{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T05:33:57Z","timestamp":1773725637907,"version":"3.50.1"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,9,22]],"date-time":"2020-09-22T00:00:00Z","timestamp":1600732800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,9,22]],"date-time":"2020-09-22T00:00:00Z","timestamp":1600732800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n<jats:title>Background<\/jats:title>\n<jats:p>Gene selection refers to find a small subset of discriminant genes from the gene expression profiles. How to select genes that affect specific phenotypic traits effectively is an important research work in the field of biology. The neural network has better fitting ability when dealing with nonlinear data, and it can capture features automatically and flexibly. In this work, we propose an embedded gene selection method using neural network. The important genes can be obtained by calculating the weight coefficient after the training is completed. In order to solve the problem of black box of neural network and further make the training results interpretable in neural network, we use the idea of knockoffs to construct the knockoff feature genes of the original feature genes. This method not only make each feature gene to compete with each other, but also make each feature gene compete with its knockoff feature gene. This approach can help to select the key genes that affect the decision-making of neural networks.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Results<\/jats:title>\n<jats:p>We use maize carotenoids, tocopherol methyltransferase, raffinose family oligosaccharides and human breast cancer dataset to do verification and analysis.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Conclusions<\/jats:title>\n<jats:p>The experiment results demonstrate that the knockoffs optimizing neural network method has better detection effect than the other existing algorithms, and specially for processing the nonlinear gene expression and phenotype data.<\/jats:p>\n<\/jats:sec>","DOI":"10.1186\/s12859-020-03717-w","type":"journal-article","created":{"date-parts":[[2020,10,2]],"date-time":"2020-10-02T15:34:28Z","timestamp":1601652868000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["An embedded gene selection method using knockoffs optimizing neural network"],"prefix":"10.1186","volume":"21","author":[{"given":"Juncheng","family":"Guo","sequence":"first","affiliation":[]},{"given":"Min","family":"Jin","sequence":"additional","affiliation":[]},{"given":"Yuanyuan","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Jianxiao","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,22]]},"reference":[{"key":"3717_CR1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2015\/198363","volume":"2015","author":"ZM Hira","year":"2015","unstructured":"Hira ZM, Gillies DF. A review of feature selection and feature extraction methods applied on microarray data. Adv Bioinforma. 2015;2015:1\u201313.","journal-title":"Adv Bioinforma"},{"key":"3717_CR2","first-page":"1","volume":"2017","author":"Q Su","year":"2017","unstructured":"Su Q, Wang Y, Jiang X, et al. A Cancer gene selection algorithm based on the K-S test and CFS. Biomed Res Int. 2017;2017:1\u20136.","journal-title":"Biomed Res Int"},{"issue":"6","key":"3717_CR3","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/j.gpb.2017.08.002","volume":"15","author":"L Gao","year":"2017","unstructured":"Gao L, Ye M, Lu X, et al. Hybrid method based on information gain and support vector machine for gene selection in Cancer classification. Genom Proteomics Bioinformatics. 2017;15(6):389\u201395.","journal-title":"Genom Proteomics Bioinformatics"},{"issue":"4\u20136","key":"3717_CR4","doi-asserted-by":"crossref","first-page":"991","DOI":"10.1016\/j.neucom.2008.04.005","volume":"72","author":"R Cai","year":"2009","unstructured":"Cai R, Hao Z, Yang X, et al. An efficient gene selection algorithm based on mutual information. Neurocomputing. 2009;72(4\u20136):991\u20139.","journal-title":"Neurocomputing."},{"issue":"2","key":"3717_CR5","doi-asserted-by":"crossref","first-page":"410","DOI":"10.1007\/s10015-008-0533-5","volume":"13","author":"MS Mohamad","year":"2009","unstructured":"Mohamad MS, Omatu S, Deris S, et al. A multi-objective strategy in genetic algorithms for gene selection of gene expression data. Artif Life Robot. 2009;13(2):410\u20133.","journal-title":"Artif Life Robot"},{"key":"3717_CR6","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.imu.2017.10.004","volume":"9","author":"H Motieghader","year":"2017","unstructured":"Motieghader H, Najafi A, Sadeghi B, et al. A hybrid gene selection algorithm for microarray cancer classification using genetic algorithm and learning automata. Inform Med Unlocked. 2017;9:246\u201354.","journal-title":"Inform Med Unlocked"},{"key":"3717_CR7","doi-asserted-by":"crossref","first-page":"1024","DOI":"10.1016\/j.neucom.2015.05.022","volume":"168","author":"S Tabakhi","year":"2015","unstructured":"Tabakhi S, Najafi A, Ranjbar R, et al. Gene selection for microarray data classification using a novel ant colony optimization. Neurocomputing. 2015;168:1024\u201336.","journal-title":"Neurocomputing."},{"key":"3717_CR8","first-page":"1","volume":"2015","author":"A Hala","year":"2015","unstructured":"Hala A, Ghada B, Yousef A. mRMR-ABC: a hybrid gene selection algorithm for cancer classification using microarray gene expression profiling. Biomed Res Int. 2015;2015:1\u201315.","journal-title":"Biomed Res Int"},{"key":"3717_CR9","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.neucom.2016.08.089","volume":"218","author":"CM Lai","year":"2016","unstructured":"Lai CM, Yeh WC, Chang CY. Gene selection using information gain and improved simplified swarm optimization. Neurocomputing. 2016;218:331\u20138.","journal-title":"Neurocomputing."},{"issue":"3","key":"3717_CR10","doi-asserted-by":"crossref","first-page":"184","DOI":"10.18178\/ijmlc.2016.6.3.596","volume":"6","author":"A Hala","year":"2016","unstructured":"Hala A, Ghada B, Yousef A. ABC-AVM: artificial bee colony and svm method for microarray gene selection and multi class cancer classification. Int J Machine Learn Comput. 2016;6(3):184\u201390.","journal-title":"Int J Machine Learn Comput"},{"issue":"6","key":"3717_CR11","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.ygeno.2016.05.001","volume":"107","author":"FV Sharbaf","year":"2016","unstructured":"Sharbaf FV, Mosafer S, Moattar MH. A hybrid gene selection approach for microarray data classification using cellular learning automata and ant colony optimization. Genomics. 2016;107(6):231\u20138.","journal-title":"Genomics"},{"issue":"2","key":"3717_CR12","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.ygeno.2017.01.004","volume":"109","author":"M Dashtban","year":"2017","unstructured":"Dashtban M, Balafar M. Gene selection for microarray cancer classification using a new evolutionary method employing artificial intelligence concepts. Genomics. 2017;109(2):91\u2013107.","journal-title":"Genomics."},{"key":"3717_CR13","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.eswa.2018.06.057","volume":"116","author":"M Ghosh","year":"2019","unstructured":"Ghosh M, Begum S, Sarkar R, et al. Recursive Memetic Algorithm for gene selection in microarray data. Expert Syst Appl. 2019;116:172\u201385.","journal-title":"Expert Syst Appl"},{"issue":"3","key":"3717_CR14","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1007\/s10489-017-0992-2","volume":"48","author":"X Huang","year":"2018","unstructured":"Huang X, Zhang L, Wang B, et al. Feature clustering based support vector machine recursive feature elimination for gene selection. Appl Intell. 2018;48(3):594\u2013607.","journal-title":"Appl Intell"},{"key":"3717_CR15","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.compbiomed.2016.12.002","volume":"81","author":"A Wang","year":"2017","unstructured":"Wang A, An N, Yang J, et al. Wrapper-based gene selection with Markov blanket. Comput Biol Med. 2017;81:11\u201323.","journal-title":"Comput Biol Med"},{"issue":"1","key":"3717_CR16","first-page":"25","volume":"12","author":"I Inza","year":"2002","unstructured":"Inza I, Sierra B, Blanco R, et al. Gene selection by sequential search wrapper approaches in microarray cancer class prediction. J Int Fuzzy Syst. 2002;12(1):25\u201333.","journal-title":"J Int Fuzzy Syst"},{"issue":"1","key":"3717_CR17","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/1471-2105-15-8","volume":"15","author":"MB Kursa","year":"2014","unstructured":"Kursa MB. Robustness of random Forest-based gene selection methods. BMC Bioinformatics. 2014;15(1):8\u20138.","journal-title":"BMC Bioinformatics"},{"key":"3717_CR18","doi-asserted-by":"crossref","unstructured":"Breiman LI , Friedman JH , Olshen RA , et al. Classification and regression trees. Biometrics. 1984;40(3):342\u20136.","DOI":"10.2307\/2530946"},{"issue":"23","key":"3717_CR19","doi-asserted-by":"crossref","first-page":"9326","DOI":"10.1016\/j.eswa.2015.08.016","volume":"42","author":"ZY Algamal","year":"2015","unstructured":"Algamal ZY, Lee MH. Penalized logistic regression with the adaptive LASSO for gene selection in high-dimensional cancer classification. Expert Syst Appl. 2015;42(23):9326\u201332.","journal-title":"Expert Syst Appl"},{"key":"3717_CR20","first-page":"1","volume-title":"Using the LASSO for gene selection in bladder cancer data. Proceedings of CIBB","author":"S Chretien","year":"2015","unstructured":"Chretien S, Guyeux C, Boyerguittaut M, et al. Using the LASSO for gene selection in bladder cancer data. Proceedings of CIBB; 2015. p. 1\u20136."},{"issue":"2","key":"3717_CR21","first-page":"1","volume":"6","author":"JO Ogutu","year":"2012","unstructured":"Ogutu JO, Schulzstreeck T, Piepho H, et al. Genomic selection using regularized linear regression models: ridge regression, lasso, elastic net and their extensions. BMC Proc. 2012;6(2):1\u20136.","journal-title":"BMC Proc"},{"key":"3717_CR22","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.compbiomed.2015.10.008","volume":"67","author":"ZY Algamal","year":"2015","unstructured":"Algamal ZY, Lee MH. Regularized logistic regression with adjusted adaptive elastic net for gene selection in high dimensional cancer classification. Comput Biol Med. 2015;67:136\u201345.","journal-title":"Comput Biol Med"},{"key":"3717_CR23","first-page":"1","volume-title":"DeepPINK: reproducible feature selection in deep neural networks. The 32nd Conference on Neural Information Processing Systems","author":"YY Lu","year":"2018","unstructured":"Lu YY, Fan Y, Lv J, et al. DeepPINK: reproducible feature selection in deep neural networks. The 32nd Conference on Neural Information Processing Systems; 2018. p. 1\u201311."},{"issue":"3","key":"3717_CR24","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1111\/rssb.12265","volume":"80","author":"E Cand\u00e8s","year":"2018","unstructured":"Cand\u00e8s E, Fan Y, Janson L, et al. Panning for gold: \u2018model-X\u2019 knockoffs for high dimensional controlled variable selection. J R Stat Soc. 2018;80(3):551\u201377.","journal-title":"J R Stat Soc"},{"key":"3717_CR25","doi-asserted-by":"crossref","first-page":"2832","DOI":"10.1038\/ncomms3832","volume":"4","author":"JJ Fu","year":"2013","unstructured":"Fu JJ, Chen YB, Linghu JJ, et al. RNA sequencing reveals the complex regulatory network in the maize kernel. Nat Commun. 2013;4:2832.","journal-title":"Nat Commun"},{"key":"3717_CR26","first-page":"baw117","volume":"2016","author":"HJ Liu","year":"2016","unstructured":"Liu HJ, Wang F, Xiao YJ, et al. MODEM: Multi-omics data envelopment and mining in maize. Database. J Biol Datab Curation. 2016;2016:baw117.","journal-title":"J Biol Datab Curation"},{"issue":"4","key":"3717_CR27","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1038\/ng.551","volume":"42","author":"J Yan","year":"2010","unstructured":"Yan J, Kandianis CB, Harjes CE, et al. Rare genetic variation at Zea mays crtRB1 increases beta-carotene in maize grain. Nat Genet. 2010;42(4):322\u20137.","journal-title":"Nat Genet"},{"issue":"2","key":"3717_CR28","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1007\/s00122-012-1987-3","volume":"126","author":"R Babu","year":"2013","unstructured":"Babu R, Rojas NP, Gao S, et al. Validation of the effects of molecular marker polymorphisms in LcyE and CrtRB1 on provitamin a concentrations for 26 tropical maize populations. Theor Appl Genet. 2013;126(2):389\u201399.","journal-title":"Theor Appl Genet"},{"key":"3717_CR29","doi-asserted-by":"crossref","first-page":"1464","DOI":"10.1111\/pbi.12889","volume":"16","author":"H Wang","year":"2018","unstructured":"Wang H, Xu S, Fan Y, et al. Beyond pathways: genetic dissection of tocopherol content in maize kernels by combining linkage and association analyses. Plant Biotechnol J. 2018;16:1464\u201375.","journal-title":"Plant Biotechnol J"},{"issue":"12","key":"3717_CR30","doi-asserted-by":"crossref","first-page":"1540","DOI":"10.1016\/j.molp.2017.10.014","volume":"10","author":"T Li","year":"2017","unstructured":"Li T, Zhang Y, Wang D, et al. Regulation of seed vigor by manipulation of raffinose family oligosaccharides in maize and arabidopsis thaliana. Mol Plant. 2017;10(12):1540\u201355.","journal-title":"Mol Plant"},{"issue":"5861","key":"3717_CR31","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1126\/science.1150255","volume":"319","author":"CE Harjes","year":"2008","unstructured":"Harjes CE, Rocheford TR, Bai L, et al. Natural genetic variation in lycopene epsilon cyclase tapped for maize biofortification. Science. 2008;319(5861):330\u20133.","journal-title":"Science."},{"issue":"2","key":"3717_CR32","volume":"7","author":"F Guo","year":"2012","unstructured":"Guo F, Zhou W, Zhang J, et al. Effect of the citrus lycopene \u03b2-Cyclase transgene on carotenoid metabolism in transgenic tomato fruits. PLoS One. 2012;7(2):e32221.","journal-title":"PLoS One"},{"issue":"9","key":"3717_CR33","doi-asserted-by":"crossref","first-page":"2246","DOI":"10.3390\/ijms20092246","volume":"20","author":"CC Jiang","year":"2019","unstructured":"Jiang CC, Zhang YF, Lin YJ, et al. Illumina((R)) sequencing reveals candidate genes of carotenoid metabolism in three pummelo cultivars (citrus maxima) with different pulp color. Int J Mol Sci. 2019;20(9):2246.","journal-title":"Int J Mol Sci"},{"issue":"5","key":"3717_CR34","volume":"7","author":"Q Li","year":"2012","unstructured":"Li Q, Yang X, Xu S, et al. Genome-wide association studies identified three independent polymorphisms associated with \u03b1-tocopherol content in maize kernels. PLoS One. 2012;7(5):e36807.","journal-title":"PLoS One"},{"key":"3717_CR35","first-page":"53","volume-title":"Causation and Prediction Challenge","author":"YW Chang","year":"2008","unstructured":"Chang YW, Lin CJ. Feature ranking using linear SVM. In: Causation and Prediction Challenge; 2008. p. 53\u201364."},{"issue":"2","key":"3717_CR36","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1109\/TNN.2008.2005601","volume":"20","author":"PA Est\u00e9vez","year":"2009","unstructured":"Est\u00e9vez PA, Tesmer M, Perez CA, et al. Normalized mutual information feature selection. IEEE Trans Neural Netw. 2009;20(2):189\u2013201.","journal-title":"IEEE Trans Neural Netw"},{"issue":"5","key":"3717_CR37","doi-asserted-by":"crossref","first-page":"2055","DOI":"10.1214\/15-AOS1337","volume":"43","author":"RF Barber","year":"2015","unstructured":"Barber RF, Cand\u00e8s EJ. Controlling the false discovery rate via knockoffs. Ann Stat. 2015;43(5):2055\u201385.","journal-title":"Ann Stat"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-020-03717-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-020-03717-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-020-03717-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,21]],"date-time":"2021-09-21T23:11:53Z","timestamp":1632265913000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-020-03717-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,22]]},"references-count":37,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["3717"],"URL":"https:\/\/doi.org\/10.1186\/s12859-020-03717-w","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,22]]},"assertion":[{"value":"3 April 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 August 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 September 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Not applicable.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing financial interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"414"}}