{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T06:36:45Z","timestamp":1772087805812,"version":"3.50.1"},"reference-count":69,"publisher":"Walter de Gruyter GmbH","issue":"2","license":[{"start":{"date-parts":[[2020,12,29]],"date-time":"2020-12-29T00:00:00Z","timestamp":1609200000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,6,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Breast cancer is the leading diseases of death in women. It induces by a genetic mutation in breast cancer cells. Genetic testing has become popular to detect the mutation in genes but test cost is relatively expensive for several patients in developing countries like India. Genetic test takes between 2 and 4\u00a0weeks to decide the cancer. The time duration suffers the prognosis of genes because some patients have high rate of cancerous cell growth. In the research work, a cost and time efficient method is proposed to predict the gene expression level on the basis of clinical outcomes of the patient by using machine learning techniques. An improved SVM-RFE_MI gene selection technique is proposed to find the most significant genes related to breast cancer afterward explained variance statistical analysis is applied to extract the genes contain high variance. Least Absolute Shrinkage Selector Operator (LASSO) and Ridge regression techniques are used to predict the gene expression level. The proposed method predicts the expression of significant genes with reduced Root Mean Square Error and acceptable adjusted R-square value. As per the study, analysis of these selected genes is beneficial to diagnose the breast cancer at prior stage in reduced cost and time.<\/jats:p>","DOI":"10.1515\/jib-2019-0110","type":"journal-article","created":{"date-parts":[[2021,1,13]],"date-time":"2021-01-13T05:06:04Z","timestamp":1610514364000},"page":"139-153","source":"Crossref","is-referenced-by-count":11,"title":["A novel gene expression test method of minimizing breast cancer risk in reduced cost and time by improving SVM-RFE gene selection method combined with LASSO"],"prefix":"10.1515","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4024-3467","authenticated-orcid":false,"given":"Madhuri","family":"Gupta","sequence":"first","affiliation":[{"name":"Department of Computer Engineering and Information Technology , ABES Engineering College , Ghaziabad , Uttar Pradesh, India"}]},{"given":"Bharat","family":"Gupta","sequence":"additional","affiliation":[{"name":"Department of CS&IT , Jaypee Institute of Information Technology , Noida , Uttar Pradesh, India"}]}],"member":"374","published-online":{"date-parts":[[2020,12,29]]},"reference":[{"key":"2023033120073840473_j_jib-2019-0110_ref_001","doi-asserted-by":"crossref","unstructured":"Rojas, K, Stuckey, A. Breast cancer epidemiology and risk factors. Clin Obstet Gynaecol 2016;59:651\u201372. https:\/\/doi.org\/10.1097\/grf.0000000000000239.","DOI":"10.1097\/GRF.0000000000000239"},{"key":"2023033120073840473_j_jib-2019-0110_ref_002","unstructured":"Globocan Project. Available from: http:\/\/www.breastcancerindia.net\/statistics\/stat_global.html [Accessed 5 Mar 2019]."},{"key":"2023033120073840473_j_jib-2019-0110_ref_003","doi-asserted-by":"crossref","unstructured":"Feng, RM, Zong, YN, Cao, SM, Xu, RH. Current cancer situation in China: good or bad news from the 2018 Global Cancer Statistics? Canc Commun 2019;39:22. https:\/\/doi.org\/10.1186\/s40880-019-0368-6.","DOI":"10.1186\/s40880-019-0368-6"},{"key":"2023033120073840473_j_jib-2019-0110_ref_004","unstructured":"Indian Breast Cancer Statistics; 2018. 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Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Canc 2015;136:E359\u201386. https:\/\/doi.org\/10.1002\/ijc.29210.","DOI":"10.1002\/ijc.29210"},{"key":"2023033120073840473_j_jib-2019-0110_ref_009","doi-asserted-by":"crossref","unstructured":"Kapoor, NS, Banks, KC. Should multi-gene panel testing replace limited BRCA1\/2 testing? A review of genetic testing for hereditary breast and ovarian cancers. World J Surg Proced 2016;6:13\u20138. https:\/\/doi.org\/10.5412\/wjsp.v6.i1.13.","DOI":"10.5412\/wjsp.v6.i1.13"},{"key":"2023033120073840473_j_jib-2019-0110_ref_010","unstructured":"Genetic Test Cost and Time; 2019. Available from: https:\/\/ghr.nlm.nih.gov\/primer\/testing\/costresults [Accessed 10 Jun 2019]."},{"key":"2023033120073840473_j_jib-2019-0110_ref_011","unstructured":"Rajiv, S. The cost of genetic testing for cancer has to come down; 2018. 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Asian Pac J Canc Prev APJCP 2019;20:529. https:\/\/doi.org\/10.31557\/apjcp.2019.20.2.529.","DOI":"10.31557\/APJCP.2019.20.2.529"},{"key":"2023033120073840473_j_jib-2019-0110_ref_063","doi-asserted-by":"crossref","unstructured":"Karabulut, S, Kaya, Z, Amuran, GG, Peker, I, \u00d6zmen, T, G\u016bll\u016bo\u1e21lu, BM, et al.. Correlation between the DNA methylation and gene expression of IGFBP5 in breast cancer. Breast Dis 2016;36:123\u201331. https:\/\/doi.org\/10.3233\/bd-160234.","DOI":"10.3233\/BD-160234"},{"key":"2023033120073840473_j_jib-2019-0110_ref_064","doi-asserted-by":"crossref","unstructured":"Bhushann Meka, P, Jarjapu, S, Vishwakarma, SK, Nanchari, SR, Cingeetham, A, Annamaneni, S, et al.. Influence of BCL2-938 C> A promoter polymorphism and BCL2 gene expression on the progression of breast cancer. Tumor Biol 2016;37:6905\u201312. https:\/\/doi.org\/10.1007\/s13277-015-4554-0.","DOI":"10.1007\/s13277-015-4554-0"},{"key":"2023033120073840473_j_jib-2019-0110_ref_065","doi-asserted-by":"crossref","unstructured":"Guleria, K, Sambyal, V, Kapahi, R, Manjari, M, Sudan, M, Uppal, MS, et al.. 43Role of functional polymorphisms of VEGF and risk of breast cancer in north-western Indians: a case-control study. Ann Oncol 2017;28(7 Suppl). https:\/\/doi.org\/10.1093\/annonc\/mdx511.009.","DOI":"10.1093\/annonc\/mdx511.009"},{"key":"2023033120073840473_j_jib-2019-0110_ref_066","doi-asserted-by":"crossref","unstructured":"Putluri, N, Maity, S, Kommagani, R, Creighton, CJ, Putluri, V, Chen, F, et al.. Pathway-centric integrative analysis identifies RRM2 as a prognostic marker in breast cancer associated with poor survival and tamoxifen resistance. Neoplasia 2014;16:390\u2013402. https:\/\/doi.org\/10.1016\/j.neo.2014.05.007.","DOI":"10.1016\/j.neo.2014.05.007"},{"key":"2023033120073840473_j_jib-2019-0110_ref_067","unstructured":"Cancer Genetics web. List of gene related to breast cancer; 2017. Available from: http:\/\/www.cancerindex.org\/geneweb\/X0401.htm [Accessed 10 Nov 2018]."},{"key":"2023033120073840473_j_jib-2019-0110_ref_068","doi-asserted-by":"crossref","unstructured":"Kim, KY, Park, J, Sohmshetty, R. Prediction measurement with mean acceptable error for proper inconsistency in noisy weldability prediction data. Robot Comput Integr Manuf 2017;43:18\u201329. https:\/\/doi.org\/10.1016\/j.rcim.2016.01.002.","DOI":"10.1016\/j.rcim.2016.01.002"},{"key":"2023033120073840473_j_jib-2019-0110_ref_069","unstructured":"Veerasamy, R, Rajak, H, Jain, A, Sivadasan, S, Varghese, CP, Agrawal, RK. Validation of QSAR models-strategies and importance. 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