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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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The description of IPCA algorithm. IPCA algorithm extends clusters based on (K, Tin, d)-vertices. If the algorithm uses the (SP ≤ d)-Version in the conditions of Definition 1, we will say that "the algorithm uses SP ≤ d". Similarly, if the algorithm uses the (ASP ≤ d)-Version in the conditions of Definition 1, we will say that "the algorithm uses ASP ≤ d".
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Figure 2: The description of IPCA algorithm. IPCA algorithm extends clusters based on (K, Tin, d)-vertices. If the algorithm uses the (SP ≤ d)-Version in the conditions of Definition 1, we will say that "the algorithm uses SP ≤ d". Similarly, if the algorithm uses the (ASP ≤ d)-Version in the conditions of Definition 1, we will say that "the algorithm uses ASP ≤ d".

Mentions: Our clustering algorithm IPCA that extends clusters based on (K, Tin, d)-vertices is given in Figure 2. If the algorithm uses the (SP ≤ d)-Version in the conditions in the definition, we will say that "the algorithm uses SP ≤ d". Similarly, if the algorithm uses the (ASP ≤ d)-Version in the conditions in the definition, we will say that "the algorithm uses ASP ≤ d".


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)

The description of IPCA algorithm. IPCA algorithm extends clusters based on (K, Tin, d)-vertices. If the algorithm uses the (SP ≤ d)-Version in the conditions of Definition 1, we will say that "the algorithm uses SP ≤ d". Similarly, if the algorithm uses the (ASP ≤ d)-Version in the conditions of Definition 1, we will say that "the algorithm uses ASP ≤ d".
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: The description of IPCA algorithm. IPCA algorithm extends clusters based on (K, Tin, d)-vertices. If the algorithm uses the (SP ≤ d)-Version in the conditions of Definition 1, we will say that "the algorithm uses SP ≤ d". Similarly, if the algorithm uses the (ASP ≤ d)-Version in the conditions of Definition 1, we will say that "the algorithm uses ASP ≤ d".
Mentions: Our clustering algorithm IPCA that extends clusters based on (K, Tin, d)-vertices is given in Figure 2. If the algorithm uses the (SP ≤ d)-Version in the conditions in the definition, we will say that "the algorithm uses SP ≤ d". Similarly, if the algorithm uses the (ASP ≤ d)-Version in the conditions in the definition, we will say that "the algorithm uses ASP ≤ d".

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
Related in: MedlinePlus