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A new optimal gene selection approach for cancer classification using enhanced Jaya-based forest optimization algorithm

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Abstract

In microarray experiments, the sample size is considerably smaller than that of the feature size, thereby imposing the curse of dimensionality problem. To resolve this issue, evolutionary algorithms are often utilized. In this paper, a novel framework for feature selection and classification of the microarray data is presented. Initially, a statistical filter, namely ANOVA, is used to select the relevant genes (features) from the original set of genes. Then, an evolutionary wrapper-based approach utilizing the principles of enhanced Jaya (EJaya) algorithm and forest optimization algorithm (FOA) is proposed to find the optimal set of genes from the previously selected genes. The main objective of using EJaya is to tune the two important parameters, namely local seeding changes and global seeding changes of FOA. During the selection of the optimal set of genes, support vector machine is employed as a classifier to classify the microarray data. To perform a comprehensive experimental study, the proposed method is tested on both binary-class and multi-class microarray datasets. From the extensive result analysis, it has been observed that the proposed technique achieves better classification accuracy with considerably less number of features than that of the benchmark schemes.

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References

  1. Algamal ZY, Lee MH (2018) A two-stage sparse logistic regression for optimal gene selection in high-dimensional microarray data classification. In: Advances in data analysis and classification. Springer, Berlin, pp 1–19

    Google Scholar 

  2. Alon U, Barkai N, Notterman DA, Gish K, Ybarra S, Mack D, Levine AJ (1999) Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc Natl Acad Sci 96(12):6745–6750

    Google Scholar 

  3. Alshamlan H, Badr G, Alohali Y (2015a) mRMR-ABC: a hybrid gene selection algorithm for cancer classification using microarray gene expression profiling. BioMed Res Int 2015:604910–604910

    Google Scholar 

  4. Alshamlan HM, Badr GH, Alohali YA (2015b) Genetic bee colony (GBC) algorithm: a new gene selection method for microarray cancer classification. Comput Biol Chem 56:49–60

    Google Scholar 

  5. Apolloni J, Leguizamón G, Alba E (2016) Two hybrid wrapper-filter feature selection algorithms applied to high-dimensional microarray experiments. Appl Soft Comput 38:922–932

    Google Scholar 

  6. Armstrong SA, Staunton JE, Silverman LB, Pieters R, den Boer ML, Minden MD, Sallan SE, Lander ES, Golub TR, Korsmeyer SJ (2001) Mll translocations specify a distinct gene expression profile that distinguishes a unique leukemia. Nat Genet 30(1):41

    Google Scholar 

  7. Baliarsingh SK, Vipsita S, Muhammad K, Dash B, Bakshi S (2019) Analysis of high-dimensional genomic data employing a novel bio-inspired algorithm. Appl Soft Comput 77:520–532

    Google Scholar 

  8. Bhattacharjee A, Richards WG, Staunton J, Li C, Monti S, Vasa P, Ladd C, Beheshti J, Bueno R, Gillette M et al (2001) Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc Natl Acad Sci 98(24):13790–13795

    Google Scholar 

  9. Bolón-Canedo V, Sánchez-Maroño N, Alonso-Betanzos A (2015) Distributed feature selection: an application to microarray data classification. Appl Soft Comput 30:136–150

    Google Scholar 

  10. Chinnaswamy A, Srinivasan R (2016) Hybrid feature selection using correlation coefficient and particle swarm optimization on microarray gene expression data. In: Innovations in bio-inspired computing and applications. Springer, Cham, pp 229–239

    Google Scholar 

  11. Cho-Vega JH, Rassidakis GZ, Admirand JH, Oyarzo M, Ramalingam P, Paraguya A, McDonnell TJ, Amin HM, Medeiros LJ (2004) Mcl-1 expression in b-cell non-hodgkin’s lymphomas. Hum Pathol 35(9):1095–1100

    Google Scholar 

  12. Chouhan SS, Kaul A, Singh UP (2018a) Soft computing approaches for image segmentation: a survey. Multimed Tools Appl 77(21):28483–28537

    Google Scholar 

  13. Chouhan SS, Kaul A, Singh UP, Jain S (2018b) Bacterial foraging optimization based radial basis function neural network (BRBFNN) for identification and classification of plant leaf diseases: an automatic approach towards plant pathology. IEEE Access 6:8852–8863

    Google Scholar 

  14. Chuang JC, Stehr H, Liang Y, Das M, Huang J, Diehn M, Wakelee HA, Neal JW (2017) Erbb2-mutated metastatic non-small cell lung cancer: response and resistance to targeted therapies. J Thorac Oncol 12(5):833–842

    Google Scholar 

  15. Collins CT, Hess JL (2016) Role of hoxa9 in leukemia: dysregulation, cofactors and essential targets. Oncogene 35(9):1090

    Google Scholar 

  16. Dashtban M, Balafar M (2017) Gene selection for microarray cancer classification using a new evolutionary method employing artificial intelligence concepts. Genomics 109(2):91–107

    Google Scholar 

  17. Dwivedi AK (2018) Artificial neural network model for effective cancer classification using microarray gene expression data. Neural Comput Appl 29(12):1545–1554

    Google Scholar 

  18. Elyasigomari V, Lee D, Screen H, Shaheed M (2017) Development of a two-stage gene selection method that incorporates a novel hybrid approach using the cuckoo optimization algorithm and harmony search for cancer classification. J Biomed Inform 67:11–20

    Google Scholar 

  19. Eskinazi R, Thöny B, Svoboda M, Robberecht P, Dassesse D, Heizmann CW, Van Laethem JL, Resibois A (1999) Overexpression of pterin-4a-carbinolamine dehydratase/dimerization cofactor of hepatocyte nuclear factor 1 in human colon cancer. Am J Pathol 155(4):1105–1113

    Google Scholar 

  20. Ezejiofor IF, Adelusola K, Durosinmi MA, Leoncini L, Odesanmi WO, Ambrosio MR, Lazzi S, Olaofe RO, Gbutorano G et al (2018) Immunohistochemical characterization of small round blue cell tumors of childhood at ile-ife, Nigeria: a 10-year retrospective study. Arch Med Health Sci 6(1):64

    Google Scholar 

  21. Galani E, Sgouros J, Petropoulou C, Janinis J, Aravantinos G, Dionysiou-Asteriou D, Skarlos D, Gonos E (2002) Correlation of mdr-1, nm23-h1 and h sema e gene expression with histopathological findings and clinical outcome in ovarian and breast cancer patients. Anticancer Res 22(4):2275–2280

    Google Scholar 

  22. García-Nieto J, Alba E (2012a) Parallel multi-swarm optimizer for gene selection in DNA microarrays. Appl Intell 37(2):255–266

    Google Scholar 

  23. García-Nieto J, Alba E (2012b) Parallel multi-swarm optimizer for gene selection in DNA microarrays. Appl Intell 37(2):255–266

    Google Scholar 

  24. Ghaemi M, Feizi-Derakhshi MR (2014) Forest optimization algorithm. Exp Syst Appl 41(15):6676–6687

    Google Scholar 

  25. Ghosh M, Guha R, Sarkar R, Abraham A (2019) A wrapper-filter feature selection technique based on ant colony optimization. Neural Comput Appl. https://doi.org/10.1007/s00521-019-04171-3

    Article  Google Scholar 

  26. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA et al (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286(5439):531–537

    Google Scholar 

  27. Hall MA (1999) Correlation-based feature selection for machine learning. Doctoral dissertation, The University of Waikato

  28. Heit C, Jackson BC, McAndrews M, Wright MW, Thompson DC, Silverman GA, Nebert DW, Vasiliou V (2013) Update of the human and mouse serpin gene superfamily. Hum Genom 7(1):22

    Google Scholar 

  29. Hernandez JCH, Duval B, Hao JK (2007) A genetic embedded approach for gene selection and classification of microarray data. In: European conference on evolutionary computation, machine learning and data mining in bioinformatics, Springer, pp 90–101

  30. Ibrahim AO, Shamsuddin SM, Abraham A, Qasem SN (2019) Adaptive memetic method of multi-objective genetic evolutionary algorithm for backpropagation neural network. Neural Comput Appl. https://doi.org/10.1007/s00521-018-03990-0

    Article  Google Scholar 

  31. Jothi G, Inbarani HH, Azar AT, Devi KR (2018) Rough set theory with jaya optimization for acute lymphoblastic leukemia classification. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3359-7

    Article  Google Scholar 

  32. Jung JH, Jung CK, Choi HJ, Jun KH, Yoo J, Kang SJ, Lee KY (2009) Diagnostic utility of expression of claudins in non-small cell lung cancer: different expression profiles in squamous cell carcinomas and adenocarcinomas. Pathol Res Pract 205(6):409–416

    Google Scholar 

  33. Kar S, Sharma KD, Maitra M (2015) Gene selection from microarray gene expression data for classification of cancer subgroups employing PSO and adaptive K-nearest neighborhood technique. Exp Syst Appl 42(1):612–627

    Google Scholar 

  34. Kečo D, Subasi A, Kevric J (2018) Cloud computing-based parallel genetic algorithm for gene selection in cancer classification. Neural Comput Appl 30(5):1601–1610

    Google Scholar 

  35. Kim Y, Yoon S, Kim SJ, Kim JS, Cheong JW, Min YH (2012) Myeloperoxidase expression in acute myeloid leukemia helps identifying patients to benefit from transplant. Yonsei Med J 53(3):530–536

    Google Scholar 

  36. Lee CT, Chow NH, Su PF, Lin SC, Lin PC, Lee JC (2008) The prognostic significance of ron and met receptor coexpression in patients with colorectal cancer. Dis Colon Rectum 51(8):1268–1274

    Google Scholar 

  37. Li T, Zhang C, Ogihara M (2004) A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression. Bioinformatics 20(15):2429–2437

    Google Scholar 

  38. 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

    Google Scholar 

  39. Matthews BW (1975) Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim Biophys Acta (BBA) Protein Struct 405(2):442–451

    Google Scholar 

  40. Melhem R, Xx Zhu, Hailat N, Strahler JR, Hanash SM (1991) Characterization of the gene for a proliferation-related phosphoprotein (oncoprotein 18) expressed in high amounts in acute leukemia. J Biol Chem 266(27):17747–17753

    Google Scholar 

  41. Mohapatra P, Chakravarty S, Dash P (2016) Microarray medical data classification using kernel ridge regression and modified cat swarm optimization based gene selection system. Swarm Evolut Comput 28:144–160

    Google Scholar 

  42. Motieghader H, Najafi A, Sadeghi B, Masoudi-Nejad A (2017) A hybrid gene selection algorithm for microarray cancer classification using genetic algorithm and learning automata. Inform Med Unlocked 9:246–254

    Google Scholar 

  43. Mukhopadhyay A, Bandyopadhyay S, Maulik U (2010) Multi-class clustering of cancer subtypes through svm based ensemble of pareto-optimal solutions for gene marker identification. PloS One 5(11):e13803

    Google Scholar 

  44. Nash MA, Deavers MT, Freedman RS (2002) The expression of decorin in human ovarian tumors. Clin Cancer Res 8(6):1754–1760

    Google Scholar 

  45. Niu Q, Zhang H, Li K (2014a) An improved TLBO with elite strategy for parameters identification of PEM fuel cell and solar cell models. Int J Hydrog Energy 39(8):3837–3854

    Google Scholar 

  46. Niu Q, Zhang L, Li K (2014b) A biogeography-based optimization algorithm with mutation strategies for model parameter estimation of solar and fuel cells. Energy Convers Manag 86:1173–1185

    Google Scholar 

  47. Orujpour M, Feizi-Derakhshi MR, Rahkar-Farshi T (2019) Multi-modal forest optimization algorithm. Neural Comput Appl. https://doi.org/10.1007/s00521-019-04113-z

    Article  Google Scholar 

  48. Pal NR, Aguan K, Sharma A, Amari Si (2007) Discovering biomarkers from gene expression data for predicting cancer subgroups using neural networks and relational fuzzy clustering. BMC Bioinform 8(1):5

    Google Scholar 

  49. Pang S, Havukkala I, Hu Y, Kasabov N (2007) Classification consistency analysis for bootstrapping gene selection. Neural Comput Appl 16(6):527–539

    Google Scholar 

  50. Petricoin EF III, Ardekani AM, Hitt BA, Levine PJ, Fusaro VA, Steinberg SM, Mills GB, Simone C, Fishman DA, Kohn EC et al (2002) Use of proteomic patterns in serum to identify ovarian cancer. Lancet 359(9306):572–577

    Google Scholar 

  51. 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(2):171–176

    Google Scholar 

  52. Rao R (2016) Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 7(1):19–34

    Google Scholar 

  53. Sharma A, Paliwal KK, Imoto S, Miyano S (2014) A feature selection method using improved regularized linear discriminant analysis. Mach Vis Appl 25(3):775–786

    Google Scholar 

  54. Sharma S, Kaul A (2018) Hybrid fuzzy multi-criteria decision making based multi cluster head dolphin swarm optimized IDS for VANET. Veh Commun 12:23–38

    Google Scholar 

  55. Sheskin DJ (2003) Handbook of parametric and nonparametric statistical procedures. CRC Press, Boca Raton

    MATH  Google Scholar 

  56. Sokolova M, Lapalme G (2009) A systematic analysis of performance measures for classification tasks. Inf Process Manag 45(4):427–437

    Google Scholar 

  57. Tabakhi S, Najafi A, Ranjbar R, Moradi P (2015) Gene selection for microarray data classification using a novel ant colony optimization. Neurocomputing 168:1024–1036

    Google Scholar 

  58. Tang B, Xiang K, Pang M (2018) An integrated particle swarm optimization approach hybridizing a new self-adaptive particle swarm optimization with a modified differential evolution. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3878-2

    Article  Google Scholar 

  59. Valdés-Mora F, Locke WJ, Bandrés E, Gallego-Ortega D, Cejas P, García-Cabezas MA, Colino-Sanguino Y, Feliú J, del Pulgar TG, Lacal JC (2017) Clinical relevance of the transcriptional signature regulated by cdc42 in colorectal cancer. Oncotarget 8(16):26755

    Google Scholar 

  60. Wang A, An N, Chen G, Yang J, Li L, Alterovitz G (2014a) Incremental wrapper based gene selection with Markov blanket. In: 2014 IEEE international conference on bioinformatics and biomedicine (BIBM). IEEE, pp 74–79

  61. Wang X, Gotoh O (2009) Accurate molecular classification of cancer using simple rules. BMC Med Genom 2(1):64

    Google Scholar 

  62. Wang Y, Yang XG, Lu Y (2019) Informative gene selection for microarray classification via adaptive elastic net with conditional mutual information. Appl Math Model 71:286–297

    MathSciNet  MATH  Google Scholar 

  63. Wang ZQ, Bachvarova M, Morin C, Plante M, Gregoire J, Renaud MC, Sebastianelli A, Bachvarov D (2014b) Role of the polypeptide n-acetylgalactosaminyltransferase 3 in ovarian cancer progression: possible implications in abnormal mucin o-glycosylation. Oncotarget 5(2):544

    Google Scholar 

  64. Yagasaki F, Wakao D, Yokoyama Y, Uchida Y, Murohashi I, Kayano H, Taniwaki M, Matsuda A, Bessho M (2001) Fusion of etv6 to fibroblast growth factor receptor 3 in peripheral t-cell lymphoma with at (4; 12)(p16; p13) chromosomal translocation. Cancer Res 61(23):8371–8374

    Google Scholar 

  65. Yakirevich E, Resnick MB, Mangray S, Wheeler M, Jackson CL, Lombardo KA, Lee J, Kim KM, Gill AJ, Wang K et al (2016) Oncogenic alk fusion in rare and aggressive subtype of colorectal adenocarcinoma as a potential therapeutic target. Clin Cancer Res 22(15):3831–3840

    Google Scholar 

  66. Yu K, Wang X, Wang Z (2016) An improved teaching-learning-based optimization algorithm for numerical and engineering optimization problems. J Intell Manuf 27(4):831–843

    Google Scholar 

  67. Zhao H, Sun Q, Li L, Zhou J, Zhang C, Hu T, Zhou X, Zhang L, Wang B, Li B et al (2019) High expression levels of aggf1 and mfap4 predict primary platinum-based chemoresistance and are associated with adverse prognosis in patients with serous ovarian cancer. J Cancer 10(2):397

    Google Scholar 

  68. Zhao Y, Lu H, Yan A, Yang Y, Meng Q, Sun L, Pang H, Li C, Dong X, Cai L (2013) Abcc3 as a marker for multidrug resistance in non-small cell lung cancer. Sci Rep 3:3120

    Google Scholar 

  69. Zhu Z, Ong YS, Dash M (2007) Markov blanket-embedded genetic algorithm for gene selection. Pattern Recognit 40(11):3236–3248

    MATH  Google Scholar 

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Acknowledgements

This research is partially supported by the following Grant: Grant No. SR/FST/ETI-335/2013 by Fund for Improvement of S&T Infrastructure in Higher Educational Institutions (FIST) Program of Department of Science and Technology, Government of India to International Institute of Information Technology, Bhubaneswar, Odisha, India.

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  1. DST-FIST Bioinformatics Laboratory, Department of Computer Science and Engineering, International Institute of Information Technology, Bhubaneswar, 751003, India

    Santos Kumar Baliarsingh, Swati Vipsita & Bodhisattva Dash

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  1. Santos Kumar Baliarsingh
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Baliarsingh, S.K., Vipsita, S. & Dash, B. A new optimal gene selection approach for cancer classification using enhanced Jaya-based forest optimization algorithm. Neural Comput & Applic 32, 8599–8616 (2020). https://doi.org/10.1007/s00521-019-04355-x

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