The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps
Abstract
MOTIVATION: Determining protein function is one of the most important problems in the post-genomic era. For the typical proteome, there are no functional annotations for one-third or more of its proteins. Recent high-throughput experiments have determined proteome-scale protein physical interaction maps for several organisms. These physical interactions are complemented by an abundance of data about other types of functional relationships between proteins, including genetic interactions, knowledge about co-expression and shared evolutionary history. Taken together, these pairwise linkages can be used to build whole-proteome protein interaction maps. RESULTS: We develop a network-flow based algorithm, FunctionalFlow, that exploits the underlying structure of protein interaction maps in order to predict protein function. In cross-validation testing on the yeast proteome, we show that FunctionalFlow has improved performance over previous methods in predicting the function of proteins with few (or no) annotated protein neighbors. By comparing several methods that use protein interaction maps to predict protein function, we demonstrate that FunctionalFlow performs well because it takes advantage of both network topology and some measure of locality. Finally, we show that performance can be improved substantially as we consider multiple data sources and use them to create weighted interaction networks. AVAILABILITY: http://compbio.cs.princeton.edu/function
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A Survey of Statistical Network Models
Goldenberg, A., Zheng, A. X., Fienberg, S. E., & Airoldi, E. M. (2010) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000005 · Cited by 819
Zhou, N., Jiang, Y., Bergquist, T. R., Lee, A. J., Kacsoh, B. Z., Crocker, A. W., Lewis, K. A., Georghiou, G., Nguyen, H. N., Hamid, M. N., et al. (2019) · Genome Biology · doi:10.1186/s13059-019-1835-8 · Cited by 452
Global Functional Atlas of Escherichia coli Encompassing Previously Uncharacterized Proteins
Hu, P., Janga, S. C., Babu, M., Díaz-Mejía, J. J., Butland, G., Yang, W., Pogoutse, O., Guo, X., Phanse, S., Wong, P., et al. (2009) · PLoS Biology · doi:10.1371/journal.pbio.1000096 · Cited by 387
Cao, M., Zhang, H., Park, J., Daniels, N. M., Crovella, M. E., Cowen, L. J., & Hescott, B. (2013) · PLoS ONE · doi:10.1371/journal.pone.0076339 · Cited by 131
Metadata
- Authors
- Nabieva, E., Jim, K., Agarwal, A., Chazelle, B., & Singh, M.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2005-06-01
- Volume
- 21
- Issue
- Suppl 1
- Pages
- i302-i310
- ISSN
- 0266-7061 (ISSN-L)
- 1367-4803
- 1460-2059
- OpenAlex
- W2171403830
- Cited by
- 500
- References
- 6
- FWCI
- 8.64
- Type
- journal article
- Language
- en
- Open access
- closed
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- Nabieva, E., Jim, K., Agarwal, A., Chazelle, B., & Singh, M. (2005). Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps. Bioinformatics. https://doi.org/10.1093/bioinformatics/bti1054
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