{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T15:30:00Z","timestamp":1744212600161,"version":"3.37.3"},"reference-count":21,"publisher":"Oxford University Press (OUP)","issue":"24","funder":[{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2013,12,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: The abundance of many transcripts changes significantly in response to a variety of molecular and environmental perturbations. A key question in this setting is as follows: what intermediate molecular perturbations gave rise to the observed transcriptional changes? Regulatory programs are not exclusively governed by transcriptional changes but also by protein abundance and post-translational modifications making direct causal inference from data difficult. However, biomedical research over the last decades has uncovered a plethora of causal signaling cascades that can be used to identify good candidates explaining a specific set of transcriptional changes.<\/jats:p>\n               <jats:p>Methods: We take a Bayesian approach to integrate gene expression profiling with a causal graph of molecular interactions constructed from prior biological knowledge. In addition, we define the biological context of a specific interaction by the corresponding Medical Subject Headings terms. The Bayesian network can be queried to suggest upstream regulators that can be causally linked to the altered expression profile.<\/jats:p>\n               <jats:p>Results: Our approach will treat candidate regulators in the right biological context preferentially, enables hierarchical exploration of resulting hypotheses and takes the complete network of causal relationships into account to arrive at the best set of upstream regulators. We demonstrate the power of our method on distinct biological datasets, namely response to dexamethasone treatment, stem cell differentiation and a neuropathic pain model. In all cases relevant biological insights could be validated.<\/jats:p>\n               <jats:p>Availability and implementation: Source code for the method is available upon request.<\/jats:p>\n               <jats:p>Contact: \u00a0daniel.ziemek@pfizer.com<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btt557","type":"journal-article","created":{"date-parts":[[2013,9,28]],"date-time":"2013-09-28T03:43:59Z","timestamp":1380339839000},"page":"3167-3173","source":"Crossref","is-referenced-by-count":15,"title":["Molecular causes of transcriptional response: a Bayesian prior knowledge approach"],"prefix":"10.1093","volume":"29","author":[{"given":"Kourosh","family":"Zarringhalam","sequence":"first","affiliation":[{"name":"1 Computational Sciences Center of Emphasis, Pfizer Worldwide Research & Development, Cambridge, MA 02140, USA, 2Department of Mathematics, University of Massachusetts Boston, Boston, MA 02125, USA, 3Drug Safety Research & Development, Pfizer, Groton, CT 06340, USA and 4Neusentis, Pfizer Worldwide Research & Development, Cambridge CB21 6GS, UK"},{"name":"1 Computational Sciences Center of Emphasis, Pfizer Worldwide Research & Development, Cambridge, MA 02140, USA, 2Department of Mathematics, University of Massachusetts Boston, Boston, MA 02125, USA, 3Drug Safety Research & Development, Pfizer, Groton, CT 06340, USA and 4Neusentis, Pfizer Worldwide Research & Development, Cambridge CB21 6GS, UK"}]},{"given":"Ahmed","family":"Enayetallah","sequence":"additional","affiliation":[{"name":"1 Computational Sciences Center of Emphasis, Pfizer Worldwide Research & Development, Cambridge, MA 02140, USA, 2Department of Mathematics, University of Massachusetts Boston, Boston, MA 02125, USA, 3Drug Safety Research & Development, Pfizer, Groton, CT 06340, USA and 4Neusentis, Pfizer Worldwide Research & Development, Cambridge CB21 6GS, UK"}]},{"given":"Alex","family":"Gutteridge","sequence":"additional","affiliation":[{"name":"1 Computational Sciences Center of Emphasis, Pfizer Worldwide Research & Development, Cambridge, MA 02140, USA, 2Department of Mathematics, University of Massachusetts Boston, Boston, MA 02125, USA, 3Drug Safety Research & Development, Pfizer, Groton, CT 06340, USA and 4Neusentis, Pfizer Worldwide Research & Development, Cambridge CB21 6GS, UK"}]},{"given":"Ben","family":"Sidders","sequence":"additional","affiliation":[{"name":"1 Computational Sciences Center of Emphasis, Pfizer Worldwide Research & Development, Cambridge, MA 02140, USA, 2Department of Mathematics, University of Massachusetts Boston, Boston, MA 02125, USA, 3Drug Safety Research & Development, Pfizer, Groton, CT 06340, USA and 4Neusentis, Pfizer Worldwide Research & Development, Cambridge CB21 6GS, UK"}]},{"given":"Daniel","family":"Ziemek","sequence":"additional","affiliation":[{"name":"1 Computational Sciences Center of Emphasis, Pfizer Worldwide Research & Development, Cambridge, MA 02140, USA, 2Department of Mathematics, University of Massachusetts Boston, Boston, MA 02125, USA, 3Drug Safety Research & Development, Pfizer, Groton, CT 06340, USA and 4Neusentis, Pfizer Worldwide Research & Development, Cambridge CB21 6GS, UK"}]}],"member":"286","published-online":{"date-parts":[[2013,9,26]]},"reference":[{"key":"2023012810495527500_btt557-B1","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1186\/1471-2105-10-47","article-title":"A general modular framework for gene set enrichment analysis","volume":"10","author":"Ackermann","year":"2009","journal-title":"BMC Bioinformatics"},{"key":"2023012810495527500_btt557-B2","doi-asserted-by":"crossref","first-page":"3523","DOI":"10.1093\/nar\/gkq045","article-title":"Going Bayesian: model-based gene set analysis of genome-scale data","volume":"38","author":"Bauer","year":"2010","journal-title":"Nucleic Acids Res."},{"key":"2023012810495527500_btt557-B3","doi-asserted-by":"crossref","first-page":"1114","DOI":"10.1093\/bioinformatics\/bts090","article-title":"Causal reasoning on biological networks: interpreting transcriptional changes","volume":"28","author":"Chindelevitch","year":"2012","journal-title":"Bioinformatics"},{"key":"2023012810495527500_btt557-B4","doi-asserted-by":"crossref","first-page":"14415","DOI":"10.1523\/JNEUROSCI.4569-09.2009","article-title":"T-cell infiltration and signaling in the adult dorsal spinal cord is a major contributor to neuropathic pain-like hypersensitivity","volume":"29","author":"Costigan","year":"2009","journal-title":"J. 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