{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T20:40:23Z","timestamp":1790628023990,"version":"4.1.0"},"reference-count":12,"publisher":"Oxford University Press (OUP)","issue":"14","license":[{"start":{"date-parts":[[2020,11,2]],"date-time":"2020-11-02T00:00:00Z","timestamp":1604275200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"European Union\u2019s Horizon 2020 Research and Innovation Program","award":["668858"],"award-info":[{"award-number":["668858"]}]},{"name":"European Union\u2019s Horizon 2020 Research and Innovation Program","award":["826121"],"award-info":[{"award-number":["826121"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,8,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Summary<\/jats:title>\n                  <jats:p>The advent of high-throughput technologies has provided researchers with measurements of thousands of molecular entities and enable the investigation of the internal regulatory apparatus of the cell. However, network inference from high-throughput data is far from being a solved problem. While a plethora of different inference methods have been proposed, they often lead to non-overlapping predictions, and many of them lack user-friendly implementations to enable their broad utilization. Here, we present Consensus Interaction Network Inference Service (COSIFER), a package and a companion web-based platform to infer molecular networks from expression data using state-of-the-art consensus approaches. COSIFER includes a selection of state-of-the-art methodologies for network inference and different consensus strategies to integrate the predictions of individual methods and generate robust networks.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>COSIFER Python source code is available at https:\/\/github.com\/PhosphorylatedRabbits\/cosifer. The web service is accessible at https:\/\/ibm.biz\/cosifer-aas.<\/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\/btaa942","type":"journal-article","created":{"date-parts":[[2020,10,26]],"date-time":"2020-10-26T12:10:49Z","timestamp":1603714249000},"page":"2070-2072","source":"Crossref","is-referenced-by-count":8,"title":["COSIFER: a Python package for the consensus inference of molecular interaction networks"],"prefix":"10.1093","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8872-0269","authenticated-orcid":false,"given":"Matteo","family":"Manica","sequence":"first","affiliation":[{"name":"Cognitive Computing and Industry Solutions, IBM Research Europe , R\u00fcschlikon, ZH 8803, Switzerland"},{"name":"Institute of Molecular Systems Biology, ETH Z\u00fcrich , Z\u00fcrich, ZH 8093, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Charlotte","family":"Bunne","sequence":"additional","affiliation":[{"name":"Cognitive Computing and Industry Solutions, IBM Research Europe , R\u00fcschlikon, ZH 8803, Switzerland"},{"name":"Institute for Machine Learning, ETH Z\u00fcrich , Z\u00fcrich, ZH 8092, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roland","family":"Mathis","sequence":"additional","affiliation":[{"name":"Cognitive Computing and Industry Solutions, IBM Research Europe , R\u00fcschlikon, ZH 8803, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joris","family":"Cadow","sequence":"additional","affiliation":[{"name":"Cognitive Computing and Industry Solutions, IBM Research Europe , R\u00fcschlikon, ZH 8803, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mehmet Eren","family":"Ahsen","sequence":"additional","affiliation":[{"name":"Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai , New York, NY 10029-5674, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gustavo A","family":"Stolovitzky","sequence":"additional","affiliation":[{"name":"Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai , New York, NY 10029-5674, USA"},{"name":"Translational Systems Biology and Nanobiotechnology, IBM T.J. Watson Research Center , Yorktown Heights, NY 10598, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mar\u00eda Rodr\u00edguez","family":"Mart\u00ednez","sequence":"additional","affiliation":[{"name":"Cognitive Computing and Industry Solutions, IBM Research Europe , R\u00fcschlikon, ZH 8803, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2020,11,2]]},"reference":[{"key":"2023061310291526000_btaa942-B1","first-page":"1","article-title":"Unsupervised evaluation and weighted aggregation of ranked classification predictions","volume":"20","author":"Ahsen","year":"2019","journal-title":"J. Mach. Learn. 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