The DOI matches its Crossref record; 103 of the 104 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Network biology: understanding the cell's functional organization
Barabási, A.-L., & Oltvai, Z. N. (2004) · Nature Reviews Genetics · doi:10.1038/nrg1272 · Cited by 7,903
Network motifs: simple building blocks of complex networks.
Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., & Alon, U. (2002) · Science · doi:10.1126/science.298.5594.824 · Cited by 7,368
Lethality and centrality in protein networks
Jeong, H., Mason, S., Barabási, A., & Oltvai, Z. (2001) · Nature · doi:10.1038/35075138 · Cited by 5,327
A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae
Uetz, P., Giot, L., Cagney, G., Mansfield, T. A., Judson, R. S., Knight, J. R., Lockshon, D., Narayan, V., Srinivasan, M., Pochart, P., et al. (2000) · Nature · doi:10.1038/35001009 · Cited by 4,706
The Gene Ontology (GO) database and informatics resource
Gene Ontology Consortium (2003) · Nucleic Acids Research · doi:10.1093/nar/gkh036 · Cited by 4,596
From molecular to modular cell biology.
Hartwell, L. H., Hopfield, J. J., Leibler, S., & Murray, A. W. (1999) · Nature · doi:10.1038/35011540 · Cited by 3,632
A comprehensive two-hybrid analysis to explore the yeast protein interactome
Ito, T., Chiba, T., Ozawa, R., Yoshida, M., Hattori, M., & Sakaki, Y. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.061034498 · Cited by 3,566
Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders
Hamosh, A. (2004) · Nucleic Acids Research · doi:10.1093/nar/gki033 · Cited by 3,443
Goh, K.-I., Cusick, M. E., Valle, D., Childs, B., Vidal, M., & Barabási, A.-L. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0701361104 · Cited by 3,242
A census of human cancer genes
Futreal, P. A., Coin, L., Marshall, M., Down, T., Hubbard, T., Wooster, R., Rahman, N., & Stratton, M. R. (2004) · Nature Reviews Cancer · doi:10.1038/nrc1299 · Cited by 3,184
Flagged first, then by citations
1 of the 104 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Items 801–817 of 817
Qian, P., Mu, X.-T., Su, B., Gao, L., & Zhang, D.-F. (2020) · Figshare · doi:10.6084/m9.figshare.13297297
Biomarkers and immunomodulatory effect of erythropoietin in postoperative ileus
Govindappa, P. K., Elfar, J. C., Gupta, Y. A., Ellur, G., Gaber, A. A., Rawat, M., & Elfar, W. (2026) · BMC Research Notes · doi:10.1186/s13104-026-07659-z
Fitime, L. F. (2016) · theses.fr (ABES) · doi:10.70675/3d12352dzfc13z4a07z87efzcd97b5b7b6f1
Valdeolivas, A. (2019) · theses.fr (ABES) · doi:10.70675/5379aa9cz8f24z45c6z8d81z2e0796525163
Chang, S., Zhang, B., Li, C., & Zhang, F. (2026) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-026-00842-7
The association between CD40 gene polymorphisms and the risk of cancer based on a meta-analysis
Li, S., Feng, X., Chen, A., Gui, J., Gu, C., Guo, C., Deng, Y., & Mi, Y. (2026) · Frontiers in Genetics · doi:10.3389/fgene.2026.1735761
Membrane-Traversing Peptides: Mechanistic Landscapes, Assays, and Applications
Taveras, A. J., Kuang, E. A., Ferrie, R. P., Ellis, A., Tait, A., Salam, R., Hristova, K. A., & Wimley, W. C. (2026) · Chemical Reviews · doi:10.1021/acs.chemrev.6c00060
LiverDCP: A Disease–Cell–Protein Framework for Multi-scale Modeling of Disease BiologyPreprint
Shi, Z., Song, Z., Steveson-Lerner, H., Dong, B., & Zhao, H. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.07.743628 · Cited by 1
MS-Based Proteomics Approach in Mapping Protein–Protein Interaction
Njoku, V., Priest, B., & Darie, C. C. (2026) · Advances in experimental medicine and biology · doi:10.1007/978-3-032-29437-1_29
Molecular Networks and Complex Diseases
Koyutürk, M., Erten, S., Chowdhury, S. A., Nibbe, R. K., & Chance, M. R. (2011) · Springer eBooks · doi:10.1007/978-1-4614-0320-3_7
Roscoe, B. P. (2014) · The Journal of the American Medical Association (JAMA) Network (American Medical Association) · doi:10.13028/m2g027
Global characterization of Arabidopsis protein interactome
Yang, J., Wang, J., Hu, Y.-L., Tang, Y., Hou, X., & Li, X. (2013) · Plant Omics
Metadata
- 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
- Health Sciences › Medicine › Health Informatics › 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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
