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Intelligent Computational Models for Cancer Diagnosis: A Comprehensive Review

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Integrating Meta-Heuristics and Machine Learning for Real-World Optimization Problems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1038))

Abstract

Computational modeling can be defined as the use of computers for studying and simulating complex systems, such as the human brain, organisms, and Earth’s global climate, using mathematics, physics, and computer science. Cancer is a complicated and heterogeneous illness that involves a large number of cells, reactions, and events that take place throughout time. Computational modelling, in combination with experimental studies and clinical testing, can help researchers better understand cancer and develop better treatment techniques. Computational modeling supplies tools for tackling the complexity of cancer and providing a detailed mechanistic insight that tells us which treatment is appropriate for a patient, whether a cancer medicine will stop a tumour from growing and whether a cancer drug will have an effect on healthy tissues in the human body. Many previous studies of Computational modeling are used for cancer diagnosis that is comparatively efficient and less weak. Gene expression microarray technology, which is generally used for cancer diagnosis, prediction, or prognosis, suffers from the curse of a dimensional problem. This problem can be solved using gene selection, which is implemented to the microarray for choosing the most useful features from the original features. The selecting of the optimal number of informative genes is considered an NP-hard problem. The meta-heuristic algorithms (MAs) which are robust at choosing the best solutions for complicated optimization problems for solving NP-hard problems, are also beneficial for solving big problems in sensible computational time. Meta-heuristic algorithms have become popular and powerful in computational modeling and many applications. The core of meta-heuristics Algorithms (MAs) is convenient for choosing the most informative and relative features as preprocessing step to Machine Learning (ML) step for achieving the highest classification accuracy for cancer diagnosis. This chapter introduces a comparative study among the most common MAs algorithms combined with ML computational modeling techniques for cancer diagnosis.

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References

  1. Z. Ji, K. Yan, W. Li, H. Hu, X. Zhu, Mathematical and computational modeling in complex biological systems. BioMed. Res. Int. 2017 (2017)

    Google Scholar 

  2. S. Namasudra, Data access control in the cloud computing environment for bioinformatics. Int. J. Appl. Res. Bioinform. (IJARB) 11(1), 40–50 (2020)

    Article  Google Scholar 

  3. V. Bolón-Canedo, N. Sánchez-Marono, A. Alonso-Betanzos, J.M. Benítez, F. Herrera, A review of microarray datasets and applied feature selection methods. Inf. Sci. 282, 111–135 (2014)

    Article  Google Scholar 

  4. W. Dubitzky, M. Granzow, C.S. Downes, D. Berrar, Introduction to microarray data analysis, in A Practical Approach to Microarray Data Analysis (Springer, 2003), pp. 1–46

    Google Scholar 

  5. A. Benso, S. Di Carlo, G. Politano, A. Savino, GPU acceleration for statistical gene classification, in 2010 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR), vol. 2 (IEEE, 2010), pp. 1–6

    Google Scholar 

  6. S.-B. Guo, M.R. Lyu, T.-M. Lok, Gene selection based on mutual information for the classification of multi-class cancer, in International Conference on Intelligent Computing (Springer, 2006), pp. 454–463

    Google Scholar 

  7. P.M. Narendra, K. Fukunaga, A branch and bound algorithm for feature subset selection. IEEE Trans. Comput. 9, 917–922 (1977)

    Article  MATH  Google Scholar 

  8. E.H. Houssein, M. Younan, A.E. Hassanien, Nature-inspired algorithms: a comprehensive review, in Hybrid Computational Intelligence: Research and Applications (2019), p. 1

    Google Scholar 

  9. E.H. Houssein, A.A. Ewees, M.A. ElAziz, Improving twin support vector machine based on hybrid swarm optimizer for heartbeat classification. Pattern Recognit. Image Anal. 28(2), 243–253 (2018)

    Article  Google Scholar 

  10. M.H. Sulaiman, Z. Mustaffa, M.M. Saari, H. Daniyal, Barnacles mating optimizer: a new bio-inspired algorithm for solving engineering optimization problems. Eng. Appl. Artif. Intell. 87, 103330 (2020)

    Google Scholar 

  11. W. Zhao, Z. Zhang, L. Wang, Manta ray foraging optimization: an effective bio-inspired optimizer for engineering applications. Eng. Appl. Artif. Intell.87, 103300 (2020)

    Google Scholar 

  12. J. Kennedy, R. Eberhart, Particle swarm optimization, in Proceedings of ICNN’95-International Conference on Neural Networks, vol. 4 (IEEE, 1995), pp. 1942–1948

    Google Scholar 

  13. D. Whitley, A genetic algorithm tutorial. Stat. Comput. 4(2), 65–85 (1994)

    Article  Google Scholar 

  14. S. Kaur, L.K. Awasthi, A. Sangal, G. Dhiman, Tunicate swarm algorithm: a new bio-inspired based metaheuristic paradigm for global optimization. Eng. Appl. Artif. Intell. 90, 103541 (2020)

    Google Scholar 

  15. D. Karaboga, An idea based on honey bee swarm for numerical optimization, Technical report-tr06, Erciyes University, Engineering Faculty, Computer ... (Technical Report, 2005)

    Google Scholar 

  16. I. Guyon, J. Weston, S. Barnhill, V. Vapnik, Gene selection for cancer classification using support vector machines. Mach. Learn. 46(1–3), 389–422 (2002)

    Article  MATH  Google Scholar 

  17. H. Peng, F. Long, C. Ding, Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1226–1238 (2005)

    Article  Google Scholar 

  18. H. Enderling, K.A. Rejniak, Simulating cancer: computational models in oncology. Front. Oncol. 3, 233 (2013)

    Article  Google Scholar 

  19. P. Macklin, H.B. Frieboes, J.L. Sparks, A. Ghaffarizadeh, S.H. Friedman, E.F. Juarez, E. Jonckheere, S.M. Mumenthaler, Progress towards computational 3-d multicellular systems biology, in Systems Biology of Tumor Microenvironment (Springer, 2016), pp. 225–246

    Google Scholar 

  20. D. Hanahan, R.A. Weinberg, Hallmarks of cancer: the next generation. Cell 144(5), 646–674 (2011)

    Google Scholar 

  21. P. Macklin, When seeing isn’t believing: how math can guide our interpretation of measurements and experiments. Cell Syst. 5(2), 92–94 (2017)

    Article  Google Scholar 

  22. S. Guo, D. Guo, L. Chen, Q. Jiang, A centroid-based gene selection method for microarray data classification. J. Theor. Biol. 400, 32–41 (2016)

    Article  MathSciNet  MATH  Google Scholar 

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

    Google Scholar 

  24. A. Jain, D. Zongker, Feature selection: evaluation, application, and small sample performance. IEEE Trans. Pattern Anal. Mach. Intell. 19(2), 153–158 (1997)

    Article  Google Scholar 

  25. K.-H. Chen, K.-J. Wang, M.-L. Tsai, K.-M. Wang, A.M. Adrian, W.-C. Cheng, T.-S. Yang, N.-C. Teng, K.-P. Tan, K.-S. Chang, Gene selection for cancer identification: a decision tree model empowered by particle swarm optimization algorithm. BMC Bioinform. 15(1), 49 (2014)

    Article  Google Scholar 

  26. I. Guyon, S. Gunn, M. Nikravesh, L.A. Zadeh, Feature Extraction: Foundations and Applications, vol. 207 (Springer, 2008)

    Google Scholar 

  27. E.P. Xing, M.I. Jordan, R.M. Karp et al., Feature selection for high-dimensional genomic microarray data, in ICML, vol. 1 (Citeseer, 2001), pp. 601–608

    Google Scholar 

  28. R. Simon, Supervised analysis when the number of candidate features (p) greatly exceeds the number of cases (n). ACM SIGKDD Explor. Newsl. 5(2), 31–36 (2003)

    Article  Google Scholar 

  29. X. Wang, O. Gotoh, Microarray-based cancer prediction using soft computing approach. Cancer Inform. 7, CIN–S2655 (2009)

    Google Scholar 

  30. J.L. Pettifor, Book review of encyclopedia of applied ethics. Can. J. Couns. Psychother. 46(4) (2012)

    Google Scholar 

  31. A.D. Baxevanis, G.D. Bader, D.S. Wishart, Bioinformatics (Wiley, 2020)

    Google Scholar 

  32. M. Schena, D. Shalon, R.W. Davis, P.O. Brown, Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270(5235), 467–470 (1995)

    Article  Google Scholar 

  33. A. Maseleno, N. Sabani, M. Huda, R. Ahmad, K.A. Jasmi, B. Basiron, Demystifying learning analytics in personalised learning. Int. J. Eng. Technol. 7(3), 1124–1129 (2018)

    Article  Google Scholar 

  34. R. Rodríguez-Peéez, M. Vogt, J. Bajorath, Support vector machine classification and regression prioritize different structural features for binary compound activity and potency value prediction. ACS Omega 2(10), 6371–6379 (2017)

    Article  Google Scholar 

  35. K. Hussain, M.N.M. Salleh, S. Cheng, Y. Shi, Metaheuristic research: a comprehensive survey. Artif. Intell. Rev. 52(4), 2191–2233 (2019)

    Article  Google Scholar 

  36. I. Fister Jr, X.-S. Yang, I. Fister, J. Brest, D. Fister, A brief review of nature-inspired algorithms for optimization. arXiv:1307.4186 (2013)

  37. E.H. Houssein, A.G. Gad, K. Hussain, P.N. Suganthan, Major advances in particle swarm optimization: theory, analysis, and application. Swarm Evol. Comput. 63, 100868 (2021)

    Google Scholar 

  38. F.A. Hashim, E.H. Houssein, M.S. Mabrouk, W. Al-Atabany, S. Mirjalili, Henry gas solubility optimization: a novel physics-based algorithm. Futur. Gener. Comput. Syst. 101, 646–667 (2019)

    Article  Google Scholar 

  39. E.H. Houssein, M.R. Saad, F.A. Hashim, H. Shaban, M. Hassaballah, Lévy flight distribution: a new metaheuristic algorithm for solving engineering optimization problems. Eng. Appl. Artif. Intell. 94, 103731 (2020)

    Google Scholar 

  40. F.A. Hashim, K. Hussain, E.H. Houssein, M.S. Mabrouk, W. Al-Atabany, Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems. Appl. Intell. 51(3), 1531–1551 (2021)

    Article  MATH  Google Scholar 

  41. F.A. Hashim, E.H. Houssein, K. Hussain, M.S. Mabrouk, W. Al-Atabany, Honey badger algorithm: new metaheuristic algorithm for solving optimization problems. Math. Comput. Simul. 192, 84–110 (2021)

    Google Scholar 

  42. E.H. Houssein, D.S. Abdelminaam, H.N. Hassan, M.M. Al-Sayed, E. Nabil, A hybrid barnacles mating optimizer algorithm with support vector machines for gene selection of microarray cancer classification. IEEE Access 9, 64,895–64,905 (2021)

    Google Scholar 

  43. E.H. Houssein, I.E. Ibrahim, N. Neggaz, M. Hassaballah, Y.M. Wazery, An efficient ECG arrhythmia classification method based on manta ray foraging optimization. Expert Syst. Appl. 181, 115131 (2021)

    Google Scholar 

  44. E.H. Houssein, D.S. AbdElminaam, I.E. Ibrahim, M. Hassaballah, Y.M. Wazery, A hybrid heartbeats classification approach based on marine predators algorithm and convolution neural networks. IEEE Access (2021)

    Google Scholar 

  45. Y.M. Wazery, E. Saber, E.H. Houssein, A.A. Ali, E. Amer, An efficient slime mould algorithm combined with k-nearest neighbor for medical classification tasks. IEEE Access (2021)

    Google Scholar 

  46. A. Hamad, E.H. Houssein, A.E. Hassanien, A.A. Fahmy, Hybrid grasshopper optimization algorithm and support vector machines for automatic seizure detection in EEG signals, in International Conference on Advanced Machine Learning Technologies and Applications (Springer, 2018), pp. 82–91

    Google Scholar 

  47. E.H. Houssein, M. Kilany, A.E. Hassanien, ECG signals classification: a review. Int. J. Intell. Eng. Inform. 5(4), 376–396 (2017)

    Google Scholar 

  48. E.H. Houssein, M. Dirar, K. Hussain, W.M. Mohamed, Assess deep learning models for Egyptian exchange prediction using nonlinear artificial neural networks. Neural Comput. Appl. 33(11), 5965–5987 (2021)

    Article  Google Scholar 

  49. E.H. Houssein, M.M. Emam, A.A. Ali, P.N. Suganthan, Deep and machine learning techniques for medical imaging-based breast cancer: a comprehensive review. Expert Syst. Appl. 114161 (2020)

    Google Scholar 

  50. N. Neggaz, E.H. Houssein, K. Hussain, An efficient henry gas solubility optimization for feature selection. Expert Syst. Appl. 152, 113364 (2020)

    Google Scholar 

  51. K. Hussain, N. Neggaz, W. Zhu, E.H. Houssein, An efficient hybrid sine-cosine Harris hawks optimization for low and high-dimensional feature selection. Expert Syst. Appl. 176, 114778 (2021)

    Google Scholar 

  52. D.S. Abd Elminaam, A. Nabil, S.A. Ibraheem, E.H. Houssein, An efficient marine predators algorithm for feature selection. IEEE Access 9, 60,136–60,153 (2021)

    Google Scholar 

  53. E.H. Houssein, M.A. Mahdy, A. Fathy, H. Rezk, A modified marine predator algorithm based on opposition based learning for tracking the global MPP of shaded PV system. Expert Syst. Appl. 183, 115253 (2021)

    Google Scholar 

  54. E.H. Houssein, B.E.-D. Helmy, H. Rezk, A.M. Nassef, An enhanced Archimedes optimization algorithm based on local escaping operator and orthogonal learning for PEM fuel cell parameter identification. Eng. Appl. Artif. Intell. 103, 104309 (2021)

    Google Scholar 

  55. M.H. Hassan, E.H. Houssein, M.A. Mahdy, S. Kamel, An improved manta ray foraging optimizer for cost-effective emission dispatch problems. Eng. Appl. Artif. Intell. 100, 104155 (2021)

    Google Scholar 

  56. A. Korashy, S. Kamel, E.H. Houssein, F. Jurado, F.A. Hashim, Development and application of evaporation rate water cycle algorithm for optimal coordination of directional overcurrent relays. Expert Syst. Appl. 185, 115538 (2021)

    Google Scholar 

  57. S. Deb, E.H. Houssein, M. Said, D.S. Abd Elminaam, Performance of turbulent flow of water optimization on economic load dispatch problem. IEEE Access (2021)

    Google Scholar 

  58. S. Deb, D.S. Abdelminaam, M. Said, E.H. Houssein, Recent methodology-based gradient-based optimizer for economic load dispatch problem. IEEE Access 9, 44,322–44,338 (2021)

    Google Scholar 

  59. E.H. Houssein, F.A. Hashim, S. Ferahtia, H. Rezk, An efficient modified artificial electric field algorithm for solving optimization problems and parameter estimation of fuel cell. Int. J. Energy Res. (2021)

    Google Scholar 

  60. E.H. Houssein, G.N. Zaki, A.A.Z. Diab, E.M. Younis, An efficient manta ray foraging optimization algorithm for parameter extraction of three-diode photovoltaic model. Comput. Electr. Eng. 94, 107304 (2021)

    Google Scholar 

  61. E.H. Houssein, Machine learning and meta-heuristic algorithms for renewable energy: a systematic review, in Advanced Control and Optimization Paradigms for Wind Energy Systems (2019), pp. 165–187

    Google Scholar 

  62. A.A. Ismaeel, E.H. Houssein, D. Oliva, M. Said, Gradient-based optimizer for parameter extraction in photovoltaic models. IEEE Access 9, 13,403–13,416 (2021)

    Google Scholar 

  63. E.H. Houssein, M.A. Mahdy, M.J. Blondin, D. Shebl, W.M. Mohamed, Hybrid slime mould algorithm with adaptive guided differential evolution algorithm for combinatorial and global optimization problems. Expert Syst. Appl. 174, 114689 (2021)

    Google Scholar 

  64. E.H. Houssein, M.A. Mahdy, M.G. Eldin, D. Shebl, W.M. Mohamed, M. Abdel-Aty, Optimizing quantum cloning circuit parameters based on adaptive guided differential evolution algorithm. J. Adv. Res. 29, 147–157 (2021)

    Article  Google Scholar 

  65. E.H. Houssein, B.E.-D. Helmy, D. Oliva, A.A. Elngar, H. Shaban, A novel black widow optimization algorithm for multilevel thresholding image segmentation. Expert Syst. Appl. 167, 114159 (2021)

    Google Scholar 

  66. E.H. Houssein, M.M. Emam, A.A. Ali, An efficient multilevel thresholding segmentation method for thermography breast cancer imaging based on improved chimp optimization algorithm. Expert Syst. Appl. 115651 (2021)

    Google Scholar 

  67. E.H. Houssein, K. Hussain, L. Abualigah, M. Abd Elaziz, W. Alomoush, G. Dhiman, Y. Djenouri, E. Cuevas, An improved opposition-based marine predators algorithm for global optimization and multilevel thresholding image segmentation. Knowl. Based Syst. 107348 (2021)

    Google Scholar 

  68. E.H. Houssein, M.M. Emam, A.A. Ali, Improved manta ray foraging optimization for multi-level thresholding using Covid-19 CT images. Neural Comput. Appl. 1–21 (2021)

    Google Scholar 

  69. E.H. Houssein, B.E.-D. Helmy, A.A. Elngar, D.S. Abdelminaam, H. Shaban, An improved tunicate swarm algorithm for global optimization and image segmentation. IEEE Access 9, 56,066–56,092 (2021)

    Google Scholar 

  70. M.M. Ahmed, E.H. Houssein, A.E. Hassanien, A. Taha, E. Hassanien, Maximizing lifetime of large-scale wireless sensor networks using multi-objective whale optimization algorithm. Telecommun. Syst. 72(2), 243–259 (2019)

    Article  Google Scholar 

  71. E.H. Houssein, A.G. Gad, Y.M. Wazery, P.N. Suganthan, Task scheduling in cloud computing based on meta-heuristics: review, taxonomy, open challenges, and future trends. Swarm Evol. Comput. 100841 (2021)

    Google Scholar 

  72. F.A. Hashim, E.H. Houssein, K. Hussain, M.S. Mabrouk, W. Al-Atabany, A modified henry gas solubility optimization for solving motif discovery problem. Neural Comput. Appl. 32(14), 10,759–10,771 (2020)

    Google Scholar 

  73. E.H. Houssein, N. Neggaz, M.E. Hosney, W.M. Mohamed, M. Hassaballah, Enhanced Harris hawks optimization with genetic operators for selection chemical descriptors and compounds activities. Neural Comput. Appl. 1–18 (2021)

    Google Scholar 

  74. E.H. Houssein, M.E. Hosney, D. Oliva, W.M. Mohamed, M. Hassaballah, A novel hybrid Harris hawks optimization and support vector machines for drug design and discovery. Comput. Chem. Eng. 133, 106656 (2020)

    Google Scholar 

  75. E. Zitzler, M. Laumanns, L. Thiele, SPEA2: improving the strength Pareto evolutionary algorithm. TIK-report 103 (2001)

    Google Scholar 

  76. T. Back, Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms (Oxford University Press, 1996)

    Google Scholar 

  77. X.-S. Yang, S. Deb, Y.-X. Zhao, S. Fong, X. He, Swarm intelligence: past, present and future. Soft. Comput. 22(18), 5923–5933 (2018)

    Article  Google Scholar 

  78. S. Mirjalili, A. Lewis, The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)

    Article  Google Scholar 

  79. G.-C. Luh, C.-Y. Lin, Structural topology optimization using ant colony optimization algorithm. Appl. Soft Comput. 9(4), 1343–1353 (2009)

    Article  Google Scholar 

  80. S. Mirjalili, S.M. Mirjalili, A. Lewis, Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)

    Article  Google Scholar 

  81. V. Hayyolalam, A.A.P. Kazem, Black widow optimization algorithm: a novel meta-heuristic approach for solving engineering optimization problems. Eng. Appl. Artif. Intell. 87, 103249 (2020)

    Google Scholar 

  82. A.A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, H. Chen, Harris hawks optimization: algorithm and applications. Futur. Gener. Comput. Syst. 97, 849–872 (2019)

    Article  Google Scholar 

  83. L.M. Zhang, C. Dahlmann, Y. Zhang, Human-inspired algorithms for continuous function optimization, in IEEE International Conference on Intelligent Computing and Intelligent Systems, vol. 1 (IEEE, 2009), pp. 318–321 (2009)

    Google Scholar 

  84. Y. Shi, An optimization algorithm based on brainstorming process, in Emerging Research on Swarm Intelligence and Algorithm Optimization (IGI Global, 2015), pp. 1–35

    Google Scholar 

  85. X.-S. Yang, Flower pollination algorithm for global optimization, in International Conference on Unconventional Computing and Natural Computation (Springer, 2012), pp. 240–249

    Google Scholar 

  86. S. Kirkpatrick, C.D. Gelatt, M.P. Vecchi, Optimization by simulated annealing. Science 220(4598), 671–680 (1983)

    Google Scholar 

  87. B. Javidy, A. Hatamlou, S. Mirjalili, Ions motion algorithm for solving optimization problems. Appl. Soft Comput. 32, 72–79 (2015)

    Article  Google Scholar 

  88. W. Zhao, L. Wang, Z. Zhang, Atom search optimization and its application to solve a hydrogeologic parameter estimation problem. Knowl. Based Syst. 163, 283–304 (2019)

    Article  Google Scholar 

  89. K. Abaci, V. Yamacli, Differential search algorithm for solving multi-objective optimal power flow problem. Int. J. Electr. Power Energy Syst. 79, 1–10 (2016)

    Article  Google Scholar 

  90. P. Civicioglu, Backtracking search optimization algorithm for numerical optimization problems. Appl. Math. Comput. 219(15), 8121–8144 (2013)

    MathSciNet  MATH  Google Scholar 

  91. P. Civicioglu, Artificial cooperative search algorithm for numerical optimization problems. Inf. Sci. 229, 58–76 (2013)

    Article  MATH  Google Scholar 

  92. Y. Saeys, I. Inza, P. Larrañaga, A review of feature selection techniques in bioinformatics. Bioinformatics 23(19), 2507–2517 (2007)

    Google Scholar 

  93. Y. Li, G. Wang, H. Chen, L. Shi, L. Qin, An ant colony optimization based dimension reduction method for high-dimensional datasets. J. Bionic Eng. 10(2), 231–241 (2013)

    Article  Google Scholar 

  94. Y. Leung, Y. Hung, A multiple-filter-multiple-wrapper approach to gene selection and microarray data classification. IEEE/ACM Trans. Comput. Biol. Bioinform. (TCBB) 7(1), 108–117 (2010)

    Article  Google Scholar 

  95. S. Tabakhi, P. Moradi, F. Akhlaghian, An unsupervised feature selection algorithm based on ant colony optimization. Eng. Appl. Artif. Intell. 32, 112–123 (2014)

    Article  Google Scholar 

  96. B. Liao, Y. Jiang, W. Liang, W. Zhu, L. Cai, Z. Cao, Gene selection using locality sensitive Laplacian score. IEEE/ACM Trans. Comput. Biol. Bioinform. (TCBB) 11(6), 1146–1156 (2014)

    Article  Google Scholar 

  97. X. He, D. Cai, P. Niyogi, Laplacian score for feature selection, in Advances in Neural Information Processing Systems (2006), pp. 507–514

    Google Scholar 

  98. R. Cai, Z. Hao, X. Yang, W. Wen, An efficient gene selection algorithm based on mutual information. Neurocomputing 72(4–6), 991–999 (2009)

    Article  Google Scholar 

  99. L.E. Raileanu, K. Stoffel, Theoretical comparison between the Gini index and information gain criteria. Ann. Math. Artif. Intell. 41(1), 77–93 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  100. A. Bertoni, R. Folgieri, G. Valentini, Bio-molecular cancer prediction with random subspace ensembles of support vector machines. Neurocomputing 63, 535–539 (2005)

    Article  Google Scholar 

  101. C. Lai, M.J. Reinders, L. Wessels, Random subspace method for multivariate feature selection. Pattern Recogn. Lett. 27(10), 1067–1076 (2006)

    Article  Google Scholar 

  102. X. Li, H. Zhao, Weighted random subspace method for high dimensional data classification. Stat. Interface 2(2), 153 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  103. M. Haindl, P. Somol, D. Ververidis, C. Kotropoulos, Feature selection based on mutual correlation, in Iberoamerican Congress on Pattern Recognition (Springer, 2006), pp. 569–577

    Google Scholar 

  104. S.N. Ghazavi, T.W. Liao, Medical data mining by fuzzy modeling with selected features. Artif. Intell. Med. 43(3), 195–206 (2008)

    Article  Google Scholar 

  105. C. Ding, H. Peng, Minimum redundancy feature selection from microarray gene expression data. J. Bioinform. Comput. Biol. 3(02), 185–205 (2005)

    Article  Google Scholar 

  106. A.J. Ferreira, M.A. Figueiredo, An unsupervised approach to feature discretization and selection. Pattern Recogn. 45(9), 3048–3060 (2012)

    Article  Google Scholar 

  107. A.J. Ferreira, M.A. Figueiredo, Efficient feature selection filters for high-dimensional data. Pattern Recogn. Lett. 33(13), 1794–1804 (2012)

    Article  Google Scholar 

  108. H. Vural, A. Subaşı, Data-mining techniques to classify microarray gene expression data using gene selection by SVD and information gain. Model. Artif. Intell. 2, 171–182 (2015)

    Article  Google Scholar 

  109. H. Abdi, Singular value decomposition (SVD) and generalized singular value decomposition, in Encyclopedia of Measurement and Statistics (2007), pp. 907–912

    Google Scholar 

  110. W. Ahmed, Fast orthogonal search for training radial basis function neural networks. Ph.D. Dissertation, University of Maine (1994)

    Google Scholar 

  111. I.A. Gheyas, L.S. Smith, Feature subset selection in large dimensionality domains. Pattern Recogn. 43(1), 5–13 (2010)

    Article  MATH  Google Scholar 

  112. A. Srivastava, S. Chakrabarti, S. Das, S. Ghosh, V.K. Jayaraman, Hybrid firefly based simultaneous gene selection and cancer classification using support vector machines and random forests, in Proceedings of Seventh International Conference on Bio-inspired Computing: Theories and Applications (BIC-TA 2012) (Springer, 2013), pp. 485–494

    Google Scholar 

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

    Article  Google Scholar 

  114. E. Martinez, M.M. Alvarez, V. Trevino, Compact cancer biomarkers discovery using a swarm intelligence feature selection algorithm. Comput. Biol. Chem. 34(4), 244–250 (2010)

    Article  Google Scholar 

  115. M.M. Kabir, M. Shahjahan, K. Murase, A new hybrid ant colony optimization algorithm for feature selection. Expert Syst. Appl. 39(3), 3747–3763 (2012)

    Article  Google Scholar 

  116. H. Yu, G. Gu, H. Liu, J. Shen, J. Zhao, A modified ant colony optimization algorithm for tumor marker gene selection. Genomics, Proteomics Bioinform. 7(4), 200–208 (2009)

    Article  Google Scholar 

  117. I. Inza, P. Larrañaga, R. Blanco, A.J. Cerrolaza, Filter versus wrapper gene selection approaches in DNA microarray domains. Artif. Intell. Med. 31(2), 91–103 (2004)

    Article  Google Scholar 

  118. I. Inza, B. Sierra, R. Blanco, P. Larrañaga, Gene selection by sequential search wrapper approaches in microarray cancer class prediction. J. Intell. Fuzzy Syst. 12(1), 25–33 (2002)

    MATH  Google Scholar 

  119. M. Ghoneimy, E. Nabil, A. Badr, S.F. El-Khamisy, Bioscience Research

    Google Scholar 

  120. H.M. Alshamlan, G.H. Badr, Y.A. Alohali, ABC-SVM: artificial bee colony and SVM method for microarray gene selection and multi class cancer classification. Int. J. Mach. Learn. Comput 6(3), 184 (2016)

    Article  Google Scholar 

  121. E. Alba, J. Garcia-Nieto, L. Jourdan, E.-G. Talbi, Gene selection in cancer classification using PSO, SVM and GA, SVM hybrid algorithms, in IEEE Congress on Evolutionary Computation (IEEE, 2007), pp. 284–290

    Google Scholar 

  122. R.R. Rani, D. Ramyachitra, Microarray cancer gene feature selection using spider monkey optimization algorithm and cancer classification using SVM. Procedia Comput. Sci. 143, 108–116 (2018)

    Article  Google Scholar 

  123. N. Almugren, H. Alshamlan, FF-SVM: new firefly-based gene selection algorithm for microarray cancer classification, in 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) (IEEE, 2019), pp. 1–6

    Google Scholar 

  124. U. Maulik, D. Chakraborty, Fuzzy preference based feature selection and semisupervised SVM for cancer classification. IEEE Trans. Nanobiosci. 13(2), 152–160 (2014)

    Article  Google Scholar 

  125. M.-S. Chen, T.-Y. Ho, D.-Y. Huang, Online transductive support vector machines for classification, in 2012 International Conference on Information Security and Intelligent Control (IEEE, 2012), pp. 258–261

    Google Scholar 

  126. L. Zhang, W. Zhou, B. Wang, Z. Zhang, F. Li, Applying 1-norm SVM with squared loss to gene selection for cancer classification. Appl. Intell. 48(7), 1878–1890 (2018)

    Article  Google Scholar 

  127. Y. Wang, I.V. Tetko, M.A. Hall, E. Frank, A. Facius, K.F. Mayer, H.W. Mewes, Gene selection from microarray data for cancer classification–a machine learning approach. Comput. Biol. Chem. 29(1), 37–46 (2005)

    Article  MATH  Google Scholar 

  128. R. Alanni, J. Hou, H. Azzawi, Y. Xiang, A novel gene selection algorithm for cancer classification using microarray datasets. BMC Med. Genomics 12(1), 1–12 (2019)

    Article  Google Scholar 

  129. S. Kar, K.D. Sharma, M. Maitra, 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 

  130. J. Dev, S.K. Dash, S. Dash, M. Swain, A classification technique for microarray gene expression data using PSO-FLANN. Int. J. Comput. Sci. Eng. 4(9), 1534 (2012)

    Google Scholar 

  131. K. Chatra, V. Kuppili, D.R. Edla, A.K. Verma, Cancer data classification using binary bat optimization and extreme learning machine with a novel fitness function. Med. Biol. Eng. Comput. 57(12), 2673–2682 (2019)

    Article  Google Scholar 

  132. W. Zhao, G. Wang, H. Wang, H. Chen, H. Dong, Z. Zhao, A novel framework for gene selection. Int. J. Adv. Comput. Technol. 3(3), 184–191 (2011)

    Google Scholar 

  133. H. Alshamlan, G. Badr, Y. Alohali, mRMR-ABC: a hybrid gene selection algorithm for cancer classification using microarray gene expression profiling. Biomed. Res. Int. 2015 (2015)

    Google Scholar 

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

    Article  Google Scholar 

  135. A. El Akadi, A. Amine, A. El Ouardighi, D. Aboutajdine, A new gene selection approach based on minimum redundancy-maximum relevance (MRMR) and genetic algorithm (GA), in 2009 IEEE/ACS International Conference on Computer Systems and Applications (IEEE, 2009), pp. 69–75

    Google Scholar 

  136. H. Liu, L. Liu, H. Zhang, Ensemble gene selection by grouping for microarray data classification. J. Biomed. Inform. 43(1), 81–87 (2010)

    Article  Google Scholar 

  137. M.J. Abdi, S.M. Hosseini, M. Rezghi, A novel weighted support vector machine based on particle swarm optimization for gene selection and tumor classification. Comput. Math. Methods Med. 2012 (2012)

    Google Scholar 

  138. L. Gao, M. Ye, X. Lu, D. Huang, Hybrid method based on information gain and support vector machine for gene selection in cancer classification. Genomics, Proteomics Bioinform. 15(6), 389–395 (2017)

    Article  Google Scholar 

  139. C.-C. Chang, C.-J. Lin, LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 1–27 (2011)

    Article  Google Scholar 

  140. G. Wang, Q. Song, B. Xu, Y. Zhou, Selecting feature subset for high dimensional data via the propositional foil rules. Pattern Recogn. 46(1), 199–214 (2013)

    Article  Google Scholar 

  141. R. Díaz-Uriarte, S.A. De Andres, Gene selection and classification of microarray data using random forest. BMC Bioinform. 7(1), 3 (2006)

    Article  Google Scholar 

  142. K.-B. Duan, J.C. Rajapakse, H. Wang, F. Azuaje, Multiple SVM-RFE for gene selection in cancer classification with expression data. IEEE Trans. Nanobiosci. 4(3), 228–234 (2005)

    Article  Google Scholar 

  143. K.-B. Duan, J.C. Rajapakse, M.N. Nguyen, One-versus-one and one-versus-all multiclass SVM-RFE for gene selection in cancer classification, in European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (Springer, 2007), pp. 47–56

    Google Scholar 

  144. E.H. Houssein, H.N. Hassan, M.M. Al-Sayed, E. Nabil, Gene selection for microarray cancer classification based on manta rays foraging optimization and support vector machines (2021)

    Google Scholar 

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Authors and Affiliations

  1. Faculty of Computers and Information, Minia University, Minia, Egypt

    Essam Halim Houssein, Hager N. Hassan & Mustafa M. Al-Sayed

  2. Faculty of Computers and Artificial Intelligence, Cairo University, Cairo, Egypt

    Emad Nabil

  3. Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah, Saudi Arabia

    Emad Nabil

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  1. Essam Halim Houssein
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  2. Hager N. Hassan
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  3. Mustafa M. Al-Sayed
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  4. Emad Nabil
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Correspondence to Essam Halim Houssein .

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  1. Faculty of Computers and Information, Minia University, Minia, Egypt

    Essam Halim Houssein

  2. Faculty of Computer Science & Engineering, Galala University, Suze, Egypt

    Mohamed Abd Elaziz

  3. Department of Computer Sciences, University of Guadalajara, Guadalajara, Jalisco, Mexico

    Diego Oliva

  4. Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, Jordan

    Laith Abualigah

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Houssein, E.H., Hassan, H.N., Al-Sayed, M.M., Nabil, E. (2022). Intelligent Computational Models for Cancer Diagnosis: A Comprehensive Review. In: Houssein, E.H., Abd Elaziz, M., Oliva, D., Abualigah, L. (eds) Integrating Meta-Heuristics and Machine Learning for Real-World Optimization Problems. Studies in Computational Intelligence, vol 1038. Springer, Cham. https://doi.org/10.1007/978-3-030-99079-4_2

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