{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T10:12:27Z","timestamp":1780049547778,"version":"3.53.1"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1008223","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2021,2,10]],"date-time":"2021-02-10T00:00:00Z","timestamp":1612915200000}}],"reference-count":90,"publisher":"Public Library of Science (PLoS)","issue":"1","license":[{"start":{"date-parts":[[2021,1,29]],"date-time":"2021-01-29T00:00:00Z","timestamp":1611878400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000051","name":"NIH \/ National Human Genome Research Institute","doi-asserted-by":"crossref","award":["NIH R01 HL133218"],"award-info":[{"award-number":["NIH R01 HL133218"]}],"id":[{"id":"10.13039\/100000051","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000051","name":"NIH \/ National Human Genome Research Institute","doi-asserted-by":"crossref","award":["NIH U01 HG007900"],"award-info":[{"award-number":["NIH U01 HG007900"]}],"id":[{"id":"10.13039\/100000051","id-type":"DOI","asserted-by":"crossref"}]},{"name":"NSF \/ Division of Information and Intelligent Systems","award":["NSF 711 CAREER 1750729"],"award-info":[{"award-number":["NSF 711 CAREER 1750729"]}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>\n                    Gene regulatory network inference is essential to uncover complex relationships among gene pathways and inform downstream experiments, ultimately enabling regulatory network re-engineering. Network inference from transcriptional time-series data requires accurate, interpretable, and efficient determination of causal relationships among thousands of genes. Here, we develop Bootstrap Elastic net regression from Time Series (BETS), a statistical framework based on Granger causality for the recovery of a directed gene network from transcriptional time-series data. BETS uses elastic net regression and stability selection from bootstrapped samples to infer causal relationships among genes. BETS is highly parallelized, enabling efficient analysis of large transcriptional data sets. We show competitive accuracy on a community benchmark, the DREAM4 100-gene network inference challenge, where BETS is one of the fastest among methods of similar performance and additionally infers whether causal effects are activating or inhibitory. We apply BETS to transcriptional time-series data of differentially-expressed genes from A549 cells exposed to glucocorticoids over a period of 12 hours. We identify a network of 2768 genes and 31,945 directed edges (FDR \u2264 0.2). We validate inferred causal network edges using two external data sources: Overexpression experiments on the same glucocorticoid system, and genetic variants associated with inferred edges in primary lung tissue in the Genotype-Tissue Expression (GTEx) v6 project. BETS is available as an open source software package at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/lujonathanh\/BETS\" xlink:type=\"simple\">https:\/\/github.com\/lujonathanh\/BETS<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1008223","type":"journal-article","created":{"date-parts":[[2021,1,29]],"date-time":"2021-01-29T13:42:12Z","timestamp":1611927732000},"page":"e1008223","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":34,"title":["Causal network inference from gene transcriptional time-series response to glucocorticoids"],"prefix":"10.1371","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1302-4875","authenticated-orcid":true,"given":"Jonathan","family":"Lu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bianca","family":"Dumitrascu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5811-1297","authenticated-orcid":true,"given":"Ian C.","family":"McDowell","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9641-7948","authenticated-orcid":true,"given":"Brian","family":"Jo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9244-9822","authenticated-orcid":true,"given":"Alejandro","family":"Barrera","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linda K.","family":"Hong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sarah M.","family":"Leichter","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7629-061X","authenticated-orcid":true,"given":"Timothy E.","family":"Reddy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6139-7334","authenticated-orcid":true,"given":"Barbara E.","family":"Engelhardt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2021,1,29]]},"reference":[{"issue":"8","key":"pcbi.1008223.ref001","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1038\/nrg3244","article-title":"Studying and modelling dynamic biological processes using time-series gene expression data","volume":"13","author":"Z Bar-Joseph","year":"2012","journal-title":"Nature Reviews Genetics"},{"key":"pcbi.1008223.ref002","first-page":"733","article-title":"Bayesian factor regression models in the \u201clarge p, small n\u201d paradigm","volume":"7","author":"J Bernardo","year":"2003","journal-title":"Bayesian Statistics"},{"key":"pcbi.1008223.ref003","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1146\/annurev-statistics-022513-115545","article-title":"High-dimensional statistics with a view toward applications in biology","volume":"1","author":"P B\u00fchlmann","year":"2014","journal-title":"Annual Review of Statistics and Its Application"},{"issue":"6","key":"pcbi.1008223.ref004","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.tcb.2008.03.005","article-title":"Circadian clock function in Arabidopsis thaliana: time beyond transcription","volume":"18","author":"P Mas","year":"2008","journal-title":"Trends in cell biology"},{"issue":"Dec","key":"pcbi.1008223.ref005","first-page":"3647","article-title":"Learning non-stationary dynamic Bayesian networks","volume":"11","author":"JW Robinson","year":"2010","journal-title":"Journal of Machine Learning Research"},{"issue":"9","key":"pcbi.1008223.ref006","doi-asserted-by":"crossref","first-page":"1272","DOI":"10.1101\/gr.233346.117","article-title":"Glucocorticoid receptor recruits to enhancers and drives activation by motif-directed binding","volume":"28","author":"IC McDowell","year":"2018","journal-title":"Genome Research"},{"key":"pcbi.1008223.ref007","article-title":"Immune regulation by glucocorticoids","author":"DW Cain","year":"2017","journal-title":"Nature Reviews Immunology"},{"key":"pcbi.1008223.ref008","unstructured":"UK Government. 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