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 in which nodes can be organized into a relatively small collection of highly interconnected modules.
“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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- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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