{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:34:18Z","timestamp":1772138058159,"version":"3.50.1"},"reference-count":18,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2023,4,6]],"date-time":"2023-04-06T00:00:00Z","timestamp":1680739200000},"content-version":"vor","delay-in-days":5,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,4,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Modular response analysis (MRA) is a well-established method to infer biological networks from perturbation data. Classically, MRA requires the solution of a linear system, and results are sensitive to noise in the data and perturbation intensities. Due to noise propagation, applications to networks of 10 nodes or more are difficult.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We propose a new formulation of MRA as a multilinear regression problem. This enables to integrate all the replicates and potential additional perturbations in a larger, over-determined, and more stable system of equations. More relevant confidence intervals on network parameters can be obtained, and we show competitive performance for networks of size up to 1000. Prior knowledge integration in the form of known null edges further improves these results.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The R code used to obtain the presented results is available from GitHub: https:\/\/github.com\/J-P-Borg\/BioInformatics<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad166","type":"journal-article","created":{"date-parts":[[2023,4,6]],"date-time":"2023-04-06T08:44:12Z","timestamp":1680770652000},"source":"Crossref","is-referenced-by-count":4,"title":["Modular response analysis reformulated as a multilinear regression problem"],"prefix":"10.1093","volume":"39","author":[{"given":"Jean-Pierre","family":"Borg","sequence":"first","affiliation":[{"name":"Institut de Recherche en Canc\u00e9rologie de Montpellier, Inserm U1194 , Montpellier 34298, France"},{"name":"Institut r\u00e9gional du Cancer Montpellier , Montpellier 34298, France"},{"name":"Universit\u00e9 de Montpellier , Montpellier 34090, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2466-4824","authenticated-orcid":false,"given":"Jacques","family":"Colinge","sequence":"additional","affiliation":[{"name":"Institut de Recherche en Canc\u00e9rologie de Montpellier, Inserm U1194 , Montpellier 34298, France"},{"name":"Institut r\u00e9gional du Cancer Montpellier , Montpellier 34298, France"},{"name":"Universit\u00e9 de Montpellier , Montpellier 34090, France"},{"name":"Facult\u00e9 de M\u00e9decine , Montpellier 34090, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5752-7343","authenticated-orcid":false,"given":"Patrice","family":"Ravel","sequence":"additional","affiliation":[{"name":"Institut de Recherche en Canc\u00e9rologie de Montpellier, Inserm U1194 , Montpellier 34298, France"},{"name":"Institut r\u00e9gional du Cancer Montpellier , Montpellier 34298, France"},{"name":"Universit\u00e9 de Montpellier , Montpellier 34090, France"},{"name":"Facult\u00e9 de Pharmacie , Montpellier 34090, France"}]}],"member":"286","published-online":{"date-parts":[[2023,4,6]]},"reference":[{"key":"2023041221071119400_","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.jtbi.2004.08.022","article-title":"Inference of signaling and gene regulatory networks by steady-state perturbation experiments: structure and accuracy","volume":"232","author":"Andrec","year":"2005","journal-title":"J Theor Biol"},{"key":"2023041221071119400_","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1038\/s41540-017-0019-y","article-title":"Reverse engineering highlights potential principles of large gene regulatory network design and learning","volume":"3","author":"Carr\u00e9","year":"2017","journal-title":"NPJ Syst Biol Appl"},{"key":"2023041221071119400_","doi-asserted-by":"crossref","first-page":"4079","DOI":"10.1093\/bioinformatics\/bty473","article-title":"Modelling signalling networks from perturbation data","volume":"34","author":"Dorel","year":"2018","journal-title":"Bioinformatics"},{"key":"2023041221071119400_","doi-asserted-by":"crossref","first-page":"e8","DOI":"10.1371\/journal.pbio.0050008","article-title":"Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles","volume":"5","author":"Faith","year":"2007","journal-title":"PLoS Biol"},{"key":"2023041221071119400_","doi-asserted-by":"crossref","first-page":"ra114","DOI":"10.1126\/scisignal.aae0535","article-title":"Integrating network reconstruction with mechanistic modeling to predict cancer therapies","volume":"9","author":"Halasz","year":"2016","journal-title":"Sci Signal"},{"key":"2023041221071119400_","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-387-84858-7","volume-title":"The Elements of Statistical Learning: Data Mining, Inference, and Prediction","author":"Hastie","year":"2009"},{"key":"2023041221071119400_","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/978-1-4939-8882-2_1","article-title":"Gene regulatory network inference: an introductory survey","volume":"1883","author":"Huynh-Thu","year":"2019","journal-title":"Methods Mol Biol"},{"key":"2023041221071119400_","doi-asserted-by":"crossref","first-page":"7272","DOI":"10.1038\/s41598-021-86544-0","article-title":"An R package for generic modular response analysis and its application to estrogen and retinoic acid receptor crosstalk","volume":"11","author":"Jimenez-Dominguez","year":"2021","journal-title":"Sci Rep"},{"key":"2023041221071119400_","doi-asserted-by":"crossref","first-page":"12841","DOI":"10.1073\/pnas.192442699","article-title":"Untangling the wires: a strategy to trace functional interactions in signaling and gene networks","volume":"99","author":"Kholodenko","year":"2002","journal-title":"Proc Natl Acad Sci USA"},{"key":"2023041221071119400_","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1042\/EBC20180012","article-title":"Reverse engineering gene regulatory networks by modular response analysis - a benchmark","volume":"62","author":"Klinger","year":"2018","journal-title":"Essays Biochem"},{"key":"2023041221071119400_","doi-asserted-by":"crossref","first-page":"S7","DOI":"10.1186\/1471-2105-7-S1-S7","article-title":"ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context","volume":"7","author":"Margolin","year":"2006","journal-title":"BMC Bioinformatics"},{"key":"2023041221071119400_","doi-asserted-by":"crossref","first-page":"e1009312","DOI":"10.1371\/journal.pcbi.1009312","article-title":"Application of modular response analysis to medium- to large-size biological systems","volume":"18","author":"Mekedem","year":"2022","journal-title":"PLoS Comput Biol"},{"key":"2023041221071119400_","doi-asserted-by":"crossref","first-page":"79879","DOI":"10.1155\/2007\/79879","article-title":"Information-theoretic inference of large transcriptional regulatory networks","volume":"2007","author":"Meyer","year":"2007","journal-title":"EURASIP J Bioinform Syst Biol"},{"key":"2023041221071119400_","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1186\/1471-2105-9-461","article-title":"minet: a R\/bioconductor package for inferring large transcriptional networks using mutual information","volume":"9","author":"Meyer","year":"2008","journal-title":"BMC Bioinformatics"},{"key":"2023041221071119400_","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1038\/ncb1543","article-title":"Growth factor-induced MAPK network topology shapes Erk response determining PC-12 cell fate","volume":"9","author":"Santos","year":"2007","journal-title":"Nat Cell Biol"},{"key":"2023041221071119400_","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1186\/1752-0509-7-57","article-title":"Integrating Bayesian variable selection with modular response analysis to infer biochemical network topology","volume":"7","author":"Santra","year":"2013","journal-title":"BMC Syst Biol"},{"key":"2023041221071119400_","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.coisb.2018.02.003","article-title":"Reconstructing static and dynamic models of signaling pathways using modular response analysis","volume":"9","author":"Santra","year":"2018","journal-title":"Curr Opin Syst Biol"},{"key":"2023041221071119400_","doi-asserted-by":"crossref","first-page":"16217","DOI":"10.1038\/s41598-018-34353-3","article-title":"Impact of measurement noise, experimental design, and estimation methods on modular response analysis based network reconstruction","volume":"8","author":"Thomaseth","year":"2018","journal-title":"Sci Rep"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btad166\/49784070\/btad166.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/39\/4\/btad166\/49871516\/btad166.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/39\/4\/btad166\/49871516\/btad166.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,10]],"date-time":"2023-12-10T04:09:03Z","timestamp":1702181343000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/doi\/10.1093\/bioinformatics\/btad166\/7109803"}},"subtitle":[],"editor":[{"given":"Janet","family":"Kelso","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2023,4,1]]},"references-count":18,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,4,3]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btad166","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2022.08.11.503572","asserted-by":"object"}]},"ISSN":["1367-4811"],"issn-type":[{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2023,4,1]]},"published":{"date-parts":[[2023,4,1]]},"article-number":"btad166"}}