ArticleComputational and structural biotechnology journal2023
Machine learning reveals STAT motifs as predictors for GR-mediated gene repression.
Article in Computational and structural biotechnology journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed, 3 citations in OpenAlex.
- Immune signaling as a determinant of cellular identity and tissue function.Frontiers in immunology · 2026Review
- Machine learning unveils RNA polymerase II binding as a predictor for SMAD2-dependent transcription dynamics in response to Actvin signalling.IET systems biology · 2024Article
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5 authors at 1 institution in 1 country.
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Abstract
Glucocorticoids are potent immunosuppressive drugs, but long-term treatment leads to severe side-effects. While there is a commonly accepted model for GR-mediated gene activation, the mechanism behind repression remains elusive. Understanding the molecular action of the glucocorticoid receptor (GR) mediated gene repression is the first step towards developing novel therapies. We devised an approach that combines multiple epigenetic assays with 3D chromatin data to find sequence patterns predicting gene expression change. We systematically tested> 100 models to evaluate the best way to integrate the data types and found that GR-bound regions hold most of the information needed to predict the polarity of Dex-induced transcriptional changes. We confirmed NF-κB motif family members as predictors for gene repression and identified STAT motifs as additional negative predictors.
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