Abstract
Proteins interact among them and different interactions form a very huge number of possible combinations representable as protein to protein interaction (PPI) networks that are mapped into graph structures. The interest in analyzing PPI networks is related to the possibility of predicting PPI properties, starting from a set of known proteins interacting among each other. For example, predicting the configuration of a subset of nodes in a graph (representing a PPI network), allows to study the generation of protein complexes. Nevertheless, due to the huge number of possible configurations of protein interactions, automatic based computation tools are required. Available prediction tools are able to analyze and predict possible combinations of proteins in a PPI network which have biological meanings. Once obtained, the protein interactions are analyzed with respect to biological meanings representing quality measures. Nevertheless, such tools strictly depend on input configuration and require biological validation. In this paper we propose a new prediction tool based on integration of different prediction results obtained from available tools. The proposed integration approach has been implemented in an on line available tool, IMPRECO standing for IMproving PREdiction of COmplexes. IMPRECO has been tested on publicly available datasets, with satisfiable results.
Chapter PDF
Similar content being viewed by others
Keywords
- Positive Predictive Value
- Protein Interaction Network
- Integration Algorithm
- Topological Relation
- Protein Interaction Data
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Sharan, R., Ideker, T., Kelley, B., Shamir, R., Karp, R.: Identification of protein complexes by comparative analysis of yeast and bacterial protein interaction data. J. Comput. Biol. 12(6), 835–846 (2005)
Fell, D., Wagner, A.: The small world of metabolism. Nat. Biotechnol. 18(11), 1121–1122 (2000)
Lesne, A.: Complex networks: from graph theory to biology. Letters in Mathematical Physics 78(3), 235–262 (2006)
Bader, G., Hogue, C.: An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics 4(1), 2 (2003)
King, A.D., Przulj, N., Jurisica, I.: Protein complex prediction via cost-based clustering. Bioinformatics 20(17), 3013–3020 (2004)
Brohe, S., van Helden, J.: Evaluation of clustering algorithms for protein-protein interaction networks. BMC Bioinformatics 7, 488 (2006)
Cannataro, M., Guzzi, P.H., Veltri, P.: A framework for the prediction of protein complexes. In: Abstract in Proceedings of the Bioinformatics Italian Society Conference (BITS 2007) (2007)
Enright, A.J., Van Dongen, S., Ouzounis, C.: An efficient algorithm for large-scale detection of protein families. Nucleic Acids Research 30(7), 1575–1584 (2002)
Mewes, H.W., Frishman, D., Mayer, K., Mnsterktter, M., Noubibou, O., Pagel, P., Rattei, T., Oesterheld, M.A.R., Stmpflen, V.: Mips: analysis and annotation of proteins from whole genomes in 2005. Nucleic Acids Res. 34(Database issue), D169–D172 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cannataro, M., Guzzi, P.H., Veltri, P. (2008). IMPRECO: A Tool for Improving the Prediction of Protein Complexes. In: Bubak, M., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds) Computational Science – ICCS 2008. ICCS 2008. Lecture Notes in Computer Science, vol 5103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69389-5_18
Download citation
DOI: https://doi.org/10.1007/978-3-540-69389-5_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69388-8
Online ISBN: 978-3-540-69389-5
eBook Packages: Computer ScienceComputer Science (R0)