{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T17:45:56Z","timestamp":1760550356331,"version":"3.41.0"},"reference-count":13,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2017,8,31]],"date-time":"2017-08-31T00:00:00Z","timestamp":1504137600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/about_us\/legal\/notices"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,1,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Summary<\/jats:title><jats:p>Gene expression alterations and potentially underlying gene copy number mutations can be measured routinely in the wet lab, but it is still extremely challenging to quantify impacts of altered genes on clinically relevant characteristics to predict putative driver genes. We developed the R package regNet that utilizes gene expression and copy number data to learn regulatory networks for the quantification of potential impacts of individual gene expression alterations on user-defined target genes via network propagation. We demonstrate the value of regNet by identifying putative major regulators that distinguish pilocytic from diffuse astrocytomas and by predicting putative impacts of glioblastoma-specific gene copy number alterations on cell cycle pathway genes and patient survival.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>regNet is available for download at https:\/\/github.com\/seifemi\/regNet under GNU GPL-3.<\/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\/btx544","type":"journal-article","created":{"date-parts":[[2017,8,30]],"date-time":"2017-08-30T19:10:58Z","timestamp":1504120258000},"page":"308-311","source":"Crossref","is-referenced-by-count":13,"title":["regNet: an R package for network-based propagation of gene expression alterations"],"prefix":"10.1093","volume":"34","author":[{"given":"Michael","family":"Seifert","sequence":"first","affiliation":[{"name":"Carl Gustav Carus Faculty of Medicine, Institute for Medical Informatics and Biometry (IMB), Technische Universit\u00e4t Dresden, D-01307 Dresden, Germany"},{"name":"National Center for Tumor Diseases (NCT), Dresden, Germany"}]},{"given":"Andreas","family":"Beyer","sequence":"additional","affiliation":[{"name":"Cellular Networks and Systems Biology, CECAD, University of Cologne, D-50931 Cologne, Germany"}]}],"member":"286","published-online":{"date-parts":[[2017,8,31]]},"reference":[{"key":"2023012712232646200_btx544-B1","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1038\/nature11003","article-title":"The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity","volume":"483","author":"Barretina","year":"2012","journal-title":"Nature"},{"key":"2023012712232646200_btx544-B2","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: a practical and powerful approach to multiple testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. 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