Protein networks in disease
Open accessAbstract
During a decade of proof-of-principle analysis in model organisms, protein networks have been used to further the study of molecular evolution, to gain insight into the robustness of cells to perturbation, and for assignment of new protein functions. Following these analyses, and with the recent rise of protein interaction measurements in mammals, protein networks are increasingly serving as tools to unravel the molecular basis of disease. We review promising applications of protein networks to disease in four major areas: identifying new disease genes; the study of their network properties; identifying disease-related subnetworks; and network-based disease classification. Applications in infectious disease, personalized medicine, and pharmacology are also forthcoming as the available protein network information improves in quality and coverage.
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Human disease genes tend to interact with each other with higher network connectivity than non-disease genes.
“Human disease genes tend to interact with each other with higher network connectivity than non-disease genes [71].”
From An integrated analysis of genes and functional pathways for aggression in human and rodent models (2019) · cites this paper as [71]
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Wang, J., Zhang, S., Wang, Y., Chen, L., & Zhang, X. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000521 · Cited by 85 · see citing works
Comparative assessment of differential network analysis methods
Lichtblau, Y., Zimmermann, K., Haldemann, B., Lenze, D., Hummel, M., & Leser, U. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw061 · Cited by 76 · see citing works
The proteostasis boundary in misfolding diseases of membrane traffic
Hutt, D. M., Powers, E. T., & Balch, W. E. (2009) · FEBS Letters · doi:10.1016/j.febslet.2009.07.014 · Cited by 85 · see citing works
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- Authors
- Ideker, T., & Sharan, R.
- Affiliations
- University of California San Diego (US)
- Tel Aviv University (IL)
- Venue
- Genome Research
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2008-04
- Volume
- 18
- Issue
- 4
- Pages
- 644-652
- ISSN
- 1088-9051 (ISSN-L)
- PMC
- PMC3863981
- OpenAlex
- W2100025044
- Cited by
- 817
- References
- 104
- Citations by year
- 2026: 7 · 2025: 16 · 2024: 21 · 2023: 30 · 2022: 15 · 2021: 33 · 2020: 50 · 2019: 46 · 2018: 40 · 2017: 42 · 2016: 43 · 2015: 64 · 2014: 61 · 2013: 81 · 2012: 77
- Type
- posted content
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- public-domain
- Locations
- Genome Research (publishedVersion, public-domain)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Israel Science Foundation
- National Institutes of Health
- National Institute of General Medical Sciences (GM 070743, R01GM070743)
- National Institute of Environmental Health Sciences (ES14811, R01ES014811)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- protein-protein interaction network, disease-associated genes, network characteristics, molecular basis of disease, protein function prediction, molecular evolution, infectious diseases, personalized medicine
- MeSH terms
- Disease, Fungal Proteins, Humans, Huntington Disease, Phenotype, Yeasts, Gene Expression Regulation, Neoplastic, Case-Control Studies, Genetic Predisposition to Disease, Protein Interaction Mapping, Genetic Diseases, Inborn, Gene Regulatory Networks
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.
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