{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T13:44:14Z","timestamp":1781790254240,"version":"3.54.5"},"reference-count":36,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2016,10,2]],"date-time":"2016-10-02T00:00:00Z","timestamp":1475366400000},"content-version":"vor","delay-in-days":1291,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2013,4,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Inferring global regulatory networks (GRNs) from genome-wide data is a computational challenge central to the field of systems biology. Although the primary data currently used to infer GRNs consist of gene expression and proteomics measurements, there is a growing abundance of alternate data types that can reveal regulatory interactions, e.g. ChIP-Chip, literature-derived interactions, protein\u2013protein interactions. GRN inference requires the development of integrative methods capable of using these alternate data as priors on the GRN structure. Each source of structure priors has its unique biases and inherent potential errors; thus, GRN methods using these data must be robust to noisy inputs.<\/jats:p>\n               <jats:p>Results: We developed two methods for incorporating structure priors into GRN inference. Both methods [Modified Elastic Net (MEN) and Bayesian Best Subset Regression (BBSR)] extend the previously described Inferelator framework, enabling the use of prior information. We test our methods on one synthetic and two bacterial datasets, and show that both MEN and BBSR infer accurate GRNs even when the structure prior used has significant amounts of error (&amp;gt;90% erroneous interactions). We find that BBSR outperforms MEN at inferring GRNs from expression data and noisy structure priors.<\/jats:p>\n               <jats:p>Availability and implementation: Code, datasets and networks presented in this article are available at http:\/\/bonneaulab.bio.nyu.edu\/software.html.<\/jats:p>\n               <jats:p>Contact: \u00a0bonneau@nyu.edu<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btt099","type":"journal-article","created":{"date-parts":[[2013,3,23]],"date-time":"2013-03-23T00:38:23Z","timestamp":1363999103000},"page":"1060-1067","source":"Crossref","is-referenced-by-count":137,"title":["Robust data-driven incorporation of prior knowledge into the inference of dynamic regulatory networks"],"prefix":"10.1093","volume":"29","author":[{"given":"Alex","family":"Greenfield","sequence":"first","affiliation":[{"name":"1 Computational Biology Program, New York University Sackler School of Medicine, New York, NY 10065, 2Department of Biology, Center for Genomics and Systems Biology, New York, NY 10003 and 3Computer Science Department, Courant Institute of Mathematical Sciences, New York University, New York, NY 10012, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christoph","family":"Hafemeister","sequence":"additional","affiliation":[{"name":"1 Computational Biology Program, New York University Sackler School of Medicine, New York, NY 10065, 2Department of Biology, Center for Genomics and Systems Biology, New York, NY 10003 and 3Computer Science Department, Courant Institute of Mathematical Sciences, New York University, New York, NY 10012, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard","family":"Bonneau","sequence":"additional","affiliation":[{"name":"1 Computational Biology Program, New York University Sackler School of Medicine, New York, NY 10065, 2Department of Biology, Center for Genomics and Systems Biology, New York, NY 10003 and 3Computer Science Department, Courant Institute of Mathematical Sciences, New York University, New York, NY 10012, USA"},{"name":"1 Computational Biology Program, New York University Sackler School of Medicine, New York, NY 10065, 2Department of Biology, Center for Genomics and Systems Biology, New York, NY 10003 and 3Computer Science Department, Courant Institute of Mathematical Sciences, New York University, New York, NY 10012, USA"},{"name":"1 Computational Biology Program, New York University Sackler School of Medicine, New York, NY 10065, 2Department of Biology, Center for Genomics and Systems Biology, New York, NY 10003 and 3Computer Science Department, Courant Institute of Mathematical Sciences, New York University, New York, NY 10012, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2013,3,21]]},"reference":[{"key":"2023012810324009200_btt099-B1","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":"Bar-Joseph","year":"2012","journal-title":"Nat. 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