{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T20:27:50Z","timestamp":1784147270774,"version":"3.55.0"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"S7","license":[{"start":{"date-parts":[[2018,12,1]],"date-time":"2018-12-01T00:00:00Z","timestamp":1543622400000},"content-version":"tdm","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 Syst Biol"],"published-print":{"date-parts":[[2018,12]]},"DOI":"10.1186\/s12918-018-0635-1","type":"journal-article","created":{"date-parts":[[2018,12,14]],"date-time":"2018-12-14T09:21:43Z","timestamp":1544779303000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["MICRAT: a novel algorithm for inferring gene regulatory networks using time series gene expression data"],"prefix":"10.1186","volume":"12","author":[{"given":"Bei","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaohui","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Maxwell","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wonryull","family":"Koh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ping","family":"Gong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaoyang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2018,12,14]]},"reference":[{"issue":"1","key":"635_CR1","doi-asserted-by":"publisher","first-page":"223","DOI":"10.4236\/jbise.2013.62A027","volume":"06","author":"N Vijesh","year":"2013","unstructured":"Vijesh N, Chakrabarti SK, Sreekumar J. Modeling of gene regulatory networks: a review. J Biomed Sci Eng. 2013;06(1):223\u201331. http:\/\/file.scirp.org\/pdf\/JBiSE_2013022716483315.pdf . Accessed 03 July 2018.","journal-title":"J Biomed Sci Eng"},{"issue":"5","key":"635_CR2","doi-asserted-by":"publisher","first-page":"R37","DOI":"10.1186\/gb-2006-7-5-r37","volume":"7","author":"K Lemmens","year":"2006","unstructured":"Lemmens K, Dhollander T, Bie TD, et al. Inferring transcriptional modules from ChIP-chip, motif and microarray data. Genome Biol. 2006;7(5):R37. http:\/\/citeseerx.ist.psu.edu\/viewdoc\/download?doi=10.1.1.279.1345&rep=rep1&type=pdf . Accessed 03 July 2018.","journal-title":"Genome Biol"},{"issue":"2","key":"635_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TCBB.2015.2450740","volume":"13","author":"N Singh","year":"2016","unstructured":"Singh N, Vidyasagar M. bLARS: an algorithm to infer gene regulatory networks. IEEE\/ACM Trans Comput Biol Bioinform. 2016;13(2):1. https:\/\/www.computer.org\/csdl\/trans\/tb\/2016\/02\/07138615.pdf . Accessed 03 July 2018.","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"issue":"1","key":"635_CR4","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1186\/1752-0509-6-145","volume":"6","author":"AC Haury","year":"2012","unstructured":"Haury AC, et al. TIGRESS: trustful inference of gene regulation using stability selection. BMC Syst Biol. 2012;6(1):145. https:\/\/core.ac.uk\/download\/pdf\/51228782.pdf . Accessed 03 July 2018.","journal-title":"BMC Syst Biol"},{"key":"635_CR5","doi-asserted-by":"crossref","unstructured":"Friedman N, Linial M, Nachman I, et al. Using Bayesian network to analyze expression data. J Comput Biol. 2000;7(3\u20134):601\u201320. www.cs.huji.ac.il\/~nir\/Papers\/FLNP1Full.pdf . Accessed 03 July 2018.","DOI":"10.1089\/106652700750050961"},{"key":"635_CR6","volume-title":"Modeling gene expression data using dynamic Bayesian networks. Technical Report, Computer Science Division","author":"K Murphy","year":"1999","unstructured":"Murphy K, Mian S. Modeling gene expression data using dynamic Bayesian networks. Technical Report, Computer Science Division. Berkeley: University of California; 1999. www.cs.ubc.ca\/~murphyk\/Papers\/ismb99.pdf . Accessed 03 July 2018"},{"issue":"1","key":"635_CR7","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1093\/bioinformatics\/bth463","volume":"21","author":"M Zou","year":"2005","unstructured":"Zou M, Conzen SD. A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data. Bioinformatics. 2005;21(1):71\u20139. https:\/\/academic.oup.com\/bioinformatics\/article\/21\/1\/71\/212416 . Accessed 03 July 2018.","journal-title":"Bioinformatics"},{"issue":"48","key":"635_CR8","doi-asserted-by":"publisher","first-page":"19436","DOI":"10.1073\/pnas.1116442108","volume":"108","author":"KY Yeung","year":"2011","unstructured":"Yeung KY, Dombek KM, Lo K, et al. Construction of regulatory networks using expression time-series data of a genotyped population. Proc Natl Acad Sci U S A. 2011;108(48):19436\u201341. www.pnas.org\/content\/108\/48\/19436.full . Accessed 03 July 2018.","journal-title":"Proc Natl Acad Sci U S A"},{"issue":"Suppl 5","key":"635_CR9","doi-asserted-by":"publisher","first-page":"S13","DOI":"10.1186\/1471-2164-12-S5-S13","volume":"12","author":"L Haoni","year":"2011","unstructured":"Haoni L, et al. Learning the structure of gene regulatory networks from time series gene expression data. BMC Genomics. 2011;12(Suppl 5):S13. https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/22369588 . Accessed 03 July 2018.","journal-title":"BMC Genomics"},{"key":"635_CR10","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1186\/s13637-014-0012-3","volume":"2014","author":"P Li","year":"2014","unstructured":"Li P, et al. Gene regulatory network inference and validation using relative chang tratio analysis and time-delayed dynamic Bayesian network. EURASIP J Bioinform Syst Biol. 2014;2014:12. https:\/\/link.springer.com\/article\/10.1186\/s13637-014-0012-3 . Accessed 03 July 2018.","journal-title":"EURASIP J Bioinform Syst Biol"},{"issue":"Suppl 3","key":"635_CR11","doi-asserted-by":"publisher","first-page":"S3","DOI":"10.1186\/1752-0509-5-S3-S3","volume":"5","author":"X Wu","year":"2011","unstructured":"Wu X, et al. State space model with hidden variables for reconstruction of gene regulatory networks. BMC Syst Biol. 2011;5(Suppl 3):S3. http:\/\/europepmc.org\/articles\/PMC3287571 . Accessed 03 July 2018.","journal-title":"BMC Syst Biol"},{"issue":"1","key":"635_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1504\/IJDMB.2008.016753","volume":"2","author":"FX Wu","year":"2008","unstructured":"Wu FX. Gene regulatory network modelling: a state-space approach. Int J Data Min Bioinform. 2008;2(1):1\u201314. http:\/\/citeseerx.ist.psu.edu\/viewdoc\/download?doi=10.1.1.192.4775&rep=rep1&type=pdf . Accessed 03 July 2018.","journal-title":"Int J Data Min Bioinform"},{"key":"635_CR13","doi-asserted-by":"publisher","first-page":"932","DOI":"10.1093\/bioinformatics\/btm639","volume":"24","author":"H Osamu","year":"2008","unstructured":"Osamu H, Ryo Y, Seiya I, Rui Y, Tomoyuki H, Charnock-Jones DS, Cristin P, Satoru M. Statistical inference of transcriptional module-based gene networks from time course gene expression profiles by using state space models. Bioinformatics. 2008;24:932\u201342. https:\/\/academic.oup.com\/bioinformatics\/article\/24\/7\/932\/295736 . Accessed 03 July 2018.","journal-title":"Bioinformatics"},{"key":"635_CR14","first-page":"56","volume":"22","author":"K Kojima","year":"2009","unstructured":"Kojima K, Rui Y, Seiya I, Mai Y, Masao N, Ryo Y, Teppei S, Kazuko U, Tomoyuki H, Noriko G, Satoru M. A state space representation of VAR models with sparse learning for dynamic gene networks. Genome Inform. 2009;22:56\u201368. https:\/\/www.jsbi.org\/pdfs\/journal1\/IBSB09\/IBSB09006.pdf . Accessed 03 July 2018.","journal-title":"Genome Inform"},{"issue":"8","key":"635_CR15","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1093\/nar\/gkt147","volume":"41","author":"J Wang","year":"2013","unstructured":"Wang J, Chen B, Wang Y, et al. Reconstructing regulatory networks from the dynamic plasticity of gene expression by mutual information. Nucleic Acids Res. 2013;41(8):395\u2013408. https:\/\/academic.oup.com\/nar\/article\/41\/8\/e97\/2409387 . Accessed 03 July 2018.","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"635_CR16","doi-asserted-by":"publisher","first-page":"S7","DOI":"10.1186\/1471-2105-7-S1-S7","volume":"7","author":"AA Margolin","year":"2006","unstructured":"Margolin AA, Nemenman I, Basso K, et al. ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics. 2006;7(1):S7. https:\/\/arxiv.org\/pdf\/q-bio\/0410037 . Accessed 03 July 2018.","journal-title":"BMC Bioinformatics"},{"issue":"13","key":"635_CR17","doi-asserted-by":"publisher","first-page":"1876","DOI":"10.1093\/bioinformatics\/btr274","volume":"27","author":"G Sales","year":"2011","unstructured":"Sales G, Romualdi C. Parmigene--a parallel R package for mutual information estimation and gene network reconstruction. Bioinformatics. 2011;27(13):1876\u20137 (2). https:\/\/academic.oup.com\/bioinformatics\/article\/27\/13\/1876\/184634 . Accessed 03 July 2018.","journal-title":"Bioinformatics"},{"issue":"1","key":"635_CR18","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1093\/bioinformatics\/btr626","volume":"28","author":"X Zhang","year":"2012","unstructured":"Zhang X, Zhao XM, He K, et al. Inferring gene regulatory networks from gene expression data by path consistency algorithm based on conditional mutual information. Bioinformatics. 2012;28(1):98\u2013104. https:\/\/dl.acm.org\/citationcfm?id=2139373 Accessed 03 July 2018.","journal-title":"Bioinformatics"},{"issue":"1","key":"635_CR19","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1093\/bioinformatics\/bts619","volume":"29","author":"X Zhang","year":"2013","unstructured":"Zhang X, et al. NARROMI: a noise and redundancy reduction technique improves accuracy of gene regulatory network inference. Bioinformatics. 2013;29(1):106\u201313. https:\/\/core.ac.uk\/download\/pdf\/52414020.pdf . Accessed 03 July 2018.","journal-title":"Bioinformatics"},{"issue":"Suppl 1","key":"635_CR20","doi-asserted-by":"publisher","first-page":"S7","DOI":"10.1186\/1752-0509-4-S1-S7","volume":"4","author":"V Chaitankar","year":"2010","unstructured":"Chaitankar V, Ghosh P, Perkins EJ, et al. A novel gene network inference algorithm using predictive minimum description length approach. BMC Syst Biol. 2010;4(Suppl 1):S7. https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC2880413 . Accessed 03 July 2018.","journal-title":"BMC Syst Biol"},{"issue":"8","key":"635_CR21","doi-asserted-by":"publisher","first-page":"e1005024","DOI":"10.1371\/journal.pcbi.1005024","volume":"12","author":"F Liu","year":"2016","unstructured":"Liu F, Zhang SW, Guo WF, Wei ZG, Chen L. Inference of gene regulatory network based on local Bayesian networks. PLoS Comput Biol. 2016;12(8):e1005024. https:\/\/doi.org\/10.1371\/journal.pcbi.1005024 http:\/\/journals.plos.org\/ploscompbiol\/article?id=10.1371\/journal.pcbi.1005024 . Accessed 03 July 2018.","journal-title":"PLoS Comput Biol"},{"issue":"20","key":"635_CR22","doi-asserted-by":"publisher","first-page":"2633","DOI":"10.1093\/bioinformatics\/btt443","volume":"29","author":"Z Wang","year":"2013","unstructured":"Wang Z. Incorporating prior knowledge into gene network study. Bioinformatics. 2013;29(20):2633\u201340. https:\/\/academic.oup.com\/bioinformatics\/article\/29\/20\/2633\/277562 . Accessed 03 July 2018.","journal-title":"Bioinformatics"},{"key":"635_CR23","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1093\/bioinformatics\/btv268","volume":"31","author":"F Petralia","year":"2015","unstructured":"Petralia F, Wang P, Yang J, Zhidong T. Integrative random forest for gene regulatory network inference. Bioinformatics. 2015;31:197\u2013205. https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC4542785\/ . Accessed 03 July 2018.","journal-title":"Bioinformatics"},{"issue":"6","key":"635_CR24","doi-asserted-by":"publisher","first-page":"1241","DOI":"10.3934\/mbe.2016041","volume":"13","author":"WC Young","year":"2017","unstructured":"Young WC, Raftery AE, Yeung KY. A posterior probability approach for gene regulatory network inference in genetic perturbation data. Math Biosci Eng. 2017;13(6):1241\u201351. https:\/\/arxiv.org\/abs\/1603.04835 . Accessed 03 July 2018.","journal-title":"Math Biosci Eng"},{"issue":"6062","key":"635_CR25","doi-asserted-by":"publisher","first-page":"1518","DOI":"10.1126\/science.1205438","volume":"334","author":"DN Reshef","year":"2011","unstructured":"Reshef DN, Reshef YA, Finucane HK, et al. Detecting novel associations in large data sets. Science. 2011;334(6062):1518\u201324. http:\/\/science.sciencemag.org\/content\/334\/6062\/1518 . Accessed 03 July 2018.","journal-title":"Science"},{"key":"635_CR26","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1038\/ng1532","volume":"37","author":"K Basso","year":"2005","unstructured":"Basso K, et al. Reverse engineering of regulatory networks in human B cells. Nat Genet. 2005;37:382\u201392. https:\/\/s3-us-west-2.amazonaws.com\/oww-files-public\/b\/b2\/Basso.pdf . Accessed 03 July 2018.","journal-title":"Nat Genet"},{"key":"635_CR27","doi-asserted-by":"crossref","unstructured":"Jiang J, Wang J, Yu H, et al. Poison identification based on Bayesian network: a novel improvement on K2 algorithm via Markov blanket[M]\/\/ advances in swarm intelligence: Springer Berlin Heidelberg; 2013. p. 173\u201382. https:\/\/link.springer.com\/chapter\/10.1007\/978-3-642-38715-9_21 . Accessed 03 July 2018","DOI":"10.1007\/978-3-642-38715-9_21"},{"key":"635_CR28","unstructured":"Dream4 In Silico Network Challenage. http:\/\/dreamchallenges.org\/project\/dream4-in-silico-network-challenge\/ . Accessed 03 July 2018."},{"issue":"22","key":"635_CR29","doi-asserted-by":"publisher","first-page":"2413","DOI":"10.1093\/bioinformatics\/btl396","volume":"22","author":"Y Wang","year":"2006","unstructured":"Wang Y, Joshi T, Zhang XS, et al. Inferring gene regulatory networks from multiple microarray datasets. Bioinformatics. 2006;22(22):2413\u201320. https:\/\/academic.oup.com\/bioinformatics\/article\/22\/19\/2413\/240982 . Accessed 03 July 2018.","journal-title":"Bioinformatics"},{"issue":"1","key":"635_CR30","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"R Tibshirani","year":"1996","unstructured":"Tibshirani R. Regression shrinkage and selection via the lasso. J Royal Stat Soc. 1996;58(1):267\u201388. http:\/\/statweb.stanford.edu\/~tibs\/ftp\/lasso-retro.pdf . Accessed 03 July 2018.","journal-title":"J Royal Stat Soc"},{"issue":"2","key":"635_CR31","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1093\/bioinformatics\/btr641","volume":"28","author":"G Geeven","year":"2012","unstructured":"Geeven G, et al. Identification of context-specific gene regulatory networks with GEMULA-gene expression modeling using LAsso. Bioinformatics. 2012;28(2):214\u201321. https:\/\/academic.oup.com\/bioinformatics\/article\/28\/2\/214\/198473 . Accessed 03 July 2018.","journal-title":"Bioinformatics"},{"issue":"9","key":"635_CR32","doi-asserted-by":"publisher","first-page":"e12776","DOI":"10.1371\/journal.pone.0012776","volume":"5","author":"VA Huynh-Thu","year":"2010","unstructured":"Huynh-Thu VA, et al. Infering regulatory networks from expression data using tree-based methods. PLoS One. 2010;5(9):e12776. http:\/\/journals.plos.org\/plosone\/article?id=10.1371\/journal.pone.0012776 . Accessed 03 July 2018.","journal-title":"PLoS One"},{"key":"635_CR33","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1186\/1752-0509-6-62","volume":"6","author":"Morshed","year":"2012","unstructured":"Morshed, et al. Simultaneous learning instantaneous and time-delayed genetic interactions using novel information theoretic scoring technique. BMC Syst Biol. 2012;6:62. https:\/\/link.springer.com\/chapter\/10.1007%2F978-3-642-24958-7_29 . Accessed 03 July 2018.","journal-title":"BMC Syst Biol"},{"issue":"16","key":"635_CR34","doi-asserted-by":"publisher","first-page":"10555","DOI":"10.1073\/pnas.152046799","volume":"99","author":"M Ronen","year":"2002","unstructured":"Ronen M, Rosenberg R, Shraiman BI, et al. Assigning numbers to the arrows: parameterizing a gene regulation network by using accurate expression kinetics. Proc Natl Acad Sci U S A. 2002;99(16):10555\u201360. www.pnas.org\/lookup\/doi\/10.1073\/pnas.152046799 . Accessed 03 July 2018.","journal-title":"Proc Natl Acad Sci U S A"},{"issue":"1","key":"635_CR35","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1038\/ng881","volume":"31","author":"SS Shenorr","year":"2002","unstructured":"Shenorr SS, Milo R, Mangan S, et al. Network motifs in the transcriptional regulation network of Escherichia coli. Nat Genet. 2002;31(1):64\u20138. https:\/\/www.weizmann.ac.il\/mcb\/UriAlon\/sites\/mcb.UriAlon\/files\/network_motifs_in_coli_0.pdf . Accessed 03 July 2018.","journal-title":"Nat Genet"},{"key":"635_CR36","unstructured":"Uri Alon\u2019s SOS Dataset webpage. https:\/\/www.weizmann.ac.il\/mcb\/UriAlon\/download\/downloadable-data . Accessed 03 July 2018."},{"key":"635_CR37","doi-asserted-by":"crossref","unstructured":"Noman N, Iba H. Inferring gene regulatory networks using differential evolution with local search heuristics. IEEE\/ACM Trans Comput Biol Bioinform. 2007;4(4):643\u20137. https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/17975274 . Accessed 03 July 2018.","DOI":"10.1109\/TCBB.2007.1058"},{"issue":"7","key":"635_CR38","doi-asserted-by":"publisher","first-page":"1154","DOI":"10.1093\/bioinformatics\/bti071","volume":"21","author":"S Kamura","year":"2005","unstructured":"Kamura S, et al. Inference of S-system models of genetic networks using a cooperative coevolutionary algorithm. Bioinformatics. 2005;21(7):1154\u201363. https:\/\/academic.oup.com\/bioinformatics\/article\/21\/7\/1154\/268773 . Accessed 03 July 2018.","journal-title":"Bioinformatics"},{"issue":"suppl 2","key":"635_CR39","doi-asserted-by":"publisher","first-page":"ii138","DOI":"10.1093\/bioinformatics\/btg1071","volume":"19","author":"BE Perrin","year":"2003","unstructured":"Perrin BE, Ralaivola L, Mazurie A, et al. Gene networks inference using dynamic Bayesian networks. Bioinformatics. 2003;19(suppl 2):ii138\u201348. https:\/\/academic.oup.com\/bioinformatics\/article\/19\/suppl_2\/ii138\/180436 . Accessed 03 July 2018.","journal-title":"Bioinformatics"},{"issue":"18","key":"635_CR40","doi-asserted-by":"publisher","first-page":"3594","DOI":"10.1093\/bioinformatics\/bth448","volume":"20","author":"J Yu","year":"2004","unstructured":"Yu J, Smith VA, Wang PP, et al. Advances to Bayesian network inference for generating causal networks from observational biological data. Bioinformatics. 2004;20(18):3594\u2013603. https:\/\/www.semanticscholar.org\/paper\/Advances-to-Bayesian-network-inference-for-causal-Yu-Smith\/adcbcb725490d64d12b4f795e1e381ca6b8de4b4 . Accessed 03 July 2018.","journal-title":"Bioinformatics"},{"issue":"19","key":"635_CR41","doi-asserted-by":"publisher","first-page":"2765","DOI":"10.1093\/bioinformatics\/btr457","volume":"27","author":"NX Vinh","year":"2011","unstructured":"Vinh NX, Chetty M, Coppel R, et al. GlobalMIT: learning globally optimal dynamic bayesian network with the mutual information test criterion. Bioinformatics. 2011;27(19):2765\u20136. https:\/\/academic.oup.com\/bioinformatics\/article\/27\/19\/2765\/231220 . Accessed 03 July 2018.","journal-title":"Bioinformatics"}],"container-title":["BMC Systems Biology"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12918-018-0635-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s12918-018-0635-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12918-018-0635-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,13]],"date-time":"2024-07-13T09:56:02Z","timestamp":1720864562000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcsystbiol.biomedcentral.com\/articles\/10.1186\/s12918-018-0635-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,12]]},"references-count":41,"journal-issue":{"issue":"S7","published-print":{"date-parts":[[2018,12]]}},"alternative-id":["635"],"URL":"https:\/\/doi.org\/10.1186\/s12918-018-0635-1","relation":{},"ISSN":["1752-0509"],"issn-type":[{"value":"1752-0509","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,12]]},"assertion":[{"value":"14 December 2018","order":1,"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"}},{"value":"Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Publisher\u2019s Note"}}],"article-number":"115"}}