{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,5]],"date-time":"2025-05-05T21:06:42Z","timestamp":1746479202738,"version":"3.37.3"},"reference-count":11,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2019,12,16]],"date-time":"2019-12-16T00:00:00Z","timestamp":1576454400000},"content-version":"vor","delay-in-days":15,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2016YFE0204900"],"award-info":[{"award-number":["2016YFE0204900"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81530088","81973142"],"award-info":[{"award-number":["81530088","81973142"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["CA209414, CA092824, and ES000002"],"award-info":[{"award-number":["CA209414, CA092824, and ES000002"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2019,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>High-throughput technologies have brought tremendous changes to biological domains, and the resulting high-dimensional data has also posed enormous challenges to computational science. A Bayesian network is a probabilistic graphical model represented by a directed acyclic graph, which provides concise semantics to describe the relationship between entities and has an independence assumption that is suitable for sparse omics data. Bayesian networks have been broadly used in biomedical research fields, including disease risk assessment and prognostic prediction. However, the inference and visualization of Bayesian networks are unfriendly to the users lacking programming skills.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>We developed an R\/Shiny application, <jats:italic>shinyBN<\/jats:italic>, which is an online graphical user interface to facilitate the inference and visualization of Bayesian networks. <jats:italic>shinyBN<\/jats:italic> supports multiple types of input and provides flexible settings for network rendering and inference. For output, users can download network plots, prediction results and external validation results in publication-ready high-resolution figures.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>Our user-friendly application (<jats:italic>shinyBN<\/jats:italic>) provides users with an easy method for Bayesian network modeling, inference and visualization via mouse clicks. <jats:italic>shinyBN<\/jats:italic> can be used in the R environment or online and is compatible with three major operating systems, including Windows, Linux and Mac OS. <jats:italic>shinyBN<\/jats:italic> is deployed at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/jiajin.shinyapps.io\/shinyBN\/\">https:\/\/jiajin.shinyapps.io\/shinyBN\/<\/jats:ext-link>. Source codes and the manual are freely available at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/www.github.com\/JiajinChen\/shinyBN\">https:\/\/github.com\/JiajinChen\/shinyBN<\/jats:ext-link>.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-019-3309-0","type":"journal-article","created":{"date-parts":[[2019,12,16]],"date-time":"2019-12-16T19:03:26Z","timestamp":1576523006000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["shinyBN: an online application for interactive Bayesian network inference and visualization"],"prefix":"10.1186","volume":"20","author":[{"given":"Jiajin","family":"Chen","sequence":"first","affiliation":[]},{"given":"Ruyang","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xuesi","family":"Dong","sequence":"additional","affiliation":[]},{"given":"Lijuan","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Ying","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Jieyu","family":"He","sequence":"additional","affiliation":[]},{"given":"David C.","family":"Christiani","sequence":"additional","affiliation":[]},{"given":"Yongyue","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Feng","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,12,16]]},"reference":[{"issue":"4","key":"3309_CR1","doi-asserted-by":"publisher","first-page":"435","DOI":"10.1038\/ng1533","volume":"37","author":"P Sebastiani","year":"2005","unstructured":"Sebastiani P, Ramoni MF, Nolan V, Baldwin CT, Steinberg MH. Genetic dissection and prognostic modeling of overt stroke in sickle cell anemia. Nat Genet. 2005;37(4):435.","journal-title":"Nat Genet"},{"key":"3309_CR2","doi-asserted-by":"crossref","unstructured":"Krishnan KC, Kurt Z, Barrere-Cain R, Sabir S, Das A, Floyd R, Vergnes L, Zhao Y, Che N, Charugundla S. Integration of multi-omics data from mouse diversity panel highlights mitochondrial dysfunction in non-alcoholic fatty liver disease. Cell Syst. 2018;6(1):103\u2013115.e107.","DOI":"10.1016\/j.cels.2017.12.006"},{"key":"3309_CR3","first-page":"902","volume-title":"Sixteenth National Conference on Artificial Intelligence and Eleventh Conference on Innovative Applications of Artificial Intelligence","author":"MJ Druzdzel","year":"1999","unstructured":"Druzdzel MJ. SMILE: structural modeling, inference, and learning engine and GeNIe: a development environment for graphical decision-theoretic models. In: Sixteenth National Conference on Artificial Intelligence and Eleventh Conference on Innovative Applications of Artificial Intelligence; 1999. p. 902\u20133."},{"key":"3309_CR4","volume-title":"Book of Abstracts of the R User Conference","author":"CM Friedrich","year":"2009","unstructured":"Friedrich CM, Klinger R. rSMILE, an interface to the Bayesian Network package GeNIe\/SMILE. In: Book of Abstracts of the R User Conference; 2009."},{"key":"3309_CR5","doi-asserted-by":"publisher","first-page":"425","DOI":"10.21105\/joss.00425","volume":"3","author":"PB Govan","year":"2018","unstructured":"Govan PB. BayesianNetwork: interactive Bayesian network modeling and analysis. J Open Source Softw. 2018;3:425.","journal-title":"J Open Source Softw"},{"issue":"03","key":"3309_CR6","doi-asserted-by":"publisher","first-page":"2010","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(03):2010.","journal-title":"J Stat Softw"},{"issue":"10","key":"3309_CR7","first-page":"1","volume":"46","author":"S H\u00f8jsgaard","year":"2012","unstructured":"H\u00f8jsgaard S. Graphical independence networks with the gRain package for R. J Stat Softw. 2012;46(10):1\u201326.","journal-title":"J Stat Softw"},{"key":"3309_CR8","unstructured":"Almende B, Thieurmel B, Robert T. visNetwork: network visualization using\u2019vis. js\u2019 Library. R Package Version 2.0.9. 2019;\u00a0https:\/\/CRAN.R-project.org\/package=visNetwork."},{"issue":"1","key":"3309_CR9","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1186\/1471-2105-12-77","volume":"12","author":"X Robin","year":"2011","unstructured":"Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez J-C, M\u00fcller M. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics. 2011;12(1):77.","journal-title":"BMC Bioinformatics"},{"key":"3309_CR10","unstructured":"Chang W, Cheng J, Allaire JJ, Xie Y, Mcpherson J. shiny: web application framework for R. R Package Version 1.4.0. 2019; https:\/\/CRAN.R-project.org\/package=shiny."},{"key":"3309_CR11","unstructured":"Friedman N, Goldszmidt M, Wyner A. Data analysis with Bayesian networks: a bootstrap approach. In: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence. San Francisco: Morgan Kaufmann Publishers Inc; 1999. p. 196\u2013205."}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-019-3309-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s12859-019-3309-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-019-3309-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,12,15]],"date-time":"2020-12-15T00:16:14Z","timestamp":1607991374000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-019-3309-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12]]},"references-count":11,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["3309"],"URL":"https:\/\/doi.org\/10.1186\/s12859-019-3309-0","relation":{},"ISSN":["1471-2105"],"issn-type":[{"type":"electronic","value":"1471-2105"}],"subject":[],"published":{"date-parts":[[2019,12]]},"assertion":[{"value":"26 March 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 December 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 December 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":"711"}}