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Gene regulatory networks in disease and ageing.
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- Author(s): Unger Avila P;Unger Avila P; Padvitski T; Padvitski T; Leote AC; Leote AC; Chen H; Chen H; Chen H; Saez-Rodriguez J; Saez-Rodriguez J; Kann M; Kann M; Kann M; Beyer A; Beyer A; Beyer A; Beyer A
- Source:
Nature reviews. Nephrology [Nat Rev Nephrol] 2024 Sep; Vol. 20 (9), pp. 616-633. Date of Electronic Publication: 2024 Jun 12.- Publication Type:
Journal Article; Review- Language:
English - Source:
- Additional Information
- Source: Publisher: Nature Pub. Group Country of Publication: England NLM ID: 101500081 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1759-507X (Electronic) Linking ISSN: 17595061 NLM ISO Abbreviation: Nat Rev Nephrol Subsets: MEDLINE
- Publication Information: Original Publication: London Nature Pub. Group
- Subject Terms:
- Abstract: The precise control of gene expression is required for the maintenance of cellular homeostasis and proper cellular function, and the declining control of gene expression with age is considered a major contributor to age-associated changes in cellular physiology and disease. The coordination of gene expression can be represented through models of the molecular interactions that govern gene expression levels, so-called gene regulatory networks. Gene regulatory networks can represent interactions that occur through signal transduction, those that involve regulatory transcription factors, or statistical models of gene-gene relationships based on the premise that certain sets of genes tend to be coexpressed across a range of conditions and cell types. Advances in experimental and computational technologies have enabled the inference of these networks on an unprecedented scale and at unprecedented precision. Here, we delineate different types of gene regulatory networks and their cell-biological interpretation. We describe methods for inferring such networks from large-scale, multi-omics datasets and present applications that have aided our understanding of cellular ageing and disease mechanisms.
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Lecca, P. Machine learning for causal inference in biological networks: perspectives of this challenge. Front. Bioinform 1, 746712 (2021). (PMID: 36303798958101010.3389/fbinf.2021.746712) - Publication Date: Date Created: 20240612 Date Completed: 20240820 Latest Revision: 20240822
- Publication Date: 20240823
- Accession Number: 10.1038/s41581-024-00849-7
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