{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T08:53:14Z","timestamp":1772268794054,"version":"3.50.1"},"reference-count":79,"publisher":"Oxford University Press (OUP)","issue":"17","license":[{"start":{"date-parts":[[2016,11,10]],"date-time":"2016-11-10T00:00:00Z","timestamp":1478736000000},"content-version":"vor","delay-in-days":73,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DBI-1356505"],"award-info":[{"award-number":["DBI-1356505"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100004897","name":"Pennsylvania Department of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004897","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100004897","name":"Pennsylvania Department of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004897","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>Reconstructing regulatory networks from expression and interaction data is a major goal of systems biology. While much work has focused on trying to experimentally and computationally determine the set of transcription-factors (TFs) and microRNAs (miRNAs) that regulate genes in these networks, relatively little work has focused on inferring the regulation of miRNAs by TFs. Such regulation can play an important role in several biological processes including development and disease. The main challenge for predicting such interactions is the very small positive training set currently available. Another challenge is the fact that a large fraction of miRNAs are encoded within genes making it hard to determine the specific way in which they are regulated.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>To enable genome wide predictions of TF\u2013miRNA interactions, we extended semi-supervised machine-learning approaches to integrate a large set of different types of data including sequence, expression, ChIP-seq and epigenetic data. As we show, the methods we develop achieve good performance on both a labeled test set, and when analyzing general co-expression networks. We next analyze mRNA and miRNA cancer expression data, demonstrating the advantage of using the predicted set of interactions for identifying more coherent and relevant modules, genes, and miRNAs. The complete set of predictions is available on the supporting website and can be used by any method that combines miRNAs, genes, and TFs.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and Implementation<\/jats:title><jats:p>Code and full set of predictions are available from the supporting website: http:\/\/cs.cmu.edu\/~mruffalo\/tf-mirna\/.<\/jats:p><\/jats:sec><jats:sec><jats:title>Contact<\/jats:title><jats:p>zivbj@cs.cmu.edu<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btw452","type":"journal-article","created":{"date-parts":[[2016,9,1]],"date-time":"2016-09-01T07:53:39Z","timestamp":1472716419000},"page":"i746-i754","source":"Crossref","is-referenced-by-count":10,"title":["Genome wide predictions of miRNA regulation by transcription factors"],"prefix":"10.1093","volume":"32","author":[{"given":"Matthew","family":"Ruffalo","sequence":"first","affiliation":[{"name":"Department of Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA 15213"}]},{"given":"Ziv","family":"Bar-Joseph","sequence":"additional","affiliation":[{"name":"Department of Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA 15213"}]}],"member":"286","published-online":{"date-parts":[[2016,8,29]]},"reference":[{"key":"2023020113395654300_btw452-B1","doi-asserted-by":"crossref","first-page":"e05005.","DOI":"10.7554\/eLife.05005","article-title":"Predicting effective microrna target sites in mammalian mrnas","volume":"4","author":"Agarwal","year":"2015","journal-title":"Elife"},{"key":"2023020113395654300_btw452-B2","doi-asserted-by":"crossref","first-page":"e64273.","DOI":"10.1371\/journal.pone.0064273","article-title":"mir-506 regulates epithelial mesenchymal transition in breast cancer cell lines","volume":"8","author":"Arora","year":"2013","journal-title":"PloS One"},{"key":"2023020113395654300_btw452-B3","doi-asserted-by":"crossref","first-page":"e1004663","DOI":"10.1371\/journal.pgen.1004663","article-title":"Methylation qtls are associated with coordinated changes in transcription factor binding, histone modifications, and gene expression levels","volume":"10","author":"Banovich","year":"2014","journal-title":"PLoS Genet"},{"key":"2023020113395654300_btw452-B4","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/S0092-8674(04)00045-5","article-title":"MicroRNAs: genomics, biogenesis, mechanism, and function","volume":"116","author":"Bartel","year":"2004","journal-title":"Cell"},{"key":"2023020113395654300_btw452-B5","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1210\/mend-2-6-543","article-title":"Expression of transforming growth factor \u03b1 and its messenger ribonucleic acid in human breast cancer: its regulation by estrogen and its possible functional significance","volume":"2","author":"Bates","year":"1988","journal-title":"Mol. 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