{"id":3912,"date":"2019-02-06T13:41:58","date_gmt":"2019-02-06T12:41:58","guid":{"rendered":"http:\/\/www.igmm.cnrs.fr\/?p=3912"},"modified":"2019-02-06T13:47:34","modified_gmt":"2019-02-06T12:47:34","slug":"machine-learning-to-probe-transcription-factor-combinatorics","status":"publish","type":"post","link":"https:\/\/www.igmm.cnrs.fr\/en\/machine-learning-to-probe-transcription-factor-combinatorics\/","title":{"rendered":"Machine learning to probe transcription factor combinatorics"},"content":{"rendered":"

Transcription factors (TFs) act in a combinatorial manner competing and collaborating to regulate their target genes. Several questions remain regarding the conservation of these combinations among different gene classes, regulatory regions and cell types. Here we propose a new machine learning approach able to infer these combinations of TFs. This approach demonstrates that TF combinatorics are conserved for different gene classes (mRNA, miRNA and lncRNA) in different cell types but are specific to regulatory sequences (enhancers and promoters).<\/p>\n

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Probing transcription factor combinatorics in different promoter classes and in enhancers. Vandel J., Cassan O., Lebre S., Lecellier CH*, Brehelin L*. BMC Genomics, in press ; doi : https :\/\/doi.org\/10.1101\/197418<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Transcription factors (TFs) act in a combinatorial manner competing and collaborating to regulate their target genes. Several questions remain regarding the conservation of these combinations among different gene classes, regulatory regions and cell types. Here we propose a new machine learning approach able to infer these combinations of TFs. This approach demonstrates that TF combinatorics are conserved for different gene … Continue reading Machine learning to probe transcription factor combinatorics<\/span><\/a><\/p>\n","protected":false},"author":4,"featured_media":3908,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4,16],"tags":[],"class_list":["post-3912","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-general-news","category-science-en"],"_links":{"self":[{"href":"https:\/\/www.igmm.cnrs.fr\/en\/wp-json\/wp\/v2\/posts\/3912","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.igmm.cnrs.fr\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.igmm.cnrs.fr\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.igmm.cnrs.fr\/en\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/www.igmm.cnrs.fr\/en\/wp-json\/wp\/v2\/comments?post=3912"}],"version-history":[{"count":1,"href":"https:\/\/www.igmm.cnrs.fr\/en\/wp-json\/wp\/v2\/posts\/3912\/revisions"}],"predecessor-version":[{"id":3913,"href":"https:\/\/www.igmm.cnrs.fr\/en\/wp-json\/wp\/v2\/posts\/3912\/revisions\/3913"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.igmm.cnrs.fr\/en\/wp-json\/wp\/v2\/media\/3908"}],"wp:attachment":[{"href":"https:\/\/www.igmm.cnrs.fr\/en\/wp-json\/wp\/v2\/media?parent=3912"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.igmm.cnrs.fr\/en\/wp-json\/wp\/v2\/categories?post=3912"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.igmm.cnrs.fr\/en\/wp-json\/wp\/v2\/tags?post=3912"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}