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Genotype to phenotype via network analysis
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A prime objective of genomic medicine is the identification of disease-causing mutations and the mechanisms by which such events result in disease. As most disease phenotypes arise not from single genes and proteins but from a complex network of molecular interactions, a priori knowledge about the molecular network serves as a framework for biological inference and data mining. Here we review recent developments at the interface of biological networks and mutation analysis. We examine how mutations may be treated as a perturbation of the molecular interaction network and what insights may be gained from taking this perspective. We review work that aims to transform static networks into rich context-dependent networks and recent attempts to integrate non-coding RNAs into such analysis. Finally, we conclude with an overview of the many challenges and opportunities that lie ahead.
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“Many biological networks have a hierarchical structure such that their nodes can be organized into a relatively small collection of highly interconnected modules4,28,29”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [29]
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Metadata
- Authors
- Carter, H., Hofree, M., & Ideker, T.
- Affiliations
- University of California San Diego (US)
- Corresponding author
- Ideker, Trey
- Venue
- Current Opinion in Genetics & Development
- Publisher
- Elsevier BV
- Published
- 2013-12
- Volume
- 23
- Issue
- 6
- Pages
- 611-621
- ISSN
- 0959-437X (ISSN-L)
- PMC
- PMC3866044
- OpenAlex
- W1993702356
- Cited by
- 132
- References
- 96
- Citations by year
- 2026: 6 · 2025: 2 · 2024: 3 · 2023: 10 · 2022: 6 · 2021: 11 · 2020: 11 · 2019: 13 · 2018: 15 · 2017: 14 · 2016: 16 · 2015: 14 · 2014: 9 · 2013: 1
- FWCI
- 1.25
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Current Opinion in Genetics & Development (publishedVersion, cc-by-nc-sa)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- NIH (P41 GM103504, P50 GM085764)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Health Sciences › Medicine › Health Informatics › Biomedical Text Mining and Ontologies
- Keywords
- genotype-phenotype correlation, pathogenic variants, molecular interaction networks, network perturbation, non-coding RNA, genomic medicine, data mining
- MeSH terms
- Genotype, Humans, Models, Genetic, Mutation, Phenotype, Genetic Predisposition to Disease, Gene Regulatory Networks, Genetic Association Studies
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