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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.

Show MeSH
Two interacting proteins, P1 and P2, with their respective annotation terms. An annotation link is created by linking an annotation from the first protein's annotation set to another annotation of the second protein. Above, the tuples {(a1, a2), (a1, a4), (a2, a2), (a2, a4), (a3, a2), (a3, a4)} are shown. There are a total of 3 × 2 = 6 annotation links between P1 and P2.
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Figure 2: Two interacting proteins, P1 and P2, with their respective annotation terms. An annotation link is created by linking an annotation from the first protein's annotation set to another annotation of the second protein. Above, the tuples {(a1, a2), (a1, a4), (a2, a2), (a2, a4), (a3, a2), (a3, a4)} are shown. There are a total of 3 × 2 = 6 annotation links between P1 and P2.

Mentions: Proteins in an organism may have multiple functional annotations. In this framework, for each protein an annotation set is kept. Using these annotation sets, functional relationships among proteins are established (see Figure 2). Each annotation of a protein is linked with its interacting neighbor's annotations and a network of annotation links is formed.


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

Bebek G, Yang J - BMC Bioinformatics (2007)

Two interacting proteins, P1 and P2, with their respective annotation terms. An annotation link is created by linking an annotation from the first protein's annotation set to another annotation of the second protein. Above, the tuples {(a1, a2), (a1, a4), (a2, a2), (a2, a4), (a3, a2), (a3, a4)} are shown. There are a total of 3 × 2 = 6 annotation links between P1 and P2.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Two interacting proteins, P1 and P2, with their respective annotation terms. An annotation link is created by linking an annotation from the first protein's annotation set to another annotation of the second protein. Above, the tuples {(a1, a2), (a1, a4), (a2, a2), (a2, a4), (a3, a2), (a3, a4)} are shown. There are a total of 3 × 2 = 6 annotation links between P1 and P2.
Mentions: Proteins in an organism may have multiple functional annotations. In this framework, for each protein an annotation set is kept. Using these annotation sets, functional relationships among proteins are established (see Figure 2). Each annotation of a protein is linked with its interacting neighbor's annotations and a network of annotation links is formed.

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