mammogram image. Circuits and Systems, 7(4), 327-338.
  72. Sakri, S. B., Rashid, N. B. A., & Zain, Z. M. (2018). Particle swarm optimization feature selection for breast cancer recurrence prediction. IEEE Access, 6, 29637-29647.
  73. Hameed, S. S., Petinrin, O. O., Osman, A., & Hashi, F. S. (2018). Filter-wrapper combination and embedded feature selection for gene expression data. Int. J. Advance Soft Compu. Appl, 10(1).
  74. Ng, W. S., Neoh, S. C., Htike, K. K., & Wang, S. L. (2017). Particle Swarm Feature Selection for Microarray Leukemia Classification. Progress in Energy and Environment, 2, 1-8.
  75. Ozger, Z. B., Bolat, B., & Diri, B. (2019). A Probabilistic Multi-Objective Artificial Bee Colony Algorithm for Gene Selection. J. UCS, 25(4), 418-443.
  76. Jagadeesh, S., & Sugumar, R. (2017). A Comparative study on Artificial Bee Colony with modified ABC algorithm. European Journal of Applied Sciences, 9(5), 243-248.
  77. Kaveh, A., & Bakhshpoori, T. (2016). An efficient multi-objective cuckoo search algorithm for design optimization. Advances in Computational Design, 1(1), 87-103.
  78. Zhang, Y., Cheng, S., Shi, Y., Gong, D. W., & Zhao, X. (2019). Cost-sensitive feature selection using two-archive multi-objective artificial bee colony algorithm. Expert Systems with Applications, 137, 46-58.
  79. Sasikala, S., alias Balamurugan, S. A., & Geetha, S. (2015). A novel feature selection technique for improved survivability diagnosis of breast cancer. Procedia Computer Science, 50, 16-23.
  80. Yahya, A. A., Osman, A., & Abd Rahman Ramli, A. B. (2011). Feature selection for high dimensional data: an evolutionary filter approach.
  81. M. Fatimaezzahra, S. Mohamed, and E. Abdelaziz, ‘‘A combined cuckoo search algorithm and genetic algorithm for parameter optimization in computer vision,’’ Int. J. Appl. Eng. Res., vol. 51, pp. 12940–12954, Dec. 2017.
  82. Othman, M. S., Kumaran, S. R., & Yusuf, L. M. (2020). Gene Selection Using Hybrid Multi-Objective Cuckoo Search Algorithm with Evolutionary Operators for Cancer Microarray Data. IEEE Access, 8, 186348-186361.
  83. Qaraad, M., Amjad, S., El-Kafrawy, P., Fathi, H., & Manhrawy, I. I. (2020, June). Parameters Optimization of Elastic NET for High Dimensional Data using PSO Algorithm. In 2020 International Conference on Intelligent Systems and Computer Vision (ISCV) (pp. 1-7). IEEE.
  84. Prabhakar, S. K., & Lee, S. W. (2020). Transformation based tri-level feature selection approach using wavelets and swarm computing for prostate cancer classification. IEEE Access, 8, 127462-127476.
  85. Saqib, P., Qamar, U., Khan, R. A., & Aslam, A. (2020, February). MF-GARF: Hybridizing Multiple Filters and GA Wrapper for Feature Selection of Microarray Cancer Datasets. In 2020 22nd International Conference on Advanced Communication Technology (ICACT) (pp. 517-524). IEEE.
  86. Tubishat, M., Ja'afar, S., Alswaitti, M., Mirjalili, S., Idris, N., Ismail, M. A., & Omar, M. S. (2020). Dynamic salp swarm algorithm for feature selection. Expert Systems with Applications, 164, 113873.
  87. Cahyaningrum, K., & Astuti, W. (2020, August). Microarray Gene Expression Classification for Cancer Detection using Artificial Neural Networks and Genetic Algorithm Hybrid Intelligence. In 2020 International Conference on Data Science and Its Applications (ICoDSA) (pp. 1-7). IEEE.
  88. Babu, T., Gupta, D., Singh, T., & Hameed, S. (2018, December). Colon Cancer Prediction on Different Magnified Colon Biopsy Images. In 2018 Tenth International Conference on Advanced Computing (ICoAC) (pp. 277-280). IEEE.
  89. Liu, M., Xu, L., Yi, J., & Huang, J. (2018, February). A feature gene selection method based on ReliefF and PSO. In 2018 10th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA) (pp. 298-301). IEEE.
  90. Wu, P., & Wang, D. (2018). Classification of a DNA microarray for diagnosing cancer using a complex network-based method. IEEE/ACM transactions on computational biology and bioinformatics, 16(3), 801-808.
  91. Deng, X., & Xu, Y. (2019, November). Cancer Classification Using Microarray Data By DPCAForest. In 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) (pp. 1081-1087). IEEE.

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