{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T14:10:53Z","timestamp":1775916653291,"version":"3.50.1"},"reference-count":9,"publisher":"Oxford University Press (OUP)","issue":"13","license":[{"start":{"date-parts":[[2017,12,29]],"date-time":"2017-12-29T00:00:00Z","timestamp":1514505600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/about_us\/legal\/notices"}],"funder":[{"name":"Labex celtisphybio and Region IdF"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Summary<\/jats:title>\n                  <jats:p>We present a web server running the MIIC algorithm, a network learning method combining constraint-based and information-theoretic frameworks to reconstruct causal, non-causal or mixed networks from non-perturbative data, without the need for an a priori choice on the class of reconstructed network. Starting from a fully connected network, the algorithm first removes dispensable edges by iteratively subtracting the most significant information contributions from indirect paths between each pair of variables. The remaining edges are then filtered based on their confidence assessment or oriented based on the signature of causality in observational data. MIIC online server can be used for a broad range of biological data, including possible unobserved (latent) variables, from single-cell gene expression data to protein sequence evolution and outperforms or matches state-of-the-art methods for either causal or non-causal network reconstruction.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>MIIC online can be freely accessed at https:\/\/miic.curie.fr.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btx844","type":"journal-article","created":{"date-parts":[[2017,12,28]],"date-time":"2017-12-28T20:10:43Z","timestamp":1514491843000},"page":"2311-2313","source":"Crossref","is-referenced-by-count":11,"title":["MIIC online: a web server to reconstruct causal or non-causal networks from non-perturbative data"],"prefix":"10.1093","volume":"34","author":[{"given":"Nadir","family":"Sella","sequence":"first","affiliation":[{"name":"Institut Curie, PSL Research University, CNRS UMR168"},{"name":"Sorbonne Universit\u00e9s, Faculty of Science and Engineering, UPMC Univ Paris 06, Paris, France"}]},{"given":"Louis","family":"Verny","sequence":"additional","affiliation":[{"name":"Institut Curie, PSL Research University, CNRS UMR168"},{"name":"Sorbonne Universit\u00e9s, Faculty of Science and Engineering, UPMC Univ Paris 06, Paris, France"}]},{"given":"Guido","family":"Uguzzoni","sequence":"additional","affiliation":[{"name":"Institut Curie, PSL Research University, CNRS UMR168"},{"name":"Sorbonne Universit\u00e9s, Faculty of Science and Engineering, UPMC Univ Paris 06, Paris, France"}]},{"given":"S\u00e9verine","family":"Affeldt","sequence":"additional","affiliation":[{"name":"Institut Curie, PSL Research University, CNRS UMR168"},{"name":"Sorbonne Universit\u00e9s, Faculty of Science and Engineering, UPMC Univ Paris 06, Paris, France"}]},{"given":"Herv\u00e9","family":"Isambert","sequence":"additional","affiliation":[{"name":"Institut Curie, PSL Research University, CNRS UMR168"},{"name":"Sorbonne Universit\u00e9s, Faculty of Science and Engineering, UPMC Univ Paris 06, Paris, France"}]}],"member":"286","published-online":{"date-parts":[[2017,12,29]]},"reference":[{"key":"2023051604212063400_btx844-B1","doi-asserted-by":"crossref","first-page":"12.","DOI":"10.1186\/s12859-015-0856-x","article-title":"3off2: a network reconstruction algorithm based on 2-point and 3-point information statistics","volume":"17","author":"Affeldt","year":"2016","journal-title":"BMC Bioinformatics"},{"key":"2023051604212063400_btx844-B2","author":"Affeldt","year":"2015"},{"key":"2023051604212063400_btx844-B3","doi-asserted-by":"crossref","first-page":"012707.","DOI":"10.1103\/PhysRevE.87.012707","article-title":"Improved contact prediction in proteins: using pseudolikelihoods to infer Potts models","volume":"87","author":"Ekeberg","year":"2013","journal-title":"Phys. Rev. E"},{"key":"2023051604212063400_btx844-B4","doi-asserted-by":"crossref","first-page":"5822","DOI":"10.1073\/pnas.1610609114","article-title":"Reconstructing blood stem cell regulatory network models from single-cell molecular profiles","volume":"114","author":"Hamey","year":"2017","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023051604212063400_btx844-B5","doi-asserted-by":"crossref","first-page":"e28766.","DOI":"10.1371\/journal.pone.0028766","article-title":"Protein 3D structure computed from evolutionary sequence variation","volume":"6","author":"Marks","year":"2011","journal-title":"PLoS One"},{"key":"2023051604212063400_btx844-B6","doi-asserted-by":"crossref","first-page":"E1293","DOI":"10.1073\/pnas.1111471108","article-title":"Direct-coupling analysis of residue coevolution captures native contacts across many protein families","volume":"108","author":"Morcos","year":"2011","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023051604212063400_btx844-B7","doi-asserted-by":"crossref","first-page":"e1005662.","DOI":"10.1371\/journal.pcbi.1005662","article-title":"Learning causal networks with latent variables from multivariate information in genomic data","volume":"13","author":"Verny","year":"2017","journal-title":"PLoS Comput. Biol"},{"key":"2023051604212063400_btx844-B8","doi-asserted-by":"crossref","first-page":"e67434.","DOI":"10.1371\/journal.pone.0067434","article-title":"LegumeGRN: a gene regulatory network prediction server for functional and comparative studies","volume":"8","author":"Wang","year":"2013","journal-title":"PloS One"},{"key":"2023051604212063400_btx844-B9","doi-asserted-by":"crossref","first-page":"2801","DOI":"10.1093\/bioinformatics\/btt472","article-title":"Bayesian Network Webserver: a comprehensive tool for biological network modeling","volume":"29","author":"Ziebarth","year":"2013","journal-title":"Bioinformatics"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/34\/13\/2311\/50316010\/bioinformatics_34_13_2311.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/34\/13\/2311\/50316010\/bioinformatics_34_13_2311.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,16]],"date-time":"2023-05-16T04:22:25Z","timestamp":1684210945000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/34\/13\/2311\/4781692"}},"subtitle":[],"editor":[{"given":"Jonathan","family":"Wren","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2017,12,29]]},"references-count":9,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2018,7,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btx844","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2018,7,1]]},"published":{"date-parts":[[2017,12,29]]}}}