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PathFinder: mining signal transduction pathway segments from protein-protein interaction networks.

Bebek G, Yang J - BMC Bioinformatics (2007)

Bottom Line: Our goal is to find biologically significant pathway segments in a given interaction network.Given a pair of starting and ending proteins, our methodology returns candidate pathway segments between these two proteins with possible missing links (recovered false negatives).In our study, S. cerevisiae (yeast) data is used to demonstrate the effectiveness of our method.

View Article: PubMed Central - HTML - PubMed

Affiliation: EECS Department, Case Western Reserve University, Cleveland, OH 44106, USA. gurkan.bebek@case.edu

ABSTRACT

Background: A Signal transduction pathway is the chain of processes by which a cell converts an extracellular signal into a response. In most unicellular organisms, the number of signal transduction pathways influences the number of ways the cell can react and respond to the environment. Discovering signal transduction pathways is an arduous problem, even with the use of systematic genomic, proteomic and metabolomic technologies. These techniques lead to an enormous amount of data and how to interpret and process this data becomes a challenging computational problem.

Results: In this study we present a new framework for identifying signaling pathways in protein-protein interaction networks. Our goal is to find biologically significant pathway segments in a given interaction network. Currently, protein-protein interaction data has excessive amount of noise, e.g., false positive and false negative interactions. First, we eliminate false positives in the protein-protein interaction network by integrating the network with microarray expression profiles, protein subcellular localization and sequence information. In addition, protein families are used to repair false negative interactions. Then the characteristics of known signal transduction pathways and their functional annotations are extracted in the form of association rules.

Conclusion: Given a pair of starting and ending proteins, our methodology returns candidate pathway segments between these two proteins with possible missing links (recovered false negatives). In our study, S. cerevisiae (yeast) data is used to demonstrate the effectiveness of our method.

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The filamentation signaling pathway recovered completely. (A) The main chain of the filamentation pathway (KEGG Database), (B) PathFinder output for Sho1-Tec1 pair (100% recall, 12% precision). For (A), the dashed links indicate interactions that do not exists in the database. For (B), the interactions that were predicted as false negatives were shown with dashed lines. The proteins that were not on the main chain of any pathway were not colored, whereas proteins that are part of other pathways were colored.
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Figure 6: The filamentation signaling pathway recovered completely. (A) The main chain of the filamentation pathway (KEGG Database), (B) PathFinder output for Sho1-Tec1 pair (100% recall, 12% precision). For (A), the dashed links indicate interactions that do not exists in the database. For (B), the interactions that were predicted as false negatives were shown with dashed lines. The proteins that were not on the main chain of any pathway were not colored, whereas proteins that are part of other pathways were colored.

Mentions: The missing pathway protein, Cdc42, shown in Figure 5B is due to a missing interaction in the PPIN. To recover this false negative interaction edge, we incorporated inferred links into our network (See Recovering false negative interactions) and acquired the pathway segment in Figure 6B.


PathFinder: mining signal transduction pathway segments from protein-protein interaction networks.

Bebek G, Yang J - BMC Bioinformatics (2007)

The filamentation signaling pathway recovered completely. (A) The main chain of the filamentation pathway (KEGG Database), (B) PathFinder output for Sho1-Tec1 pair (100% recall, 12% precision). For (A), the dashed links indicate interactions that do not exists in the database. For (B), the interactions that were predicted as false negatives were shown with dashed lines. The proteins that were not on the main chain of any pathway were not colored, whereas proteins that are part of other pathways were colored.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2100073&req=5

Figure 6: The filamentation signaling pathway recovered completely. (A) The main chain of the filamentation pathway (KEGG Database), (B) PathFinder output for Sho1-Tec1 pair (100% recall, 12% precision). For (A), the dashed links indicate interactions that do not exists in the database. For (B), the interactions that were predicted as false negatives were shown with dashed lines. The proteins that were not on the main chain of any pathway were not colored, whereas proteins that are part of other pathways were colored.
Mentions: The missing pathway protein, Cdc42, shown in Figure 5B is due to a missing interaction in the PPIN. To recover this false negative interaction edge, we incorporated inferred links into our network (See Recovering false negative interactions) and acquired the pathway segment in Figure 6B.

Bottom Line: Our goal is to find biologically significant pathway segments in a given interaction network.Given a pair of starting and ending proteins, our methodology returns candidate pathway segments between these two proteins with possible missing links (recovered false negatives).In our study, S. cerevisiae (yeast) data is used to demonstrate the effectiveness of our method.

View Article: PubMed Central - HTML - PubMed

Affiliation: EECS Department, Case Western Reserve University, Cleveland, OH 44106, USA. gurkan.bebek@case.edu

ABSTRACT

Background: A Signal transduction pathway is the chain of processes by which a cell converts an extracellular signal into a response. In most unicellular organisms, the number of signal transduction pathways influences the number of ways the cell can react and respond to the environment. Discovering signal transduction pathways is an arduous problem, even with the use of systematic genomic, proteomic and metabolomic technologies. These techniques lead to an enormous amount of data and how to interpret and process this data becomes a challenging computational problem.

Results: In this study we present a new framework for identifying signaling pathways in protein-protein interaction networks. Our goal is to find biologically significant pathway segments in a given interaction network. Currently, protein-protein interaction data has excessive amount of noise, e.g., false positive and false negative interactions. First, we eliminate false positives in the protein-protein interaction network by integrating the network with microarray expression profiles, protein subcellular localization and sequence information. In addition, protein families are used to repair false negative interactions. Then the characteristics of known signal transduction pathways and their functional annotations are extracted in the form of association rules.

Conclusion: Given a pair of starting and ending proteins, our methodology returns candidate pathway segments between these two proteins with possible missing links (recovered false negatives). In our study, S. cerevisiae (yeast) data is used to demonstrate the effectiveness of our method.

Show MeSH