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Feature selection methods in microarray gene expression data: a systematic mapping study

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Abstract

Feature selection (FS) is an important area of research in medicine and genetics. Cancer classification based on the microarray gene expression data is a challenge in this area due to its high-dimensional features and small sample size. This can negatively impact the performance of data mining and machine learning algorithms. FS is a key issue in reducing the size of the microarray, which is done to obtain useful information and eliminate redundant features. With the absence of a thorough investigation of the field, it is almost impossible for researchers to get an idea of how their work relates to existing studies and how it contributes to the research community. This paper provides a systematic mapping study to analyze and synthesize the studies conducted on the FS techniques in microarrays. To this end, 108 related articles published between 2000 and February 2022 were selected and reviewed based on five criteria: year and region, FS method adopted, dataset type, source of release, and type of evaluation software. Our main goal is to provide a fair idea to future researchers about the current situation of the field and future directions. The results of the study showed that classification is the most important task in FS. In a history-based evaluation, evolutionary methods were found to have the widest application to FS.

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References

  1. Shah S, Kusiak A (2007) Cancer gene search with data-mining and genetic algorithms. Comput Biol Med 37:251–261. https://doi.org/10.1016/j.compbiomed.2006.01.007

    Article  Google Scholar 

  2. Aminzadeh A, Ramzanpoor M, Molaarazi A, Kebria Ghasemi F, Roshandel G (2017) Relationship between rainfall and temperature with the incidence of cancer in Golestan Province, northern Iran. J Gorgan Univ Med Sci 19:80–85

    Google Scholar 

  3. Jain I, Jain VK, Jain R (2018) Correlation feature selection based improved-binary particle swarm optimization for gene selection and cancer classification. Appl Soft Comput 62:203–215. https://doi.org/10.1016/j.asoc.2017.09.038

    Article  Google Scholar 

  4. Chlioui I, Idri A, Abnane I (2020) Data preprocessing in knowledge discovery in breast cancer: systematic mapping study. Comput Methods Biomech Biomed Eng Imaging Vis. https://doi.org/10.1080/21681163.2020.1730974

    Article  Google Scholar 

  5. Idri A, Chlioui I, Ouassif BEl (2018) A systematic map of data analytics in breast cancer. In: Proceedings of the Australasian computer science week multiconference, proceedings of the Australasian computer science week multiconference. pp 1–10 https://doi.org/10.1145/3167918.3167930

  6. Kadi I, Idri A, Fernandez-Aleman JL (2019) Systematic mapping study of data mining–based empirical studies in cardiology. Health Inform J 25:741–770. https://doi.org/10.1177/1460458217717636

    Article  Google Scholar 

  7. Benhar H, Idri A, Fernandez-Aleman JL (2019) A systematic mapping study of data preparation in heart disease knowledge discovery. J Med Syst 43:1–17. https://doi.org/10.1007/s10916-018-1134-z

    Article  Google Scholar 

  8. El Idrissi T, Idri A, Bakkoury Z (2018) Data mining techniques in diabetes self-management: A systematic map,. In: World conference on information systems and technologies. vol 162, pp 1142–1152. https://doi.org/10.1007/978-3-319-77712-2

  9. Idri A, Benhar H, Fernandez-Aleman JL, Kadi I (2018) A systematic map of medical data preprocessing in knowledge discovery. Comput Methods Programs Biomed. https://doi.org/10.1016/j.cmpb.2018.05.007

    Article  Google Scholar 

  10. Maldonado S, Weber R, Famili F (2014) Feature selection for high-dimensional class-imbalanced data sets using support vector machines. Inf Sci 286:228–246. https://doi.org/10.1016/j.ins.2014.07.015

    Article  Google Scholar 

  11. Wang SL, Li X, Zhang S, Gui J, Huang DS (2010) Tumor classification by combining PNN classifier ensemble with neighborhood rough set based gene reduction. Comput Biol Med 40:179–189. https://doi.org/10.1016/j.compbiomed.2009.11.014

    Article  Google Scholar 

  12. Duval B, Hao JK (2010) Advances in metaheuristics for gene selection and classification of microarray data. Brief Bioinform 11:127–141. https://doi.org/10.1093/bib/bbp035

    Article  Google Scholar 

  13. AbdElNabi MLR, Wajeeh Jasim M, EL Bakry HM, Taha MHN, Khalifa NEM (2020) Breast and colon cancer classification from gene expression profiles using data mining techniques. Symmetry 12:1–16. https://doi.org/10.3390/sym12030408

    Article  Google Scholar 

  14. Santhakumar D, Logeswari S (2020) Efficient attribute selection technique for leukaemia prediction using microarray gene data. Soft Comput 24:14265–14274. https://doi.org/10.1007/s00500-020-04793-z

    Article  Google Scholar 

  15. Gumaei A, Sammouda R, Al-Rakhami M, AlSalman H, El-Zaart A (2021) Feature selection with ensemble learning for prostate cancer diagnosis from microarray gene expression. Health Inform J 27:1–13. https://doi.org/10.1177/1460458221989402

    Article  Google Scholar 

  16. Fajila F, Yusof Y (2021) Incremental search for informative gene selection in cancer classification. Ann Emerg Technol Comput (AETiC) 5:15–21. https://doi.org/10.33166/AETiC.2021.02.002

    Article  Google Scholar 

  17. Qasem SN, Saeed F (2021) Hybrid feature selection and ensemble learning methods for gene selection and cancer classification. Int J Adv Comput Sci Appl (IJACSA) 12:193–200. https://doi.org/10.14569/IJACSA.2021.0120225

    Article  Google Scholar 

  18. Hamim M, Moudden El I, Hicham M, Hain M (2021) Gene selection for cancer classification: a new hybrid filter-C5.0 approach for breast cancer risk prediction. Adv Sci Technol Eng Syst J 6:871–878. https://doi.org/10.25046/aj060196

    Article  Google Scholar 

  19. Chandrakar PK, Shrivas AK, Sahu N (2021) Design of a novel ensemble model of classification technique for gene-expression data of lung cancer with modified genetic algorithm. EAI Endorsed Trans Pervasive Health Technol 7:1–13. https://doi.org/10.4108/eai.8-1-2021.167845

    Article  Google Scholar 

  20. www.scopus.com

  21. www.sciencedirect.com

  22. https://pubmed.ncbi.nlm.nih.gov

  23. Bolon-Canedo V, Sanchez-Marono N, Alonso-Betanzos A (2012) An ensemble of filters and classifiers for microarray data classification. Pattern Recogn 45:531–539. https://doi.org/10.1016/j.patcog.2011.06.006

    Article  Google Scholar 

  24. Yu H, Ni J, Zhao J (2013) ACOSampling: an ant colony optimization-based undersampling method for classifying imbalanced DNA microarray data. Neurocomputing 101:309–318. https://doi.org/10.1016/j.neucom.2012.08.018

    Article  Google Scholar 

  25. www.mathworks.com/products/matlab.htm

  26. www.cs.waikato.ac.nz/~ml/weka

  27. www.r-project.org

  28. www.python.org

  29. www.csie.ntu.edu.tw/~cjlin/libsvm

  30. https://hadoop.apache.org

  31. www.oracle.com/java

  32. https://isocpp.org

  33. https://docs.microsoft.com/en-us/cpp

  34. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif intell Res 16:321–357. https://doi.org/10.1613/jair.953

    Article  MATH  Google Scholar 

  35. Liu B, Cui Q, Jiang T, Ma S (2004) A combinational feature selection and ensemble neural network method for classification of gene expression data. BMC Bioinform 5:1–12. https://doi.org/10.1186/1471-2105-5-136

    Article  Google Scholar 

  36. Valentini G, Muselli M, Ruffino F (2004) Cancer recognition with bagged ensembles of support vector machines. Neurocomputing 56:461–466. https://doi.org/10.1016/j.neucom.2003.09.001

    Article  Google Scholar 

  37. Yu Z, Chen H, You J, Liu J, Wong HS, Han Guoqiang, Li Le (2014) Adaptive fuzzy consensus clustering framework for clustering analysis of cancer data. IEEE/ACM Trans Comput Biol Bioinf 12:887–901. https://doi.org/10.1109/TCBB.2014.2359433

    Article  Google Scholar 

  38. Sun L, Wang W, Xu J, Zhang S (2019) Improved LLE and neighborhood rough sets-based gene selection using Lebesgue measure for cancer classification on gene expression data. J Intell Fuzzy Syst 37:5731–5742. https://doi.org/10.3233/JIFS-181904

    Article  Google Scholar 

  39. Potharaju SP, Sreedevi M (2019) Distributed feature selection (DFS) strategy for microarray gene expression data to improve the classification performance. Clin Epidemiol Glob Health 7:171–176. https://doi.org/10.1016/j.cegh.2018.04.001

    Article  Google Scholar 

  40. Yu Z, Zhang Y, Chen CLP, You J, Wong HS, Dai D, Wu S, Zhang J (2018) Multiobjective semisupervised classifier ensemble. IEEE Trans Cybern 49:2280–2293. https://doi.org/10.1109/TCYB.2018.2824299

    Article  Google Scholar 

  41. Zhao W, Wang G, Wang HB, Chen HL, Dong H, Zhao ZD (2011) A novel framework for gene selection. Int J Adv Comput Technol 3:184–191. https://doi.org/10.4156/ijact.vol3.issue3.18

    Article  Google Scholar 

  42. Liu KH, Tong M, Xie ST, Yee Ng VT (2015) Genetic programming based ensemble system for microarray data classification. Comput Math Methods Med 2015:1–11. https://doi.org/10.1155/2015/193406

    Article  Google Scholar 

  43. Chen Z, Li J, Wei L, Xu W, Shi Y (2011) Multiple-kernel SVM based multiple-task oriented data mining system for gene expression data analysis. Expert Syst Appl 38:12151–12159. https://doi.org/10.1016/j.eswa.2011.03.025

    Article  Google Scholar 

  44. Han F, Sun W, Ling QH (2014) A novel strategy for gene selection of microarray data based on gene-to-class sensitivity information. PLoS ONE 9:888–896. https://doi.org/10.1016/j.neunet.2011.05.010

    Article  Google Scholar 

  45. Nagpal A, Singh V (2019) Feature selection from high dimensional data based on iterative qualitative mutual information. J Intell Fuzzy Syst 36:5845–5856. https://doi.org/10.3233/JIFS-181665

    Article  Google Scholar 

  46. Wu XY, Wu ZY, Kang Li (2008) Identification of differential gene expression for microarray data using recursive random forest. Chin Med J 121:2492–2496. https://doi.org/10.1097/00029330-200812020-00005

    Article  Google Scholar 

  47. Wang H, Jing X, Niu B (2017) A discrete bacterial algorithm for feature selection in classification of microarray gene expression cancer data. Knowl-Based Syst 126:8–19. https://doi.org/10.1016/j.knosys.2017.04.004

    Article  Google Scholar 

  48. Piao H (2011) DNA microarray data analysis using a correlational Bayesian network. J Med Imaging Health Inform 1:366–370. https://doi.org/10.1166/jmihi.2011.1044

    Article  Google Scholar 

  49. Sathya M, Manju Priya S (2020) Modified Whale Optimization Algorithm For Feature Selection In Micro Array Cancer Dataset. Int J Sci Technol Res 9:549–556

    Google Scholar 

  50. Leung YY, Chang CQ, Hung YS (2012) An integrated approach for identifying wrongly labelled samples when performing classification in microarray data. PLoS ONE 7:1–10. https://doi.org/10.1371/journal.pone.0046700

    Article  Google Scholar 

  51. Islam AK, Jeong S, Bari AT, Lim CG, Jeon SH (2015) MapReduce based parallel gene selection method. Appl Intell 42:147–156. https://doi.org/10.1007/s10489-014-0561-x

    Article  Google Scholar 

  52. Tang J, Zhou S (2016) A new approach for feature selection from microarray data based on mutual information. IEEE/ACM Trans Comput Biol Bioinf 13:1004–1015. https://doi.org/10.1109/TCBB.2016.2515582

    Article  Google Scholar 

  53. Bolon-Canedo V, Sanchez-Marono N, Alonso-Betanzos A (2014) Data classification using an ensemble of filters. Neurocomputing 135:13–20. https://doi.org/10.1016/j.neucom.2013.03.067

    Article  Google Scholar 

  54. Lai CM, Yeh WC, Chang Chung-Yi (2016) Gene selection using information gain and improved simplified swarm optimization. Neurocomputing 218:331–338. https://doi.org/10.1016/j.neucom.2016.08.089

    Article  Google Scholar 

  55. Khaire UM, Dhanalakshmi R (2020) Stability investigation of improved whale optimization algorithm in the process of feature selection. Int J Data Min Boinform. https://doi.org/10.1080/02564602.2020.1843554

    Article  MATH  Google Scholar 

  56. Li J, Wang Fei (2016) Towards unsupervised gene selection: a matrix factorization framework. IEEE/ACM Trans Comput Biol Bioinf 14:514–521. https://doi.org/10.1109/TCBB.2016.2591545

    Article  Google Scholar 

  57. Zhou X, Tuck DP (2007) MSVM-RFE: extensions of SVM-RFE for multiclass gene selection on DNA microarray data. Bioinformatics 23:1106–1114. https://doi.org/10.1093/bioinformatics/btm036

    Article  Google Scholar 

  58. Fortino V, Kinaret P, Fyhrquist N, Alenius H, Greco D (2014) A robust and accurate method for feature selection and prioritization from multi-class OMICs data. PLoS ONE 9:1–9. https://doi.org/10.1371/journal.pone.0107801

    Article  Google Scholar 

  59. Jansi Rani M, Devaraj D (2019) Two-stage hybrid gene selection using mutual information and genetic algorithm for cancer data classification. J Med Syst 43:1–11. https://doi.org/10.1007/s10916-019-1372-8

    Article  Google Scholar 

  60. Yan C, Ma J, Luo H, Zhang G, Luo J (2019) A novel feature selection method for high-dimensional biomedical data based on an improved binary clonal flower pollination algorithm. Hum Hered 84:34–46. https://doi.org/10.1159/000501652

    Article  Google Scholar 

  61. Baliarsingh SK, Vipsita S, Muhammad K, Bakshi S (2019) Analysis of high-dimensional biomedical data using an evolutionary multi-objective emperor penguin optimizer. Swarm Evol Comput 48:262–273. https://doi.org/10.1016/j.swevo.2019.04.010

    Article  Google Scholar 

  62. Venkataramana L, Jacob SG, Ramadoss R, Saisuma D, Haritha D, Manoja K (2019) Improving classification accuracy of cancer types using parallel hybrid feature selection on microarray gene expression data. Genes Genomics 41:1301–1313. https://doi.org/10.1007/s13258-019-00859-x

    Article  Google Scholar 

  63. Dif N, Elberrichi Z (2019) An enhanced recursive firefly algorithm for informative gene selection. Int J Swarm Intell Res (IJSIR) 10:21–33. https://doi.org/10.4018/IJSIR.2019040102

    Article  Google Scholar 

  64. Mekour N, Hamou RM, Amine A (2019) Filter/wrapper methods for gene selection and classification of microarray dataset. J Softw Innov (IJSI) 7:65–80. https://doi.org/10.4018/IJSI.2019070104

    Article  Google Scholar 

  65. Alomari OA, Khader AT, Al-Betar MA, Abualigah LM (2017) Gene selection for cancer classification by combining minimum redundancy maximum relevancy and bat-inspired algorithm. Int J Data Min Bioinform 19:32–51. https://doi.org/10.1504/IJDMB.2017.088538

    Article  Google Scholar 

  66. Aziz R, Verma CK, Srivastava N (2017) A novel approach for dimension reduction of microarray. Comput Biol Chem 71:161–169. https://doi.org/10.1016/j.compbiolchem.2017.10.009

    Article  Google Scholar 

  67. Annavarapu CS, Dara S, Banka H (2016) Cancer microarray data feature selection using multi-objective binary particle swarm optimization algorithm. EXCLI J 15:460–473. https://doi.org/10.17179/excli2016-481

    Article  Google Scholar 

  68. Tran B, Xue B, Zhang M (2016) Genetic programming for feature construction and selection in classification on high-dimensional data. Memet Comput 8:3–15. https://doi.org/10.1007/s12293-015-0173-y

    Article  Google Scholar 

  69. Chhabra G, Vashisht V, Ranjan J (2019) Improving accuracy for cancer classification with gene selection. Int J Innov Technol Explor Eng (IJITEE) 8:192–199

    Google Scholar 

  70. Mohamed NS, Zainudin S, Othman ZA (2017) Metaheuristic approach for an enhanced mRMR filter method for classification using drug response microarray data. Expert Syst Appl 90:224–231. https://doi.org/10.1016/j.eswa.2017.08.026

    Article  Google Scholar 

  71. Salem H, Attiya G, El-Fishawy N (2017) Classification of human cancer diseases by gene expression profiles. Appl Soft Comput 50:124–134. https://doi.org/10.1016/j.asoc.2016.11.026

    Article  Google Scholar 

  72. Brahim AB, Limam M (2016) A hybrid feature selection method based on instance learning and cooperative subset search. Pattern Recogn Lett 69:28–34. https://doi.org/10.1016/j.patrec.2015.10.005

    Article  Google Scholar 

  73. Bennet J, Ganaprakasam C, Kumar N (2015) A hybrid approach for gene selection and classification using support vector machine. Int Arab J Inf Technol (IAJIT) 12:695–700

    Google Scholar 

  74. Hatami N, Chira C (2013) Diverse accurate feature selection for microarray cancer diagnosis. Intell Data Anal 17:697–716. https://doi.org/10.3233/IDA-130601

    Article  Google Scholar 

  75. Boucheham A, Batouche M, Meshoul S (2015) Robust hybrid wrapper/filter biomarker discovery from gene expression data based on generalised Island model. Int J Comput Biol Drug Des 8:251–274

    Article  Google Scholar 

  76. Park CH, Kim SB (2015) Sequential random k-nearest neighbor feature selection for high-dimensional data. Expert Syst Appl 42:2336–2342. https://doi.org/10.1016/j.eswa.2014.10.044

    Article  Google Scholar 

  77. Gonzalez F, Belanche LA (2013) Feature selection for microarray gene expression data using simulated annealing guided by the multivariate joint entropy. Computacion y Sistemas 18:275–293. https://doi.org/10.13053/cys-18-2-1473

    Google Scholar 

  78. Han F, Yang S, Guan J (2015) An effective hybrid approach of gene selection and classification for microarray data based on clustering and particle swarm optimisation. Int J Data Min Bioinform 13:103–121. https://doi.org/10.1504/ijdmb.2015.071515

    Article  Google Scholar 

  79. Dessì N, Pes B, Cannas LM (2015) An evolutionary approach for balancing effectiveness and representation level in gene selection. J Inf Technol Res (JITR) 8:16–33. https://doi.org/10.4018/jitr.2015040102

    Article  Google Scholar 

  80. Wang A, An N, Chen G, Li L, Alterovitz G (2015) Accelerating wrapper-based feature selection with K-nearest-neighbor. Knowl-Based Syst 83:81–91. https://doi.org/10.1016/j.knosys.2015.03.009

    Article  Google Scholar 

  81. Qiu X, Qiu Y, Feng G, Li P (2015) A sparse fuzzy c-means algorithm based on sparse clustering framework. Neurocomputing 157:290–295. https://doi.org/10.1016/j.neucom.2015.01.003

    Article  Google Scholar 

  82. Mavroeidis D, Marchiori E (2014) Feature selection for k-means clustering stability: theoretical analysis and an algorithm. Data Min Knowl Disc 28:918–960. https://doi.org/10.1007/s10618-013-0320-3

    Article  MathSciNet  MATH  Google Scholar 

  83. Li X, Gong X, Peng X, Peng S (2014) SSiCP: a new svm based recursive feature elimination algorithm for multiclass cancer classification. Int J Multimed Ubiquitous Eng 9:347–360. https://doi.org/10.14257/ijmue.2014.9.6.33

    Article  Google Scholar 

  84. Park DK, Jung EY, Lee SH, Lim JS (2015) A composite gene selection for DNA microarray data analysis. Multimed Tools Appl 74:9031–9041. https://doi.org/10.1007/s11042-013-1583-9

    Article  Google Scholar 

  85. Prasartvit T, Banharnsakun A, Kaewkamnerdpong B, Achalakul T (2013) Reducing bioinformatics data dimension with ABC-kNN. Neurocomputing 116:367–381. https://doi.org/10.1016/j.neucom.2012.01.045

    Article  Google Scholar 

  86. Li Z, Yang A, Chen X, Zeng L, Cao T (2014) A composite method for feature selection of microarray data. J Comput Theor Nanosci 11:472–476. https://doi.org/10.1166/jctn.2014.3382

    Article  Google Scholar 

  87. Sumathi A, Santhoshkumar S, Sakthivel NK (2012) Development of an efficient data mining classifier with microarray data set for gene selection and classification. J Theor Appl Inf Technol 35:208–214

    Google Scholar 

  88. Revathy N, Balasubramanian R (2012) GA-SVM wrapper approach for gene ranking and classification using expressions of very few genes. J Theor Appl Inf Technol 40:113–119

    Google Scholar 

  89. Porto-Diaz I, Bolon-Canedo V, Alonso-Betanzos A, Fontenla-Romero O (2011) A study of performance on microarray data sets for a classifier based on information theoretic learning. Neural Netw 24:888–896. https://doi.org/10.1016/j.neunet.2011.05.010

    Article  Google Scholar 

  90. Du W, Sun Y, Wang Y, Cao Z, Zhang C, Liang Y (2013) A novel multi-stage feature selection method for microarray expression data analysis. Int J Data Min Bioinform 7:58–77. https://doi.org/10.1504/ijdmb.2013.050977

    Article  Google Scholar 

  91. Jeyachidra J, Punithavalli M, Jeyachidra J (2015) A Novel Distinguishability Based Weighted Feature Selection Algorithms for Improved Classification of Gene Microarray. 11:443–452. https://doi.org/10.3844/jcssp.2015.443.452

  92. Sungheetha A, Suganthi J (2013) An efficient clustering-classification method in an information gain NRGA-KNN algorithm for feature selection of micro array data. Life Sci J 10:691–700

    Google Scholar 

  93. Apiletti D, Baralis E, Bruno G, Fiori A (2012) Maskedpainter: feature selection for microarray data analysis. Intell Data Anal 16:717–737. https://doi.org/10.3233/IDA-2012-0546

    Article  Google Scholar 

  94. Luo L, Ye L, Luo M, Huang D, Peng H, Yang F (2011) Methods of forward feature selection based on the aggregation of classifiers generated by single attribute. Comput Biol Med 41:435–441. https://doi.org/10.1016/j.compbiomed.2011.04.005

    Article  Google Scholar 

  95. Mahmoodian H, Marhaban Hamiruce M, Abdulrahim R, Rosli R, Saripan I (2011) Using fuzzy association rule mining in cancer classification. Aust Phys Eng Sci Med 34:41–54. https://doi.org/10.1007/s13246-011-0054-8

    Article  Google Scholar 

  96. Chuang LY, Ke CH, Chang HW, Yang CH (2009) A two-stage feature selection method for gene expression data. OMICS 13:127–137. https://doi.org/10.1089/omi.2008.0083

    Article  Google Scholar 

  97. Chuang LY, Ke CH, Chang HW, Yang CH (2008) An evolutionary algorithm approach to optimal ensemble classifiers for DNA microarray data analysis. IEEE Trans Evol Comput 12:377–388. https://doi.org/10.1109/TEVC.2007.906660

    Article  Google Scholar 

  98. Debnath R, Kurita T (2010) An evolutionary approach for gene selection and classification of microarray data based on SVM error-bound theories. Biosystems 100:39–46. https://doi.org/10.1016/j.biosystems.2009.12.006

    Article  Google Scholar 

  99. Wang X, Gotoh O (2009) Accurate molecular classification of cancer using simple rules. BMC Med Genomics 2:1–23. https://doi.org/10.1186/1755-8794-2-64

    Article  Google Scholar 

  100. Zhu S, Wang D, Yu K, Li T, Gong Y (2008) Feature selection for gene expression using model-based entropy. IEEE/ACM Trans Comput Biol Bioinf 7:25–36. https://doi.org/10.1109/TCBB.2008.35

    Article  Google Scholar 

  101. Zhang LJ, Li ZJ, Chen HW (2008) Handling gene redundancy in microarray data using grey relational analysis. Int J Data Min Bioinform 2:134–144. https://doi.org/10.1504/IJDMB.2008.019094

    Article  Google Scholar 

  102. Shen Q, Shi WM, Kong W (2008) Hybrid particle swarm optimization and tabu search approach for selecting genes for tumor classification using gene expression data. Comput Biol Chem 32:53–60. https://doi.org/10.1016/j.compbiolchem.2007.10.001

    Article  MATH  Google Scholar 

  103. Kianmehr K, Zhang H, Nikolov K, Özyer T, Alhajj R (2007) Utilising neural network and support vector machine for gene expression classification. J Inf Knowl Manag 6:251–260. https://doi.org/10.1142/S0219649207001822

    Article  Google Scholar 

  104. Chiang JH, Ho SH (2008) A combination of rough-based feature selection and RBF neural network for classification using gene expression data. IEEE Trans Nanobiosci 7:91–99. https://doi.org/10.1109/TNB.2008.2000142

    Article  Google Scholar 

  105. Dash R, Misra B (2017) Gene selection and classification of microarray data: a Pareto DE approach. Intell Decis Technol 11:93–107. https://doi.org/10.3233/IDT-160280

    Article  Google Scholar 

  106. Lin HY (2016) Gene discretization based on EM clustering and adaptive sequential forward gene selection for molecular classification. Appl Soft Comput 48:683–690. https://doi.org/10.1016/j.asoc.2016.07.015

    Article  Google Scholar 

  107. Huang HL, Chang FL (2007) ESVM: evolutionary support vector machine for automatic feature selection and classification of microarray data. Biosystems 90:516–528. https://doi.org/10.1016/j.biosystems.2006.12.003

    Article  Google Scholar 

  108. Yousef M, Jung S, Showe LC, Showe MK (2007) Recursive Cluster Elimination (RCE) for classification and feature selection from gene expression data. BMC Bioinform 8:1–12. https://doi.org/10.1186/1471-2105-8-144

    Article  Google Scholar 

  109. Dashtban M, Balafar M, Suravajhala P (2018) Gene selection for tumor classification using a novel bio-inspired multi-objective approach. Genomics 110:10–17. https://doi.org/10.1016/j.ygeno.2017.07.010

    Article  Google Scholar 

  110. Vanitha CDA, Devaraj D, Venkatesulu M (2015) Gene expression data classification using support vector machine and mutual information-based gene selection. Procedia Comput Sci 47:13–21. https://doi.org/10.1016/j.procs.2015.03.178

    Article  Google Scholar 

  111. Nematzadeh H, Enayatifar R, Mahmud M, Akbari E (2019) Frequency based feature selection method using whale algorithm. Genomics 111:1946–1955. https://doi.org/10.1016/j.ygeno.2019.01.006

    Article  Google Scholar 

  112. Bolon-Canedo V, Sanchez-Marono N, Alonso-Betanzos A (2015) Distributed feature selection: An application to microarray data classification. Appl Soft Comput 30:136–150. https://doi.org/10.1016/j.asoc.2015.01.035

    Article  Google Scholar 

  113. Garro BA, Rodriguez K, Vazquez RA (2016) Classification of DNA microarrays using artificial neural networks and ABC algorithm. Appl Soft Comput 38:548–560. https://doi.org/10.1016/j.asoc.2015.10.002

    Article  Google Scholar 

  114. Iam-On N, Boongoen T, Garrett S (2010) LCE: a link-based cluster ensemble method for improved gene expression data analysis. Bioinformatics 26:1513–1519. https://doi.org/10.1093/bioinformatics/btq226

    Article  Google Scholar 

  115. Moayedikia A, Ong KL, Boo YL, Yeoh WGS, Jensen R (2017) Feature selection for high dimensional imbalanced class data using harmony search. Eng Appl Artif Intell 57:38–49. https://doi.org/10.1016/j.engappai.2016.10.008

    Article  Google Scholar 

  116. Ram M, Najafi A, Shakeri MT (2017) Classification and biomarker genes selection for cancer gene expression data using random forest. Iran J Pathol 12:339–347. https://doi.org/10.30699/ijp.2017.27990

    Article  Google Scholar 

  117. Liu KH, Zeng ZH, Ng VTY (2016) A hierarchical ensemble of ECOC for cancer classification based on multi-class microarray data. Inf Sci 349:102–118. https://doi.org/10.1016/j.ins.2016.02.028

    Article  Google Scholar 

  118. Liu H, Liu L, Zhang H (2010) Ensemble gene selection for cancer classification. Pattern Recogn 43:2763–2772. https://doi.org/10.1016/j.patcog.2010.02.008

    Article  Google Scholar 

  119. Balakrishnan K, Dhanalakshmi R, Khaire UM (2021) Improved salp swarm algorithm based on the levy flight for feature selection. J Supercomput 77:12399–12419. https://doi.org/10.1007/s11227-021-03773-w

    Article  Google Scholar 

  120. Azadifar S, Ahmadi A (2021) A graph-based gene selection method for medical diagnosis problems using a many-objective PSO algorithm. BMC Med Inform Decis Mak 21:1–16. https://doi.org/10.1186/s12911-021-01696-3

    Article  Google Scholar 

  121. Xie J, Wang M, Xu S, Huang Z, Grant PW (2021) The Unsupervised Feature Selection Algorithms Based on Standard Deviation and Cosine Similarity for Genomic Data Analysis. Front Genet 12:1–17. https://doi.org/10.3389/fgene.2021.684100

    Article  Google Scholar 

  122. Zhang H (2021) Feature selection using approximate conditional entropy based on fuzzy information granule for gene expression data classification. Front Genet 12:1–8. https://doi.org/10.3389/fgene.2021.631505

    Article  Google Scholar 

  123. Dash R (2021) An adaptive harmony search approach for gene selection and classification of high dimensional medical data. J King Saud Univ-Comput Inf Sci 33:195–207. https://doi.org/10.1016/j.jksuci.2018.02.013

    Article  Google Scholar 

  124. Mahmood SG, Karyakos RS, Yacoob IM (2021) Hybrid gene selection method based on mutual information technique and dragonfly optimization algorithm. East-Eur J Enterp Technol 3:64–69. https://doi.org/10.15587/1729-4061.2021.233382

    Article  Google Scholar 

  125. Sharifai AG, Zainol ZB (2021) Multiple filter-based rankers to guide hybrid grasshopper optimization algorithm and simulated annealing for feature selection with high dimensional multi-class imbalanced datasets. IEEE Access 9:74127–74142. https://doi.org/10.1109/ACCESS.2021.3081366

    Article  Google Scholar 

  126. Hamim M, El Moudden I, Pant MD, Moutachaouik H, Hain M (2021) A hybrid gene selection strategy based on fisher and ant colony optimization algorithm for breast cancer classification. Int J Online Biomed Eng (iJOE) 17:148–163. https://doi.org/10.3991/ijoe.v17i02.19889

    Article  Google Scholar 

  127. Baliarsingh SK, Vipsita S, Gandomi AH, Panda A, Bakshi S, Ramasubbareddy S (2020) Analysis of high-dimensional genomic data using MapReduce based probabilistic neural network. Comput Methods Programs Biomed 195:105–625. https://doi.org/10.1016/j.cmpb.2020.105625

    Article  Google Scholar 

  128. Sharifai AG, Zainol Z (2020) The correlation-based redundancy multiple-filter approach for gene selection. Int J Data Min Bioinform 23:62–78. https://doi.org/10.1504/ijdmb.2020.10027155

    Article  Google Scholar 

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Acknowledgements

This research was partially supported by Shokrolah Vahmiyan based on grant number (No.1/S/K). We thank Dr. Vahid Erfani-Moghaddam from Golestan University of Medical Sciences, who provided useful comments that greatly improved the research.

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  1. Mahnaz Vahmiyan

    Present address: Department of Computer Engineering, Babol Branch, Islamic Azad University, Babol, Iran

  2. Mohammadtaghi Kheirabadi

    Present address: Department of Computer Engineering, Gorgan Branch, Islamic Azad University, Gorgan, Iran

  3. Ebrahim Akbari

    Present address: Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran

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    1. Mahnaz Vahmiyan
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    Correspondence to Mohammadtaghi Kheirabadi.

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    Vahmiyan, M., Kheirabadi, M. & Akbari, E. Feature selection methods in microarray gene expression data: a systematic mapping study. Neural Comput & Applic 34, 19675–19702 (2022). https://doi.org/10.1007/s00521-022-07661-z

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