{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T11:08:33Z","timestamp":1721387313846},"reference-count":13,"publisher":"Oxford University Press (OUP)","issue":"19","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2015,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Summary: With the widespread availability of high-throughput experimental technologies it has become possible to study hundreds to thousands of cellular factors simultaneously, such as coding- or non-coding mRNA or protein concentrations. Still, extracting information about the underlying regulatory or signaling interactions from these data remains a difficult challenge. We present a flexible approach towards network inference based on linear programming. Our method reconstructs the interactions of factors from a combination of perturbation\/non-perturbation and steady-state\/time-series data. We show both on simulated and real data that our methods are able to reconstruct the underlying networks fast and efficiently, thus shedding new light on biological processes and, in particular, into disease\u2019s mechanisms of action. We have implemented the approach as an R package available through bioconductor.<\/jats:p>\n               <jats:p>Availability and implementation: This R package is freely available under the Gnu Public License (GPL-3) from bioconductor.org (http:\/\/bioconductor.org\/packages\/release\/bioc\/html\/lpNet.html) and is compatible with most operating systems (Windows, Linux, Mac OS) and hardware architectures.<\/jats:p>\n               <jats:p>Contact: \u00a0bettina.knapp@helmholtz-muenchen.de<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btv327","type":"journal-article","created":{"date-parts":[[2015,5,31]],"date-time":"2015-05-31T00:10:18Z","timestamp":1433031018000},"page":"3231-3233","source":"Crossref","is-referenced-by-count":4,"title":["lpNet: a linear programming approach to reconstruct signal transduction networks"],"prefix":"10.1093","volume":"31","author":[{"given":"Marta R. A.","family":"Matos","sequence":"first","affiliation":[{"name":"1 Institute for Medical Informatics and Biometry, Medical Faculty Carl Gustav Carus, Technische Universit\u00e4t Dresden, 01307 Dresden, Germany and"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bettina","family":"Knapp","sequence":"additional","affiliation":[{"name":"1 Institute for Medical Informatics and Biometry, Medical Faculty Carl Gustav Carus, Technische Universit\u00e4t Dresden, 01307 Dresden, Germany and"},{"name":"2 Institute of Computational Biology, Helmholtz Zentrum M\u00fcnchen, 85764 Neuherberg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lars","family":"Kaderali","sequence":"additional","affiliation":[{"name":"1 Institute for Medical Informatics and Biometry, Medical Faculty Carl Gustav Carus, Technische Universit\u00e4t Dresden, 01307 Dresden, Germany and"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2015,5,29]]},"reference":[{"key":"2023020202223029500_btv327-B1","doi-asserted-by":"crossref","first-page":"1283","DOI":"10.1093\/bioinformatics\/btt130","article-title":"Sorad: a systems biology approach to predict and modulate dynamic signaling pathway response from phosphoproteome time-course measurements","volume":"29","author":"\u00c4ij\u00f6","year":"2013","journal-title":"Bioinformatics"},{"key":"2023020202223029500_btv327-B2","author":"Bender","year":"2013"},{"key":"2023020202223029500_btv327-B3","doi-asserted-by":"crossref","first-page":"i596","DOI":"10.1093\/bioinformatics\/btq385","article-title":"Dynamic deterministic effects propagation networks: learning signalling pathways from longitudinal protein array data","volume":"26","author":"Bender","year":"2010","journal-title":"Bioinformatics"},{"key":"2023020202223029500_btv327-B4","doi-asserted-by":"crossref","first-page":"e35077","DOI":"10.1371\/journal.pone.0035077","article-title":"Hub-centered gene network reconstruction using automatic relevance determination","volume":"7","author":"Bock","year":"2012","journal-title":"PLoS One"},{"key":"2023020202223029500_btv327-B5","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1089\/106652700750050961","article-title":"Using Bayesian networks to analyze expression data","volume":"7","author":"Friedman","year":"2000","journal-title":"J. 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