{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:28:47Z","timestamp":1775665727425,"version":"3.50.1"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"35-36","license":[{"start":{"date-parts":[[2020,10,6]],"date-time":"2020-10-06T00:00:00Z","timestamp":1601942400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,10,6]],"date-time":"2020-10-06T00:00:00Z","timestamp":1601942400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100004054","name":"King Abdulaziz University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004054","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1007\/s00521-020-05367-8","type":"journal-article","created":{"date-parts":[[2020,10,6]],"date-time":"2020-10-06T10:02:41Z","timestamp":1601978561000},"page":"29049-29060","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["Optimized gene selection and classification of cancer from microarray gene expression data using deep learning"],"prefix":"10.1007","volume":"37","author":[{"given":"Shamveel Hussain","family":"Shah","sequence":"first","affiliation":[]},{"given":"Muhammad Javed","family":"Iqbal","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3719-2387","authenticated-orcid":false,"given":"Iftikhar","family":"Ahmad","sequence":"additional","affiliation":[]},{"given":"Suleman","family":"Khan","sequence":"additional","affiliation":[]},{"given":"Joel J. P. C.","family":"Rodrigues","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,6]]},"reference":[{"key":"5367_CR1","unstructured":"NIH (2019) National Cancer Institute (NCI), cancer statistics. Available from: https:\/\/www.cancer.gov\/. Accessed 23 April 2019"},{"key":"5367_CR2","unstructured":"World Health Organization, Cancer (2018) Available from: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/cancer. Accessed 23 April 2019"},{"key":"5367_CR3","doi-asserted-by":"crossref","unstructured":"Babu M, Sarkar K (2016) A comparative study of gene selection methods for cancer classification using microarray data. In: 2016 second international conference on research in computational intelligence and communication networks (ICRCICN). IEEE","DOI":"10.1109\/ICRCICN.2016.7813657"},{"key":"5367_CR4","doi-asserted-by":"publisher","DOI":"10.18201\/ijisae.267094","author":"MT Arslan","year":"2016","unstructured":"Arslan MT, Kalinli A (2016) A comparative study of statistical and artificial intelligence based classification algorithms on central nervous system cancer microarray gene expression data. Int J Intell Syst Appl Eng. https:\/\/doi.org\/10.18201\/ijisae.267094","journal-title":"Int J Intell Syst Appl Eng"},{"key":"5367_CR5","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.ins.2014.05.042","volume":"282","author":"V Bol\u00f3n-Canedo","year":"2014","unstructured":"Bol\u00f3n-Canedo V, S\u00e1nchez-Marono N, Alonso-Betanzos A, Ben\u00edtez JM, Herrera F (2014) A review of microarray datasets and applied feature selection methods. Inf Sci 282:111\u2013135","journal-title":"Inf Sci"},{"key":"5367_CR6","doi-asserted-by":"publisher","first-page":"17605","DOI":"10.4238\/2015.December.21.33","volume":"14","author":"H Hu","year":"2015","unstructured":"Hu H, Niu Z, Bai Y, Tan X (2015) Cancer classification based on gene expression using neural networks. Genet Mol Res 14:17605\u201317611","journal-title":"Genet Mol Res"},{"issue":"3\/4","key":"5367_CR7","first-page":"01","volume":"2","author":"A Bhola","year":"2015","unstructured":"Bhola A, Tiwari AK (2015) Machine learning based approaches for cancer classification using gene expression data. Mach Learn Appl Int J 2(3\/4):01\u201312","journal-title":"Mach Learn Appl Int J"},{"key":"5367_CR8","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.procs.2015.04.060","volume":"50","author":"RK Singh","year":"2015","unstructured":"Singh RK, Sivabalakrishnan M (2015) Feature selection of gene expression data for cancer classification: a review. Proc Comput Sci 50:52\u201357","journal-title":"Proc Comput Sci"},{"key":"5367_CR9","unstructured":"G\u00f6lc\u00fck G (2017) Cancer classification using gene expression data with deep learning. Paper presented at Department of Electronics, Informatics and Bioengineering Polytechnic University of Milan, Italy, 20 Dec 2017. http:\/\/hdl.handle.net\/10589\/138427"},{"key":"5367_CR10","doi-asserted-by":"publisher","first-page":"72622","DOI":"10.1109\/ACCESS.2019.2918275","volume":"7","author":"MZ Khan","year":"2019","unstructured":"Khan MZ, Harous S, Hassan SU, Khan MUG, Iqbal R, Mumtaz S (2019) Deep unified model for face recognition based on convolution neural network and edge computing. IEEE Access 7:72622\u201372633","journal-title":"IEEE Access"},{"key":"5367_CR11","doi-asserted-by":"crossref","unstructured":"Guillen P, Ebalunode J (2016) Cancer classification based on microarray gene expression data using deep learning. In: 2016 international conference on computational science and computational intelligence (CSCI). IEEE","DOI":"10.1109\/CSCI.2016.0270"},{"key":"5367_CR12","doi-asserted-by":"crossref","unstructured":"Bhat RR, Viswanath V, Li X (2017) DeepCancer: detecting cancer via deep generative learning through gene expressions. In: 2017 IEEE 15th international conference on dependable, autonomic and secure computing, 15th international conference on pervasive intelligence and computing, 3rd international conference on big data intelligence and computing and cyber science and technology congress (DASC\/PiCom\/DataCom\/CyberSciTech). IEEE","DOI":"10.1109\/DASC-PICom-DataCom-CyberSciTec.2017.152"},{"key":"5367_CR13","doi-asserted-by":"crossref","unstructured":"Danaee P, Ghaeini R, Hendrix DA (2017) A deep learning approach for cancer detection and relevant gene identification. In: Pacific symposium on biocomputing 2017. World Scientific","DOI":"10.1142\/9789813207813_0022"},{"issue":"2","key":"5367_CR14","first-page":"555557","volume":"1","author":"Z Wenyan","year":"2017","unstructured":"Wenyan Z, Xuewen L, Jingjing W (2017) Feature selection for cancer classification using microarray gene expression data. Biostat Biom Open Access J 1(2):555557","journal-title":"Biostat Biom Open Access J"},{"key":"5367_CR15","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2020.2986322","author":"S Dang","year":"2020","unstructured":"Dang S, Wen M, Mumtaz S, Li J, Li C (2020) Enabling multi-carrier relay selection by sensing fusion and cascaded ANN for intelligent vehicular communications. IEEE Sens J. https:\/\/doi.org\/10.1109\/JSEN.2020.2986322","journal-title":"IEEE Sens J"},{"issue":"03","key":"5367_CR16","doi-asserted-by":"publisher","first-page":"1940007","DOI":"10.1142\/S0219720019400079","volume":"17","author":"T Matsubara","year":"2019","unstructured":"Matsubara T, Ochiai T, Hayashida M, Akutsu T, Nacher JC (2019) Convolutional neural network approach to lung cancer classification integrating protein interaction network and gene expression profiles. J Bioinform Comput Biol 17(03):1940007","journal-title":"J Bioinform Comput Biol"},{"issue":"5","key":"5367_CR17","first-page":"454","volume":"8","author":"S Hamena","year":"2018","unstructured":"Hamena S, Meshoul S (2018) Multi-class classification of gene expression data using deep learning for cancer prediction. Int J Mach Learn Comput 8(5):454\u2013459","journal-title":"Int J Mach Learn Comput"},{"key":"5367_CR18","unstructured":"Luque-Baena R, Urda D, Subirats J, Franco L, Jerez J (2013) Analysis of cancer microarray data using constructive neural networks and genetic algorithms. In: Proceedings of the IWBBIO, international work-conference on bioinformatics and biomedical engineering"},{"issue":"1","key":"5367_CR19","first-page":"126","volume":"5","author":"A Natarajan","year":"2014","unstructured":"Natarajan A, Ravi T (2014) A survey on gene feature selection using microarray data for cancer classification. Int J Comput Sci Commun (IJCSC) 5(1):126\u2013129","journal-title":"Int J Comput Sci Commun (IJCSC)"},{"issue":"1","key":"5367_CR20","doi-asserted-by":"publisher","first-page":"16477","DOI":"10.1038\/s41598-018-34833-6","volume":"8","author":"Y Kong","year":"2018","unstructured":"Kong Y, Yu T (2018) A deep neural network model using random forest to extract feature representation for gene expression data classification. Sci Rep 8(1):16477","journal-title":"Sci Rep"},{"key":"5367_CR21","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1016\/j.procs.2015.06.035","volume":"54","author":"M Kumar","year":"2015","unstructured":"Kumar M, Rath NK, Swain A, Rath SK (2015) Feature selection and classification of microarray data using MapReduce based ANOVA and K-nearest neighbor. Proc Comput Sci 54:301\u2013310","journal-title":"Proc Comput Sci"},{"key":"5367_CR22","doi-asserted-by":"publisher","DOI":"10.1002\/ett.4017","author":"MS Iqbal","year":"2020","unstructured":"Iqbal MS, Ahmad I, Bin L, Khan S, Rodrigues JJ (2020) Deep learning recognition of diseased and normal cell representation. Trans Emerg Telecommun Technol. https:\/\/doi.org\/10.1002\/ett.4017","journal-title":"Trans Emerg Telecommun Technol"},{"key":"5367_CR23","doi-asserted-by":"crossref","unstructured":"Lyu B, Haque A (2018) Deep learning based tumor type classification using gene expression data. In: Proceedings of the 2018 ACM international conference on bioinformatics, computational biology, and health informatics. ACM","DOI":"10.1101\/364323"},{"key":"5367_CR24","doi-asserted-by":"publisher","first-page":"22874","DOI":"10.1109\/ACCESS.2020.2970210","volume":"8","author":"NEM Khalifa","year":"2020","unstructured":"Khalifa NEM, Taha MHN, Ali DE, Slowik A, Hassanien AE (2020) Artificial intelligence technique for gene expression by tumor RNA-seq data: a novel optimized deep learning approach. IEEE Access 8:22874\u201322883","journal-title":"IEEE Access"},{"issue":"6","key":"5367_CR25","doi-asserted-by":"publisher","first-page":"9237","DOI":"10.1109\/JIOT.2019.2896120","volume":"6","author":"S Khan","year":"2019","unstructured":"Khan S, Muhammad K, Mumtaz S, Baik SW, de Albuquerque VHC (2019) Energy-efficient deep CNN for smoke detection in foggy IoT environment. IEEE Internet Things J 6(6):9237\u20139245","journal-title":"IEEE Internet Things J"},{"issue":"5","key":"5367_CR26","first-page":"1523","volume":"2","author":"G Reena","year":"2011","unstructured":"Reena G (2011) A survey of human cancer classification using micro array data. Int J Comput Technol Appl 2(5):1523\u20131533. http:\/\/www.ijcta.com\/vol2issue5-page3.php","journal-title":"Int J Comput Technol Appl"},{"key":"5367_CR27","unstructured":"Joseph M, Devaraj M, Leung CK (2019) DeepGx: deep learning using gene expression for cancer classification. In: 2019 IEEE\/ACM international conference on advances in social networks analysis and mining (ASONAM). IEEE"},{"key":"5367_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12920-020-0677-2","volume":"13","author":"M Mostavi","year":"2020","unstructured":"Mostavi M, Chiu Y-C, Huang Y, Chen Y (2020) Convolutional neural network models for cancer type prediction based on gene expression. BMC Med Genom 13:1\u201313","journal-title":"BMC Med Genom"},{"issue":"3","key":"5367_CR29","doi-asserted-by":"publisher","first-page":"1013","DOI":"10.2298\/GENSR1403013V","volume":"46","author":"M Vimaladevi","year":"2014","unstructured":"Vimaladevi M, Kalaavathi B (2014) A microarray gene expression data classification using hybrid back propagation neural network. Genetika 46(3):1013\u20131026","journal-title":"Genetika"},{"key":"5367_CR30","doi-asserted-by":"crossref","unstructured":"Zeebaree DQ, Haron H, Abdulazeez AM (2018) Gene selection and classification of microarray data using convolutional neural network. In: 2018 international conference on advanced science and engineering (ICOASE). IEEE","DOI":"10.1109\/ICOASE.2018.8548836"},{"issue":"4","key":"5367_CR31","doi-asserted-by":"publisher","first-page":"594","DOI":"10.1016\/j.jbi.2013.03.009","volume":"46","author":"Z Mao","year":"2013","unstructured":"Mao Z, Cai W, Shao X (2013) Selecting significant genes by randomization test for cancer classification using gene expression data. J Biomed Inform 46(4):594\u2013601","journal-title":"J Biomed Inform"},{"key":"5367_CR32","volume-title":"Feature selection for cancer classification using microarray gene expression data","author":"W Zhong","year":"2014","unstructured":"Zhong W (2014) Feature selection for cancer classification using microarray gene expression data. University of Calgary, Calgary"},{"key":"5367_CR33","doi-asserted-by":"publisher","first-page":"e270","DOI":"10.7717\/peerj-cs.270","volume":"6","author":"R Tabares-Soto","year":"2020","unstructured":"Tabares-Soto R, Orozco-Arias S, Romero-Cano V, Bucheli VS, Rodr\u00edguez-Sotelo JL, Jim\u00e9nez-Var\u00f3n CF (2020) A comparative study of machine learning and deep learning algorithms to classify cancer types based on microarray gene expression data. PeerJ Comput Sci 6:e270","journal-title":"PeerJ Comput Sci"},{"key":"5367_CR34","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.asoc.2016.11.026","volume":"50","author":"H Salem","year":"2017","unstructured":"Salem H, Attiya G, El-Fishawy N (2017) Classification of human cancer diseases by gene expression profiles. Appl Soft Comput 50:124\u2013134","journal-title":"Appl Soft Comput"},{"issue":"65","key":"5367_CR35","doi-asserted-by":"publisher","first-page":"109646","DOI":"10.18632\/oncotarget.22762","volume":"8","author":"J Liu","year":"2017","unstructured":"Liu J, Wang X, Cheng Y, Zhang L (2017) Tumor gene expression data classification via sample expansion-based deep learning. Oncotarget 8(65):109646","journal-title":"Oncotarget"},{"issue":"3\u20134","key":"5367_CR36","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1007\/s00521-012-0847-z","volume":"22","author":"K Lee","year":"2013","unstructured":"Lee K, Man Z, Wang D, Cao Z (2013) Classification of bioinformatics dataset using finite impulse response extreme learning machine for cancer diagnosis. Neural Comput Appl 22(3\u20134):457\u2013468","journal-title":"Neural Comput Appl"},{"key":"5367_CR37","doi-asserted-by":"crossref","unstructured":"Wu Q, Boueiz A, Bozkurt A, Masoomi A, Wang A, DeMeo DL, Weiss ST, Qiu W (2018) Deep learning for predicting disease status using genomic data. PeerJ Preprints","DOI":"10.7287\/peerj.preprints.27123v1"},{"issue":"3","key":"5367_CR38","doi-asserted-by":"publisher","first-page":"8871","DOI":"10.4238\/2015.August.3.10","volume":"14","author":"Y Liu","year":"2015","unstructured":"Liu Y, Zhang N, He Y, Lun L (2015) Prediction of core cancer genes using a hybrid of feature selection and machine learning methods. Genet Mol Res 14(3):8871\u20138882","journal-title":"Genet Mol Res"},{"key":"5367_CR39","unstructured":"He X, Cai D, Niyogi P (2006) Laplacian score for feature selection. In: Advances in neural information processing systems"},{"issue":"7","key":"5367_CR40","first-page":"172","volume":"3","author":"S Mandal","year":"2015","unstructured":"Mandal S, Banerjee I (2015) Cancer classification using neural network. Int J Emerg Eng Res Technol 3(7):172\u2013178","journal-title":"Int J Emerg Eng Res Technol"},{"key":"5367_CR41","doi-asserted-by":"crossref","unstructured":"Liu B, Wei Y, Zhang Y, Yang Q (2017) Deep neural networks for high dimension, low sample size data. In: IJCAI","DOI":"10.24963\/ijcai.2017\/318"},{"issue":"5","key":"5367_CR42","doi-asserted-by":"publisher","first-page":"1360","DOI":"10.1093\/bioinformatics\/btz772","volume":"36","author":"B-H Kim","year":"2020","unstructured":"Kim B-H, Yu K, Lee PC (2020) Cancer classification of single-cell gene expression data by neural network. Bioinformatics 36(5):1360\u20131366","journal-title":"Bioinformatics"},{"key":"5367_CR43","unstructured":"Smolander J (2016) Deep learning classification methods for complex disorders"},{"key":"5367_CR44","unstructured":"Fakoor R, Ladhak F, Nazi A, Huber M (2013) Using deep learning to enhance cancer diagnosis and classification. In: Proceedings of the international conference on machine learning. ACM, New York, USA"},{"key":"5367_CR45","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.compbiomed.2014.01.014","volume":"47","author":"W Zhou","year":"2014","unstructured":"Zhou W, Dickerson JA (2014) A novel class dependent feature selection method for cancer biomarker discovery. Comput Biol Med 47:66\u201375","journal-title":"Comput Biol Med"},{"issue":"5","key":"5367_CR46","doi-asserted-by":"publisher","first-page":"802","DOI":"10.1007\/s11426-011-4263-5","volume":"54","author":"J Liu","year":"2011","unstructured":"Liu J, Cai W, Shao X (2011) Cancer classification based on microarray gene expression data using a principal component accumulation method. Sci China Chem 54(5):802\u2013811","journal-title":"Sci China Chem"},{"issue":"8","key":"5367_CR47","first-page":"2446","volume":"13","author":"A Nagpal","year":"2018","unstructured":"Nagpal A, Singh V (2018) Identification of significant features using random forest for high dimensional microarray data. J Eng Sci Technol 13(8):2446\u20132463","journal-title":"J Eng Sci Technol"},{"issue":"4","key":"5367_CR48","doi-asserted-by":"publisher","first-page":"339","DOI":"10.30699\/ijp.2017.27990","volume":"12","author":"M Ram","year":"2017","unstructured":"Ram M, Najafi A, Shakeri MT (2017) Classification and biomarker genes selection for cancer gene expression data using random forest. Iran J Pathol 12(4):339","journal-title":"Iran J Pathol"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05367-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-05367-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05367-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T11:48:02Z","timestamp":1766058482000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-020-05367-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,6]]},"references-count":48,"journal-issue":{"issue":"35-36","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["5367"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-05367-8","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,6]]},"assertion":[{"value":"5 May 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 September 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 October 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}