Limits...
Modifying the DPClus algorithm for identifying protein complexes based on new topological structures.

Li M, Chen JE, Wang JX, Hu B, Chen G - BMC Bioinformatics (2008)

Bottom Line: Identification of protein complexes is crucial for understanding principles of cellular organization and functions.As the size of protein-protein interaction set increases, a general trend is to represent the interactions as a network and to develop effective algorithms to detect significant complexes in such networks.Based on the study of known complexes in protein networks, this paper proposes a new topological structure for protein complexes, which is a combination of subgraph diameter (or average vertex distance) and subgraph density.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Information Science and Engineering, Central South University, Changsha, Hunan 410083, PR China. limin@mail.csu.edu.cn

ABSTRACT

Background: Identification of protein complexes is crucial for understanding principles of cellular organization and functions. As the size of protein-protein interaction set increases, a general trend is to represent the interactions as a network and to develop effective algorithms to detect significant complexes in such networks.

Results: Based on the study of known complexes in protein networks, this paper proposes a new topological structure for protein complexes, which is a combination of subgraph diameter (or average vertex distance) and subgraph density. Following the approach of that of the previously proposed clustering algorithm DPClus which expands clusters starting from seeded vertices, we present a clustering algorithm IPCA based on the new topological structure for identifying complexes in large protein interaction networks. The algorithm IPCA is applied to the protein interaction network of Sacchromyces cerevisiae and identifies many well known complexes. Experimental results show that the algorithm IPCA recalls more known complexes than previously proposed clustering algorithms, including DPClus, CFinder, LCMA, MCODE, RNSC and STM.

Conclusion: The proposed algorithm based on the new topological structure makes it possible to identify dense subgraphs in protein interaction networks, many of which correspond to known protein complexes. The algorithm is robust to the known high rate of false positives and false negatives in data from high-throughout interaction techniques. The program is available at http://netlab.csu.edu.cn/bioinformatics/limin/IPCA.

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Two examples of graphs with SP = 2. Graphs with the same diameter can have very different topologies. To distinguish different topologies of graphs with the same diameter, we can use the parameter INvK. For example, the two graphs in this figure both have diameter 2. However, for all vertices v in the first graph, the value INvK' is 4/5; while for five of the six vertices in the second graph, the value INvK' is 1/5 (where we define K' = K - v).
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Figure 1: Two examples of graphs with SP = 2. Graphs with the same diameter can have very different topologies. To distinguish different topologies of graphs with the same diameter, we can use the parameter INvK. For example, the two graphs in this figure both have diameter 2. However, for all vertices v in the first graph, the value INvK' is 4/5; while for five of the six vertices in the second graph, the value INvK' is 1/5 (where we define K' = K - v).

Mentions: As shown in Figure 1, graphs with the same diameter can have very different topologies. To distinguish different topologies of graphs with the same diameter, we need another control parameter. For a dense graph, a vertex is connected to most of the vertices in the graph. On the other hand, in a sparse graph a vertex may be connected to only a few vertices in the graph. We introduce a new concept to measure how strongly a vertex v is connected to a subgraph K: the interaction probability INvK of a vertex v to a subgraph K, where v ∉ K, is defined by


Modifying the DPClus algorithm for identifying protein complexes based on new topological structures.

Li M, Chen JE, Wang JX, Hu B, Chen G - BMC Bioinformatics (2008)

Two examples of graphs with SP = 2. Graphs with the same diameter can have very different topologies. To distinguish different topologies of graphs with the same diameter, we can use the parameter INvK. For example, the two graphs in this figure both have diameter 2. However, for all vertices v in the first graph, the value INvK' is 4/5; while for five of the six vertices in the second graph, the value INvK' is 1/5 (where we define K' = K - v).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2570695&req=5

Figure 1: Two examples of graphs with SP = 2. Graphs with the same diameter can have very different topologies. To distinguish different topologies of graphs with the same diameter, we can use the parameter INvK. For example, the two graphs in this figure both have diameter 2. However, for all vertices v in the first graph, the value INvK' is 4/5; while for five of the six vertices in the second graph, the value INvK' is 1/5 (where we define K' = K - v).
Mentions: As shown in Figure 1, graphs with the same diameter can have very different topologies. To distinguish different topologies of graphs with the same diameter, we need another control parameter. For a dense graph, a vertex is connected to most of the vertices in the graph. On the other hand, in a sparse graph a vertex may be connected to only a few vertices in the graph. We introduce a new concept to measure how strongly a vertex v is connected to a subgraph K: the interaction probability INvK of a vertex v to a subgraph K, where v ∉ K, is defined by

Bottom Line: Identification of protein complexes is crucial for understanding principles of cellular organization and functions.As the size of protein-protein interaction set increases, a general trend is to represent the interactions as a network and to develop effective algorithms to detect significant complexes in such networks.Based on the study of known complexes in protein networks, this paper proposes a new topological structure for protein complexes, which is a combination of subgraph diameter (or average vertex distance) and subgraph density.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Information Science and Engineering, Central South University, Changsha, Hunan 410083, PR China. limin@mail.csu.edu.cn

ABSTRACT

Background: Identification of protein complexes is crucial for understanding principles of cellular organization and functions. As the size of protein-protein interaction set increases, a general trend is to represent the interactions as a network and to develop effective algorithms to detect significant complexes in such networks.

Results: Based on the study of known complexes in protein networks, this paper proposes a new topological structure for protein complexes, which is a combination of subgraph diameter (or average vertex distance) and subgraph density. Following the approach of that of the previously proposed clustering algorithm DPClus which expands clusters starting from seeded vertices, we present a clustering algorithm IPCA based on the new topological structure for identifying complexes in large protein interaction networks. The algorithm IPCA is applied to the protein interaction network of Sacchromyces cerevisiae and identifies many well known complexes. Experimental results show that the algorithm IPCA recalls more known complexes than previously proposed clustering algorithms, including DPClus, CFinder, LCMA, MCODE, RNSC and STM.

Conclusion: The proposed algorithm based on the new topological structure makes it possible to identify dense subgraphs in protein interaction networks, many of which correspond to known protein complexes. The algorithm is robust to the known high rate of false positives and false negatives in data from high-throughout interaction techniques. The program is available at http://netlab.csu.edu.cn/bioinformatics/limin/IPCA.

Show MeSH