{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T18:44:39Z","timestamp":1773773079565,"version":"3.50.1"},"reference-count":28,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2019,9,3]],"date-time":"2019-09-03T00:00:00Z","timestamp":1567468800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"ERC Advanced","award":["693174"],"award-info":[{"award-number":["693174"]}]},{"name":"Data-Driven Genomic Computing"},{"name":"Italian Association for Cancer Research-AIRC","award":["IG 21663"],"award-info":[{"award-number":["IG 21663"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,2,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Genome regulatory networks have different layers and ways to modulate cellular processes, such as cell differentiation, proliferation, and adaptation to external stimuli. Transcription factors and other chromatin-associated proteins act as combinatorial protein complexes that control gene transcription. Thus, identifying functional interaction networks among these proteins is a fundamental task to understand the genome regulation framework.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We developed a novel approach to infer interactions among transcription factors in user-selected genomic regions, by combining the computation of association rules and of a novel Importance Index on ChIP-seq datasets. The hallmark of our method is the definition of the Importance Index, which provides a relevance measure of the interaction among transcription factors found associated in the computed rules. Examples on synthetic data explain the index use and potential. A straightforward pre-processing pipeline enables the easy extraction of input data for our approach from any set of ChIP-seq experiments. Applications on ENCODE ChIP-seq data prove that our approach can reliably detect interactions between transcription factors, including known interactions that validate our approach.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>A R\/Bioconductor package implementing our association rules and Importance Index-based method is available at http:\/\/bioconductor.org\/packages\/release\/bioc\/html\/TFARM.html.<\/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\/btz687","type":"journal-article","created":{"date-parts":[[2019,8,29]],"date-time":"2019-08-29T19:28:43Z","timestamp":1567106923000},"page":"1007-1013","source":"Crossref","is-referenced-by-count":18,"title":["Association rule mining to identify transcription factor interactions in genomic regions"],"prefix":"10.1093","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9512-7781","authenticated-orcid":false,"given":"Gaia","family":"Ceddia","sequence":"first","affiliation":[{"name":"Dipartimento di Elettronica, Informazione e Bioingegneria , Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liuba Nausicaa","family":"Martino","sequence":"additional","affiliation":[{"name":"MOX - Dipartimento di Matematica, Politecnico di Milano , Milan 20133, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alice","family":"Parodi","sequence":"additional","affiliation":[{"name":"MOX - Dipartimento di Matematica, Politecnico di Milano , Milan 20133, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Piercesare","family":"Secchi","sequence":"additional","affiliation":[{"name":"MOX - Dipartimento di Matematica, Politecnico di Milano , Milan 20133, Italy"},{"name":"Center for Analysis, Decisions and Society, Human Technopole , Milan 20157, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stefano","family":"Campaner","sequence":"additional","affiliation":[{"name":"Center for Genomic Science of IIT@SEMM, Istituto Italiano di Tecnologia (IIT) , Milan 20139, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marco","family":"Masseroli","sequence":"additional","affiliation":[{"name":"Dipartimento di Elettronica, Informazione e Bioingegneria , Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2019,9,3]]},"reference":[{"key":"2023013110104336900_btz687-B1","first-page":"995","article-title":"Identifying hotspots in lung cancer data using association rule mining","author":"Agrawal","year":"2011","journal-title":"Proceedings of ICDMW11"},{"key":"2023013110104336900_btz687-B2","first-page":"487","article-title":"Fast algorithms for mining association rules in large databases","author":"Agrawal","year":"1994","journal-title":"Proceedings of VLDB94"},{"key":"2023013110104336900_btz687-B3","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.1126\/science.2006410","article-title":"Max: a helix-loop-helix zipper protein that forms a sequence-specific DNA-binding complex with Myc","volume":"251","author":"Blackwood","year":"1991","journal-title":"Science"},{"key":"2023013110104336900_btz687-B4","doi-asserted-by":"crossref","first-page":"2812","DOI":"10.1039\/C3AY41907J","article-title":"Principal component analysis","volume":"6","author":"Bro","year":"2014","journal-title":"Anal. 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