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Cited by 620

The DOI matches its Crossref record; 93 of the 93 references it cites were found in a registry or an index.

Protein-to-protein interactions: Technologies, databases, and algorithms

Cannataro, Mario, Guzzi, Pietro H., Veltri, Pierangelo·2010·ACM Computing Surveys
DOI 10.1145/1824795.1824796
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Abstract

Studying proteins and their structures has an important role for understanding protein functionalities. Recently, due to important results obtained with proteomics, a great interest has been given to interactomics , that is, the study of protein-to-protein interactions, called PPI, or more generally, interactions among macromolecules, particularly within cells. Interactomics means studying, modeling, storing, and retrieving protein-to-protein interactions as well as algorithms for manipulating, simulating, and predicting interactions. PPI data can be obtained from biological experiments studying interactions. Modeling and storing PPIs can be realized by using graph theory and graph data management, thus graph databases can be queried for further experiments. PPI graphs can be used as input for data-mining algorithms, where raw data are binary interactions forming interaction graphs, and analysis algorithms retrieve biological interactions among proteins (i.e., PPI biological meanings). For instance, predicting the interactions between two or more proteins can be obtained by mining interaction networks stored in databases. In this article we survey modeling, storing, analyzing, and manipulating PPI data. After describing the main PPI models, mostly based on graphs, the article reviews PPI data representation and storage, as well as PPI databases. Algorithms and software tools for analyzing and managing PPI networks are discussed in depth. The article concludes by discussing the main challenges and research directions in PPI networks.

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Cites

Show all 93 references

Research works printed but not linked: 7

  1. 10.1109/MEMB.2005.1436466 · doi:10.1109/memb.2005.1436466
  2. Deng M., Proceedings of the Pacific Symposium on Biocomputing, World Scientific, 140--151
  3. Italiano G. F. 1991. Dynamic data structures for graphs. Ph.D. dissertation Department of Computer Science Columbia University New York NY. Italiano G. F. 1991. Dynamic data structures for graphs. Ph.D. dissertation Department of Computer Science Columbia University New York NY.
  4. Ivanic J., 2008, Evidence of probabilistic behaviour in protein interaction networks. BMC Syst, Biol., 2, 1
  5. King A. 2004. Graph clustering with restricted neighbourhood search. M.S. thesis University of Toronto Toronto Ontario Canada. King A. 2004. Graph clustering with restricted neighbourhood search. M.S. thesis University of Toronto Toronto Ontario Canada.
  6. Penrose M. 2003. Geometric Random Graphs. Oxford University Press Oxford UK. Penrose M. 2003. Geometric Random Graphs. Oxford University Press Oxford UK.
  7. Wei C., Proceedings of the Pacific Sym- posium on Biocomputing. R. B. Altman, et al. Eds., World Scientific.

Cited by

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Metadata

Authors
Cannataro, M., Guzzi, P. H., & Veltri, P.
Venue
ACM Computing Surveys
Publisher
Association for Computing Machinery (ACM)
Published
2010-11
Volume
43
Issue
1
Pages
1-36
ISSN
  • 0360-0300 (ISSN-L)
  • 1557-7341
DOI
10.1145/1824795.1824796
OpenAlex
W1966631994
Cited by
620
References
93
FWCI
17.60
Type
journal article
Language
en
Open access
closed
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)

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CoChat. (2026). Citation check: Protein-to-protein interactions: Technologies, databases, and algorithms. https://app.cochat.ai/papers/10.1145/1824795.1824796
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Cannataro, M., Guzzi, P. H., & Veltri, P. (2010). Protein-to-protein interactions: Technologies, databases, and algorithms. ACM Computing Surveys. https://doi.org/10.1145/1824795.1824796

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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