{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T04:43:34Z","timestamp":1769316214883,"version":"3.49.0"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,8,28]],"date-time":"2019-08-28T00:00:00Z","timestamp":1566950400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2019,8,28]],"date-time":"2019-08-28T00:00:00Z","timestamp":1566950400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2019,12]]},"DOI":"10.1186\/s12859-019-3022-z","type":"journal-article","created":{"date-parts":[[2019,8,28]],"date-time":"2019-08-28T04:17:03Z","timestamp":1566965823000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Epi-GTBN: an approach of epistasis mining based on genetic Tabu algorithm and Bayesian network"],"prefix":"10.1186","volume":"20","author":[{"given":"Yang","family":"Guo","sequence":"first","affiliation":[]},{"given":"Zhiman","family":"Zhong","sequence":"additional","affiliation":[]},{"given":"Chen","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Jiangfeng","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Yaling","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Zizhen","family":"Liang","sequence":"additional","affiliation":[]},{"given":"Hui","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Jianxiao","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,8,28]]},"reference":[{"issue":"1","key":"3022_CR1","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1093\/biostatistics\/kxm010","volume":"9","author":"MY Park","year":"2008","unstructured":"Park MY, Hastie T. Penalized logistic regression for detecting gene interactions. Biostatistics. 2008;9(1):30\u201350.","journal-title":"Biostatistics."},{"issue":"1","key":"3022_CR2","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1186\/s12859-017-1488-0","volume":"18","author":"V Stanislas","year":"2017","unstructured":"Stanislas V, Dalmasso C, Ambroise C. Eigen-epistasis for detecting gene-gene interactions. BMC Bioinformatics. 2017;18(1):54.","journal-title":"BMC Bioinformatics"},{"issue":"4","key":"3022_CR3","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pgen.1005965","volume":"12","author":"F Zhang","year":"2016","unstructured":"Zhang F, Xie D, Liang M, et al. Functional regression models for epistasis analysis of multiple quantitative traits. PLoS Genet. 2016;12(4):e1005965.","journal-title":"PLoS Genet"},{"issue":"1","key":"3022_CR4","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1186\/s13040-015-0077-x","volume":"8","author":"R De","year":"2015","unstructured":"De R, Hu T, Moore JH, et al. Characterizing gene-gene interactions in a statistical epistasis network of twelve candidate genes for obesity. Biodata Mining. 2015;8(1):45.","journal-title":"Biodata Mining"},{"key":"3022_CR5","doi-asserted-by":"crossref","unstructured":"Zhang X, Zou F, Wang W. FastANOVA: an efficient algorithm for genome-wide association study. Int Confer Knowl Discov Data Mining. 2008;821.","DOI":"10.1145\/1401890.1401988"},{"issue":"1","key":"3022_CR6","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1086\/321276","volume":"69","author":"MD Ritchie","year":"2001","unstructured":"Ritchie MD, Hahn LW, Roodi N, et al. Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am J Hum Genet. 2001;69(1):138\u201347.","journal-title":"Am J Hum Genet"},{"issue":"17","key":"3022_CR7","doi-asserted-by":"publisher","first-page":"i605","DOI":"10.1093\/bioinformatics\/btw424","volume":"32","author":"W Yu","year":"2016","unstructured":"Yu W, Lee S, Park T. A unified model based multifactor dimensionality reduction framework for detecting gene-gene interactions. Bioinformatics. 2016;32(17):i605.","journal-title":"Bioinformatics."},{"issue":"15","key":"3022_CR8","doi-asserted-by":"publisher","first-page":"2354","DOI":"10.1093\/bioinformatics\/btx163","volume":"33","author":"CH Yang","year":"2017","unstructured":"Yang CH, Chuang LY, Lin YD. CMDR based differential evolution identify the epistatic interaction in genome-wide association studies. Bioinformatics. 2017;33(15):2354.","journal-title":"Bioinformatics."},{"issue":"9","key":"3022_CR9","doi-asserted-by":"publisher","first-page":"1167","DOI":"10.1038\/ng2110","volume":"39","author":"Y Zhang","year":"2007","unstructured":"Zhang Y, Liu JS. Bayesian inference of epistatic interactions in case-control studies. Nat Genet. 2007;39(9):1167\u201373.","journal-title":"Nat Genet"},{"issue":"2","key":"3022_CR10","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1093\/bioinformatics\/btv504","volume":"32","author":"R Colak","year":"2016","unstructured":"Colak R, Kim TH, Kazan H, et al. JBASE: joint Bayesian analysis of subphenotypes and epistasis. Bioinformatics. 2016;32(2):203.","journal-title":"Bioinformatics."},{"key":"3022_CR11","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1038\/sj.ejhg.5201921","volume":"16","author":"CZ Dong","year":"2008","unstructured":"Dong CZ, Chu X, Wang Y, et al. Exploration of gene-gene interaction effects using entropy-based methods. Eur J Hum Genet. 2008;16:229\u201335.","journal-title":"Eur J Hum Genet"},{"issue":"4","key":"3022_CR12","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1136\/amiajnl-2012-001525","volume":"20","author":"T Hu","year":"2013","unstructured":"Hu T, Chen Y, Kiralis JW, et al. An information-gain approach to detecting three-way epistatic interactions in genetic association studies. J Am Med Inform Assoc Jamia. 2013;20(4):630.","journal-title":"J Am Med Inform Assoc Jamia"},{"issue":"Suppl 1","key":"3022_CR13","first-page":"S6","volume":"7","author":"MS Kwon","year":"2014","unstructured":"Kwon MS, Park M, Park T. IGENT: efficient entropy based algorithm for genome-wide gene-gene interaction analysis. BMC Med Genet. 2014;7(Suppl 1):S6.","journal-title":"BMC Med Genet"},{"issue":"18","key":"3022_CR14","doi-asserted-by":"publisher","first-page":"2829","DOI":"10.1093\/bioinformatics\/btx339","volume":"33","author":"Xiong Li","year":"2017","unstructured":"Li X. A fast and exhaustive method for heterogeneity and epistasis analysis based on multi-objective optimization. Bioinformatics. 2017;33(18):2829\u201336.","journal-title":"Bioinformatics"},{"issue":"1","key":"3022_CR15","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1186\/1471-2105-12-89","volume":"12","author":"X Jiang","year":"2011","unstructured":"Jiang X, Neapolitan RE, Barmada MM, et al. Learning genetic epistasis using Bayesian network scoring criteria. BMC Bioinformatics. 2011;12(1):89.","journal-title":"BMC Bioinformatics"},{"issue":"1","key":"3022_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-016-1084-8","volume":"17","author":"Z Zeng","year":"2016","unstructured":"Zeng Z, Jiang X, Richard N. Discovering causal interactions using Bayesian network scoring and information gain. BMC Bioinformatics. 2016;17(1):1\u201314.","journal-title":"BMC Bioinformatics"},{"issue":"4","key":"3022_CR17","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1093\/bioinformatics\/btn652","volume":"25","author":"C Yang","year":"2009","unstructured":"Yang C, He ZX, Yang Q, et al. SNPHarvester: a filtering-based approach for detecting epistatic interactions in genome-wide association studies. Bioinformatics. 2009;25(4):504\u201311.","journal-title":"Bioinformatics."},{"issue":"1","key":"3022_CR18","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1093\/bioinformatics\/btp622","volume":"26","author":"X Wan","year":"2010","unstructured":"Wan X, Yang C, Yang Q, et al. Predictive rule inference for epistatic interaction detection in genome-wide association studies. Bioinformatics. 2010;26(1):30\u20137.","journal-title":"Bioinformatics."},{"issue":"3","key":"3022_CR19","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1002\/gepi.21889","volume":"39","author":"X Jiang","year":"2015","unstructured":"Jiang X, Neapolitan RE. LEAP: biomarker inference through learning and evaluating association patterns. Genet Epidemiol. 2015;39(3):173.","journal-title":"Genet Epidemiol"},{"issue":"12","key":"3022_CR20","doi-asserted-by":"publisher","first-page":"i19","DOI":"10.1093\/bioinformatics\/btu261","volume":"30","author":"Y Arkin","year":"2014","unstructured":"Arkin Y, Rahmani E, Kleber ME, et al. EPIQ-efficient detection of SNP-SNP epistatic interactions for quantitative traits. Bioinformatics. 2014;30(12):i19.","journal-title":"Bioinformatics."},{"issue":"3","key":"3022_CR21","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1016\/j.ajhg.2010.07.021","volume":"87","author":"X Wan","year":"2010","unstructured":"Wan X, Yang C, Yang Q, et al. BOOST: a fast approach to detecting gene-gene interactions in genome-wide case-control studies. Am J Hum Genet. 2010;87(3):325.","journal-title":"Am J Hum Genet"},{"issue":"1","key":"3022_CR22","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1186\/s13040-016-0093-5","volume":"9","author":"J Li","year":"2016","unstructured":"Li J, Malley JD, Andrew AS, et al. Detecting gene-gene interactions using a permutation-based random forest method. Biodata Mining. 2016;9(1):14.","journal-title":"Biodata Mining"},{"issue":"2","key":"3022_CR23","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1002\/gepi.20272","volume":"32","author":"SH Chen","year":"2008","unstructured":"Chen SH, Sun J, Dimitrov L, et al. A support vector machine approach for detecting gene-gene interaction. Genet Epidemiol. 2008;32(2):152.","journal-title":"Genet Epidemiol"},{"issue":"6","key":"3022_CR24","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1003627","volume":"10","author":"Q Zhang","year":"2014","unstructured":"Zhang Q, Long Q, Ott J, et al. AprioriGWAS, a new pattern mining strategy for detecting genetic variants associated with disease through interaction effects. PLoS Comput Biol. 2014;10(6):e1003627.","journal-title":"PLoS Comput Biol"},{"issue":"2","key":"3022_CR25","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1109\/TCBB.2013.27","volume":"10","author":"CH Yang","year":"2013","unstructured":"Yang CH, Lin YD, Chuang LY, et al. Evaluation of breast cancer susceptibility using improved genetic algorithms to generate genotype SNP barcodes. IEEE\/ACM Trans Comput Biol Bioinform. 2013;10(2):361.","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"3022_CR26","first-page":"524821","volume":"2015","author":"J Shang","year":"2015","unstructured":"Shang J, Sun Y, Li S, et al. An improved opposition-based learning particle swarm optimization for the detection of SNP-SNP interactions. Biomed Res Int. 2015;2015:524821.","journal-title":"Biomed Res Int"},{"issue":"1","key":"3022_CR27","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1186\/1756-0500-3-117","volume":"3","author":"Y Wang","year":"2010","unstructured":"Wang Y, Liu X, Robbins K, et al. AntEpiSeeker: detecting epistatic interactions for case-control studies using a two-stage ant colony optimization algorithm. BMC Res Notes. 2010;3(1):117.","journal-title":"BMC Res Notes"},{"issue":"5","key":"3022_CR28","doi-asserted-by":"publisher","first-page":"634","DOI":"10.1093\/bioinformatics\/btu702","volume":"31","author":"PJ Jing","year":"2015","unstructured":"Jing PJ, Shen HB. MACOED: a multi-objective ant colony optimization algorithm for SNP epistasis detection in genome-wide association studies. Bioinformatics. 2015;31(5):634\u201341.","journal-title":"Bioinformatics."},{"issue":"1","key":"3022_CR29","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1186\/s13040-017-0143-7","volume":"10","author":"Y Sun","year":"2017","unstructured":"Sun Y, Shang J, Liu JX, et al. epiACO-a method for identifying epistasis based on ant Colony optimization algorithm. Biodata Mining. 2017;10(1):23.","journal-title":"Biodata Mining"},{"issue":"1","key":"3022_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2017\/5024867","volume":"2017","author":"L Yuan","year":"2017","unstructured":"Yuan L, Yuan CA, Huang DS. FAACOSE: a fast adaptive ant colony optimization algorithm for detecting SNP epistasis. Complexity. 2017;2017(1):1\u201310.","journal-title":"Complexity."},{"key":"3022_CR31","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1155\/2015\/639367","volume":"2015","author":"FF Sherif","year":"2015","unstructured":"Sherif FF, Zayed N, Fakhr M. Discovering Alzheimer genetic biomarkers using Bayesian networks. Adv Bioinforma. 2015;2015:8.","journal-title":"Adv Bioinforma"},{"issue":"1","key":"3022_CR32","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1186\/s13637-016-0046-9","volume":"2016","author":"Y Jin","year":"2016","unstructured":"Jin Y, Su Y, Zhou XH, et al. Heterogeneous multimodal biomarkers analysis for Alzheimer\u2019s disease via Bayesian network. Eurasip J Bioinform Syst Biol. 2016;2016(1):12.","journal-title":"Eurasip J Bioinform Syst Biol"},{"issue":"1","key":"3022_CR33","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/0305-0548(93)E0023-M","volume":"22","author":"F Glover","year":"1995","unstructured":"Glover F, Kelly JP, Laguna M. Genetic algorithms and tabu search: hybrids for optimization. Comput Oper Res. 1995;22(1):111\u201334.","journal-title":"Comput Oper Res"},{"issue":"2","key":"3022_CR34","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/j.epsr.2004.01.016","volume":"71","author":"DJ Shin","year":"2004","unstructured":"Shin DJ, Kim JO, Kim TK, et al. Optimal service restoration and reconfiguration of network using genetic-Tabu algorithm. Electr Pow Syst Res. 2004;71(2):145\u201352.","journal-title":"Electr Pow Syst Res"},{"issue":"3","key":"3022_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v035.i03","volume":"35","author":"M Scutari","year":"2010","unstructured":"Scutari M. Learning Bayesian networks with the bnlearn R package. J Stat Softw. 2010;35(3):1\u201322.","journal-title":"J Stat Softw"},{"issue":"1","key":"3022_CR36","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1186\/1756-0381-5-16","volume":"5","author":"RJ Urbanowicz","year":"2012","unstructured":"Urbanowicz RJ, Kiralis J, Sinnott-Armstrong NA, et al. GAMETES: a fast, direct algorithm for generating pure, strict, epistatic models with random architectures. Biodata Mining. 2012;5(1):16.","journal-title":"Biodata Mining"},{"key":"3022_CR37","unstructured":"Dejong K. An analysis of the behavior of a class of genetic adaptive systems. Ann Arbor: Ph. D. Thesis, University of Michigan; 1975."},{"key":"3022_CR38","unstructured":"Schaffer JD, Caruana R, Eshelman LJ, et al. A study of control parameters affecting online performance of genetic algorithms for function optimization. International Conference on Genetic Algorithms. San Francisco: Morgan Kaufmann Publishers Inc; 1989. p. 51\u201360."},{"issue":"5720","key":"3022_CR39","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1126\/science.1109557","volume":"308","author":"RJ Klein","year":"2005","unstructured":"Klein RJ, Zeiss C, Chew EY, et al. Complement factor H polymorphism in age-related macular degeneration. Science. 2005;308(5720):385\u20139.","journal-title":"Science."},{"issue":"1","key":"3022_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.dsp.2013.08.007","volume":"24","author":"J Shang","year":"2014","unstructured":"Shang J, Zhang J, Sun Y, et al. EpiMiner: a three-stage co-information based method for detecting and visualizing epistatic interactions. Digital Signal Process. 2014;24(1):1\u201313.","journal-title":"Digital Signal Process"},{"issue":"3","key":"3022_CR41","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0150669","volume":"11","author":"SH Tuo","year":"2016","unstructured":"Tuo SH, Zhang J, Yuan XG, et al. FHSA-SED: two-locus model detection for genome-wide association study with harmony search algorithm. PLoS One. 2016;11(3):e0150669.","journal-title":"PLoS One"},{"issue":"s1","key":"3022_CR42","doi-asserted-by":"publisher","first-page":"0","DOI":"10.1186\/1471-2105-10-S1-S65","volume":"10","author":"R Jiang","year":"2009","unstructured":"Jiang R, Tang W, Wu X, et al. A random forest approach to the detection of epistatic interactions in case-control studies. BMC Bioinformatics. 2009;10(s1):0.","journal-title":"BMC Bioinformatics"},{"issue":"5","key":"3022_CR43","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pgen.1000464","volume":"5","author":"W Tang","year":"2009","unstructured":"Tang W, Wu X, Jiang R, et al. Epistatic module detection for case-control studies: a Bayesian model with a Gibbs sampling strategy. PLoS Genet. 2009;5(5):e1000464.","journal-title":"PLoS Genet"},{"issue":"3","key":"3022_CR44","first-page":"1","volume":"6","author":"B Han","year":"2012","unstructured":"Han B, Chen X, Talebizadeh Z, et al. Genetic studies of complex human diseases: characterizing SNP-disease associations using Bayesian networks. BMC Syst Biol. 2012;6(3):1\u201312.","journal-title":"BMC Syst Biol"},{"issue":"1","key":"3022_CR45","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1186\/s13040-016-0094-4","volume":"9","author":"R Li","year":"2016","unstructured":"Li R, Dudek SM, Kim D, et al. Identification of genetic interaction networks via an evolutionary algorithm evolved Bayesian network. BioData Mining. 2016;9(1):18.","journal-title":"BioData Mining"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-019-3022-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s12859-019-3022-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-019-3022-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,19]],"date-time":"2023-09-19T13:44:59Z","timestamp":1695131099000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-019-3022-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,8,28]]},"references-count":45,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["3022"],"URL":"https:\/\/doi.org\/10.1186\/s12859-019-3022-z","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,8,28]]},"assertion":[{"value":"4 October 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 August 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 August 2019","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 that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"444"}}