The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Associating Genes and Protein Complexes with Disease via Network Propagation
Open accessAbstract
A fundamental challenge in human health is the identification of disease-causing genes. Recently, several studies have tackled this challenge via a network-based approach, motivated by the observation that genes causing the same or similar diseases tend to lie close to one another in a network of protein-protein or functional interactions. However, most of these approaches use only local network information in the inference process and are restricted to inferring single gene associations. Here, we provide a global, network-based method for prioritizing disease genes and inferring protein complex associations, which we call PRINCE. The method is based on formulating constraints on the prioritization function that relate to its smoothness over the network and usage of prior information. We exploit this function to predict not only genes but also protein complex associations with a disease of interest. We test our method on gene-disease association data, evaluating both the prioritization achieved and the protein complexes inferred. We show that our method outperforms extant approaches in both tasks. Using data on 1,369 diseases from the OMIM knowledgebase, our method is able (in a cross validation setting) to rank the true causal gene first for 34% of the diseases, and infer 139 disease-related complexes that are highly coherent in terms of the function, expression and conservation of their member proteins. Importantly, we apply our method to study three multi-factorial diseases for which some causal genes have been found already: prostate cancer, alzheimer and type 2 diabetes mellitus. PRINCE's predictions for these diseases highly match the known literature, suggesting several novel causal genes and protein complexes for further investigation.
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.
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
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
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
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
Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders
Hamosh, A. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.1.52 · Cited by 762
Franke, L., Bakel, H. V., Fokkens, L., de Jong, E. D., Egmont-Petersen, M., & Wijmenga, C. (2006) · The American Journal of Human Genetics · doi:10.1086/504300 · Cited by 476
Cited by
Network medicine: a network-based approach to human disease
Barabási, A.-L., Gulbahce, N., & Loscalzo, J. (2011) · Nature Reviews Genetics · doi:10.1038/nrg2918 · Cited by 5,034
Systematic Evaluation of Molecular Networks for Discovery of Disease Genes
Huang, J. K., Carlin, D. E., Yu, M. K., Zhang, W., Kreisberg, J. F., Tamayo, P., & Ideker, T. (2018) · Cell Systems · doi:10.1016/j.cels.2018.03.001 · Cited by 289
Prediction and Validation of Disease Genes Using HeteSim Scores
Zeng, X., Liao, Y., Liu, Y., & Zou, Q. (2017) · IEEE/ACM Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2016.2520947 · Cited by 208
Xu, R., Wang, Q., & Li, L. (2015) · BMC Genomics · doi:10.1186/1471-2164-16-s7-s4 · Cited by 193
Rac2 Controls Tumor Growth, Metastasis and M1-M2 Macrophage Differentiation In Vivo
Joshi, S., Singh, A. R., Zulcic, M., Bao, L., Messer, K., Ideker, T., Dutkowski, J., & Durden, D. L. (2014) · PLoS ONE · doi:10.1371/journal.pone.0095893 · Cited by 128
“We then applied a network propagation algorithm similar to Google’s PageRank [61,62] which seeks to identify novel candidate genes using the ‘‘guilt by association’’ principle, i.”
MethodNetwork Medicine in Pathobiology
Lee, L. Y.-H., & Loscalzo, J. (2019) · The American Journal of Pathology · doi:10.1016/j.ajpath.2019.03.009 · Cited by 95
Network-based characterization of drug-regulated genes, drug targets, and toxicity
Kotlyar, M., Fortney, K., & Jurisica, I. (2012) · Methods · doi:10.1016/j.ymeth.2012.06.003 · Cited by 92
Reconstruction of Cell-type-Specific Interactomes at Single-Cell Resolution
Mohammadi, S., Davila-Velderrain, J., & Kellis, M. (2019) · Cell Systems · doi:10.1016/j.cels.2019.10.007 · Cited by 73
A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases
Zolotareva, O., & Kleine, M. (2019) · Journal of Integrative Bioinformatics · doi:10.1515/jib-2018-0069 · Cited by 46
Reverse engineering and analysis of large genome-scale gene networks
Aluru, M., Zola, J., Nettleton, D., & Aluru, S. (2013) · Nucleic Acids Research · doi:10.1093/nar/gks904 · Cited by 34
State of the interactomes: an evaluation of molecular networks for generating biological insights
Wright, S. N., Colton, S., Schaffer, L. V., Pillich, R. T., Churas, C., Pratt, D., & Ideker, T. (2024) · Molecular Systems Biology · doi:10.1038/s44320-024-00077-y · Cited by 32
Tumor stratification by a novel graph-regularized bi-clique finding algorithm
Ahmadi Adl, A., & Qian, X. (2015) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2015.02.010 · Cited by 3
Metadata
- Authors
- Vanunu, O., Magger, O., Ruppin, E., Shlomi, T., & Sharan, R.
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2010-01-15
- Volume
- 6
- Issue
- 1
- Pages
- e1000641
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- OpenAlex
- W2038122249
- Cited by
- 872
- References
- 6
- FWCI
- 17.60
- Type
- journal article
- Language
- en
- Open access
- gold
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
