The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.
The power of protein interaction networks for associating genes with diseases
Open accessAbstract
Motivation: Understanding the association between genetic diseases and their causal genes is an important problem concerning human health. With the recent influx of high-throughput data describing interactions between gene products, scientists have been provided a new avenue through which these associations can be inferred. Despite the recent interest in this problem, however, there is little understanding of the relative benefits and drawbacks underlying the proposed techniques. Results: We assessed the utility of physical protein interactions for determining gene–disease associations by examining the performance of seven recently developed computational methods (plus several of their variants). We found that random-walk approaches individually outperform clustering and neighborhood approaches, although most methods make predictions not made by any other method. We show how combining these methods into a consensus method yields Pareto optimal performance. We also quantified how a diffuse topological distribution of disease-related proteins negatively affects prediction quality and are thus able to identify diseases especially amenable to network-based predictions and others for which additional information sources are absolutely required. Availability: The predictions made by each algorithm considered are available online at http://www.cbcb.umd.edu/DiseaseNet Contact: carlk@cs.umd.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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Barabási, A.-L., Gulbahce, N., & Loscalzo, J. (2011) · Nature Reviews Genetics · doi:10.1038/nrg2918 · Cited by 5,034
Luo, Y., Zhao, X., Zhou, J., Yang, J., Zhang, Y., Kuang, W., Peng, J., Chen, L., & Zeng, J. (2017) · Nature Communications · doi:10.1038/s41467-017-00680-8 · Cited by 916
Structure and dynamics of molecular networks: A novel paradigm of drug discovery
Csermely, P., Korcsmáros, T., Kiss, H. J. M., London, G., & Nussinov, R. (2013) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2013.01.016 · Cited by 892
Network propagation: a universal amplifier of genetic associations
Cowen, L., Ideker, T., Raphael, B. J., & Sharan, R. (2017) · Nature Reviews Genetics · doi:10.1038/nrg.2017.38 · Cited by 657
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Metadata
- Authors
- Navlakha, S., & Kingsford, C.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2010-02-24
- Volume
- 26
- Issue
- 8
- Pages
- 1057-1063
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2110684048
- Cited by
- 358
- References
- 50
- FWCI
- 10.52
- Type
- journal article
- Language
- en
- Open access
- hybrid
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