{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T17:02:22Z","timestamp":1778259742794,"version":"3.51.4"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"15","license":[{"start":{"date-parts":[[2023,10,17]],"date-time":"2023-10-17T00:00:00Z","timestamp":1697500800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,17]],"date-time":"2023-10-17T00:00:00Z","timestamp":1697500800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Data Science Research of Interdisciplinary Cyber-Physical Systems (ICPS) Programme of the Department of Science and Technology","award":["Sanction Number T-54"],"award-info":[{"award-number":["Sanction Number T-54"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-023-17149-0","type":"journal-article","created":{"date-parts":[[2023,10,17]],"date-time":"2023-10-17T07:02:10Z","timestamp":1697526130000},"page":"45761-45776","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Assessing gene stability and gene affinity in microarray data classification using an extended relieff algorithm"],"prefix":"10.1007","volume":"83","author":[{"given":"Neha","family":"Srivastava","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9044-8989","authenticated-orcid":false,"given":"Devendra K.","family":"Tayal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,17]]},"reference":[{"key":"17149_CR1","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1038\/35000501","volume":"403","author":"AA Alizadeh","year":"2000","unstructured":"Alizadeh AA, Eisen MB et al (2000) Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403:503\u2013511. https:\/\/doi.org\/10.1038\/35000501","journal-title":"Nature"},{"issue":"1","key":"17149_CR2","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.compeleceng.2013.11.024","volume":"40","author":"G Chandrashekar","year":"2014","unstructured":"Chandrashekar G, Sahin F (2014) A survey on feature selection methods. Comput Electr Eng 40(1):16\u201328. https:\/\/doi.org\/10.1016\/j.compeleceng.2013.11.024","journal-title":"Comput Electr Eng"},{"key":"17149_CR3","doi-asserted-by":"publisher","unstructured":"Dang\u00a0TH, Trung\u00a0DP, Tran\u00a0HL, Le Van\u00a0Q (2016) Using dimension reduction with feature selection to enhance accuracy of tumor classification. 2016 IntConf Biomed Eng (BME-HUST). https:\/\/doi.org\/10.1109\/bme-hust.2016.7782082","DOI":"10.1109\/bme-hust.2016.7782082"},{"issue":"2","key":"17149_CR4","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.ygeno.2017.01.004","volume":"109","author":"M Dashtban","year":"2017","unstructured":"Dashtban M, Balafar M (2017) Gene selection for microarray cancer classification using a new evolutionary method employing artificial intelligence concepts. Genomics 109(2):91\u2013107. https:\/\/doi.org\/10.1016\/j.ygeno.2017.01.004","journal-title":"Genomics"},{"key":"17149_CR5","unstructured":"Dhanalakshmi R,\u00a0 Khaire UM (2019) Feature selection and classification of microarray data for cancer prediction using mapreduce implementation of random forest algorithm. Journal of Scientific and Industrial Research\u00a0 78:158:161"},{"key":"17149_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.compbiomed.2015.08.010","volume":"66","author":"P Drot\u00e1r","year":"2015","unstructured":"Drot\u00e1r P, Gazda J, Sm\u00e9kal Z (2015) An experimental comparison of feature selection methods on two-class biomedical datasets. Comput Biol Med 66:1\u201310. https:\/\/doi.org\/10.1016\/j.compbiomed.2015.08.010","journal-title":"Comput Biol Med"},{"issue":"5\u20136","key":"17149_CR7","doi-asserted-by":"publisher","first-page":"641","DOI":"10.1016\/s0893-6080(03)00103-5","volume":"16","author":"C Furlanello","year":"2003","unstructured":"Furlanello C, Serafini M, Merler S, Jurman G (2003) An accelerated procedure for recursive feature ranking on microarray data. Neural Netw 16(5\u20136):641\u2013648. https:\/\/doi.org\/10.1016\/s0893-6080(03)00103-5","journal-title":"Neural Netw"},{"issue":"2","key":"17149_CR8","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.gene.2016.02.015","volume":"583","author":"A Ghosh","year":"2016","unstructured":"Ghosh A, Barman S (2016) Application of Euclidean distance measurement and principal component analysis for gene identification. Gene 583(2):112\u2013120. https:\/\/doi.org\/10.1016\/j.gene.2016.02.015","journal-title":"Gene"},{"key":"17149_CR9","doi-asserted-by":"publisher","unstructured":"Giurcaneanu\u00a0C, Tabus\u00a0I, Shmulevich\u00a0I, Wei Zhang (2003) Stability-based cluster analysis applied to microarray data. Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings. https:\/\/doi.org\/10.1109\/isspa.2003.1224814","DOI":"10.1109\/isspa.2003.1224814"},{"issue":"5439","key":"17149_CR10","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1126\/science.286.5439.531","volume":"286","author":"TR Golub","year":"1999","unstructured":"Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES (1999) Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 286(5439):531\u2013537. https:\/\/doi.org\/10.1126\/science.286.5439.531","journal-title":"Science"},{"issue":"6","key":"17149_CR11","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1109\/memb.2002.1175154","volume":"21","author":"J Goncalves","year":"2002","unstructured":"Goncalves J, Marks W (2002) Roles and requirements for a research microarray database. IEEE Eng Med Biol Mag 21(6):154\u2013157. https:\/\/doi.org\/10.1109\/memb.2002.1175154","journal-title":"IEEE Eng Med Biol Mag"},{"key":"17149_CR12","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1023\/A:1012487302797","volume":"46","author":"I Guyon","year":"2002","unstructured":"Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene Selection for Cancer Classification using Support Vector Machines. Mach Learn 46:389\u2013422. https:\/\/doi.org\/10.1023\/A:1012487302797","journal-title":"Mach Learn"},{"key":"17149_CR13","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.jco.2018.08.003","volume":"50","author":"A Hinrichs","year":"2019","unstructured":"Hinrichs A, Prochno J, Ullrich M (2019) The curse of dimensionality for numerical integration on general domains. J Complex 50:25\u201342. https:\/\/doi.org\/10.1016\/j.jco.2018.08.003","journal-title":"J Complex"},{"issue":"3","key":"17149_CR14","doi-asserted-by":"publisher","first-page":"754","DOI":"10.1109\/tcbb.2011.151","volume":"9","author":"S Imoto","year":"2012","unstructured":"Imoto S, Miyano S (2012) A Top-R feature selection algorithm for Microarray gene expression data. IEEE\/ACM Trans Comput Biol Bioinf 9(3):754\u2013764. https:\/\/doi.org\/10.1109\/tcbb.2011.151","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"key":"17149_CR15","unstructured":"K C,\u00a0S.\u00a0K, Mundayoor\u00a0S (2015) A BBO based feature selection method for DNA microarray. ARC J Int J Res Stud Biosci (IJRSB)3(1):201\u2013204"},{"issue":"2","key":"17149_CR16","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.ygeno.2012.05.014","volume":"100","author":"MW Khan","year":"2012","unstructured":"Khan MW, Alam M (2012) A survey of application: Genomics and genetic programming, a new frontier. Genomics 100(2):65\u201371. https:\/\/doi.org\/10.1016\/j.ygeno.2012.05.014","journal-title":"Genomics"},{"key":"17149_CR17","doi-asserted-by":"publisher","first-page":"584","DOI":"10.1016\/j.knosys.2015.09.005","volume":"89","author":"M Kumar","year":"2015","unstructured":"Kumar M, Kumar Rath S (2015) Classification of microarray using MapReduce based proximal support vector machine classifier. Knowl-Based Syst 89:584\u2013602. https:\/\/doi.org\/10.1016\/j.knosys.2015.09.005","journal-title":"Knowl-Based Syst"},{"key":"17149_CR18","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. Procedia Comput Sci 54:301\u2013310. https:\/\/doi.org\/10.1016\/j.procs.2015.06.035","journal-title":"Procedia Comput Sci"},{"key":"17149_CR19","doi-asserted-by":"publisher","unstructured":"Kumar\u00a0V (2014) Feature selection: A literature review. Smart Comput Rev 4(3). https:\/\/doi.org\/10.6029\/smartcr.2014.03.007","DOI":"10.6029\/smartcr.2014.03.007"},{"key":"17149_CR20","doi-asserted-by":"publisher","unstructured":"Li\u00a0X, Li\u00a0M, Yin\u00a0M (2017) Multiobjective ranking binary artificial bee colony for gene selection problems using microarray datasets. IEEE\/CAA J Autom Sin 1\u201316. https:\/\/doi.org\/10.1109\/jas.2016.7510034","DOI":"10.1109\/jas.2016.7510034"},{"issue":"4","key":"17149_CR21","doi-asserted-by":"publisher","first-page":"897","DOI":"10.1016\/s0888-7543(05)80111-9","volume":"14","author":"K Nakai","year":"1992","unstructured":"Nakai K, Kanehisa M (1992) A knowledge base for predicting protein localization sites in eukaryotic cells. Genomics 14(4):897\u2013911. https:\/\/doi.org\/10.1016\/s0888-7543(05)80111-9","journal-title":"Genomics"},{"issue":"5","key":"17149_CR22","doi-asserted-by":"publisher","first-page":"1422","DOI":"10.1109\/tcbb.2012.63","volume":"9","author":"H Pang","year":"2012","unstructured":"Pang H, George SL, Hui K, Tong T (2012) Gene selection using iterative feature elimination random forests for survival outcomes. IEEE\/ACM Trans Comput Biol Bioinf 9(5):1422\u20131431. https:\/\/doi.org\/10.1109\/tcbb.2012.63","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"issue":"4","key":"17149_CR23","doi-asserted-by":"publisher","first-page":"783","DOI":"10.1007\/s11222-015-9569-2","volume":"26","author":"\u00c9 Perthame","year":"2016","unstructured":"Perthame \u00c9, Friguet C, Causeur D (2016) Stability of feature selection in classification issues for high-dimensional correlated data. Stat Comput 26(4):783\u2013796. https:\/\/doi.org\/10.1007\/s11222-015-9569-2","journal-title":"Stat Comput"},{"issue":"11","key":"17149_CR24","doi-asserted-by":"publisher","first-page":"1921","DOI":"10.1109\/TPAMI.2010.34","volume":"32","author":"P Somol","year":"2010","unstructured":"Somol P, Novovi\u010dov\u00e1 J (2010) Evaluating Stability and Comparing Output of Feature Selectors that Optimize Feature Subset Cardinality. IEEE Trans Pattern Anal Mach Intell 32(11):1921\u20131939. https:\/\/doi.org\/10.1109\/TPAMI.2010.34","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"11","key":"17149_CR25","doi-asserted-by":"publisher","first-page":"1119","DOI":"10.1016\/0167-8655(94)90127-9","volume":"15","author":"P Pudil","year":"1994","unstructured":"Pudil P, Novovi\u010dov\u00e1 J, Kittler J (1994) Floating search methods in feature selection. Pattern Recogn Lett 15(11):1119\u20131125. https:\/\/doi.org\/10.1016\/0167-8655(94)90127-9","journal-title":"Pattern Recogn Lett"},{"issue":"9","key":"17149_CR26","doi-asserted-by":"publisher","first-page":"1890","DOI":"10.1109\/tnnls.2015.2460994","volume":"27","author":"SS Ray","year":"2016","unstructured":"Ray SS, Ganivada A, Pal SK (2016) A granular self-organizing map for clustering and gene selection in Microarray data. IEEE Trans Neural Netw Learn Syst 27(9):1890\u20131906. https:\/\/doi.org\/10.1109\/tnnls.2015.2460994","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"1","key":"17149_CR27","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.ygeno.2016.07.005","volume":"111","author":"J Ruan","year":"2019","unstructured":"Ruan J, Jahid MJ, Gu F, Lei C, Huang Y, Hsu Y, Mutch DG, Chen C, Kirma NB, Huang TH (2019) A novel algorithm for network-based prediction of cancer recurrence. Genomics 111(1):17\u201323. https:\/\/doi.org\/10.1016\/j.ygeno.2016.07.005","journal-title":"Genomics"},{"issue":"6","key":"17149_CR28","doi-asserted-by":"publisher","first-page":"922","DOI":"10.1016\/j.ygeno.2004.08.005","volume":"84","author":"K Tu","year":"2004","unstructured":"Tu K, Yu H, Guo Z, Li X (2004) Learnability-based further prediction of gene functions in gene ontology. Genomics 84(6):922\u2013928. https:\/\/doi.org\/10.1016\/j.ygeno.2004.08.005","journal-title":"Genomics"},{"key":"17149_CR29","unstructured":"Yates (1999) Modern information retrieval. Pearson Education India"},{"issue":"4","key":"17149_CR30","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/j.ygeno.2013.05.006","volume":"102","author":"J Zahiri","year":"2013","unstructured":"Zahiri J, Yaghoubi O, Mohammad-Noori M, Ebrahimpour R, Masoudi-Nejad A (2013) PPIevo\u202f: Protein\u2013protein interaction prediction from PSSM based evolutionary information. Genomics 102(4):237\u2013242. https:\/\/doi.org\/10.1016\/j.ygeno.2013.05.006","journal-title":"Genomics"},{"key":"17149_CR31","doi-asserted-by":"publisher","unstructured":"Srivastava N, Gautam J (2017) Prognosis of disease that may occur with growing age using confabulation based algorithm.\u00a0Def Life Sci J\u00a02(4):399\u2013405. https:\/\/doi.org\/10.14429\/dlsj.2.11029","DOI":"10.14429\/dlsj.2.11029"},{"issue":"5","key":"17149_CR32","doi-asserted-by":"publisher","first-page":"3733","DOI":"10.1007\/s10586-022-03598-z","volume":"25","author":"S Ahmad","year":"2022","unstructured":"Ahmad S, Mehfuz S, Mebarek-Oudina F, Beg J (2022) RSM analysis based cloud access security broker: a systematic literature review. Clust Comput 25(5):3733\u20133763","journal-title":"Clust Comput"},{"issue":"30","key":"17149_CR33","doi-asserted-by":"publisher","first-page":"43837","DOI":"10.1007\/s11042-022-13215-1","volume":"81","author":"MT Nyo","year":"2022","unstructured":"Nyo MT, Mebarek-Oudina F, Hlaing SS, Khan NA (2022) Otsu\u2019s thresholding technique for MRI image brain tumor segmentation. Multimed Tools Appl 81(30):43837\u201343849","journal-title":"Multimed Tools Appl"},{"issue":"3","key":"17149_CR34","first-page":"557","volume":"34","author":"CJJ Sheela","year":"2022","unstructured":"Sheela CJJ, Suganthi G (2022) Automatic brain tumor segmentation from MRI using greedy snake model and fuzzy C-means optimization. J King Saud Univ-Comput Inf Sci 34(3):557\u2013566","journal-title":"J King Saud Univ-Comput Inf Sci"},{"key":"17149_CR35","doi-asserted-by":"crossref","unstructured":"Sucharita S, Sahu B, Swarnkar T, Meher SK (2023) Classification of cancer microarray data using a two-step feature selection framework with moth-flame optimization and extreme learning machine.\u00a0Multimed Tools Appl 1\u201328","DOI":"10.1007\/s11042-023-16353-2"},{"issue":"9","key":"17149_CR36","doi-asserted-by":"publisher","first-page":"13453","DOI":"10.1007\/s11042-022-13964-z","volume":"82","author":"PK Ram","year":"2023","unstructured":"Ram PK, Kuila P (2023) Dynamic scaling factor based differential evolution with multi-layer perceptron for gene selection from pathway information of microarray data. Multimed Tools Appl 82(9):13453\u201313478","journal-title":"Multimed Tools Appl"},{"key":"17149_CR37","doi-asserted-by":"publisher","first-page":"11163","DOI":"10.1007\/s11042-019-7181-8","volume":"79","author":"J Chaki","year":"2020","unstructured":"Chaki J, Dey N (2020) Pattern analysis of genetics and genomics: a survey of the state-of-art. Multimed Tools Appl 79:11163\u201311194","journal-title":"Multimed Tools Appl"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-17149-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-17149-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-17149-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T11:25:02Z","timestamp":1714389902000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-17149-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,17]]},"references-count":37,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["17149"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-17149-0","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,17]]},"assertion":[{"value":"16 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 August 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 September 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 October 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}