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ABC and IFC: modules detection method for PPI network.

Lei X, Wu FX, Tian J, Zhao J - Biomed Res Int (2014)

Bottom Line: Many clustering algorithms are unable to solve the clustering problem of protein-protein interaction (PPI) networks effectively.A novel clustering model which combines the optimization mechanism of artificial bee colony (ABC) with the fuzzy membership matrix is proposed in this paper.Firstly, the cluster centers are set randomly and the initial clustering results are obtained by using fuzzy membership matrix.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science, Shaanxi Normal University, Xi'an, Shaanxi 710062, China ; School of Electronics Engineering and Computer Science, Peking University (Visiting Scholar), Beijing 100871, China.

ABSTRACT
Many clustering algorithms are unable to solve the clustering problem of protein-protein interaction (PPI) networks effectively. A novel clustering model which combines the optimization mechanism of artificial bee colony (ABC) with the fuzzy membership matrix is proposed in this paper. The proposed ABC-IFC clustering model contains two parts: searching for the optimum cluster centers using ABC mechanism and forming clusters using intuitionistic fuzzy clustering (IFC) method. Firstly, the cluster centers are set randomly and the initial clustering results are obtained by using fuzzy membership matrix. Then the cluster centers are updated through different functions of bees in ABC algorithm; then the clustering result is obtained through IFC method based on the new optimized cluster center. To illustrate its performance, the ABC-IFC method is compared with the traditional fuzzy C-means clustering and IFC method. The experimental results on MIPS dataset show that the proposed ABC-IFC method not only gets improved in terms of several commonly used evaluation criteria such as precision, recall, and P value, but also obtains a better clustering result.

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Related in: MedlinePlus

Comparison of three algorithms in Pvalue.
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fig5: Comparison of three algorithms in Pvalue.

Mentions: Figure 5 describes the comparison of P value among three algorithms. P value of ABC-IFC decreases with the cluster number increasing at the beginning. P value increases gradually after the cluster number increases to 80. What is more, P value becomes lower and holds steady when the number of clusters arranges from 80 to 120. P value of IFC is between ABC-IFC and FCM. The highest value and strongest fluctuation of P value appears in FCM.


ABC and IFC: modules detection method for PPI network.

Lei X, Wu FX, Tian J, Zhao J - Biomed Res Int (2014)

Comparison of three algorithms in Pvalue.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4060787&req=5

fig5: Comparison of three algorithms in Pvalue.
Mentions: Figure 5 describes the comparison of P value among three algorithms. P value of ABC-IFC decreases with the cluster number increasing at the beginning. P value increases gradually after the cluster number increases to 80. What is more, P value becomes lower and holds steady when the number of clusters arranges from 80 to 120. P value of IFC is between ABC-IFC and FCM. The highest value and strongest fluctuation of P value appears in FCM.

Bottom Line: Many clustering algorithms are unable to solve the clustering problem of protein-protein interaction (PPI) networks effectively.A novel clustering model which combines the optimization mechanism of artificial bee colony (ABC) with the fuzzy membership matrix is proposed in this paper.Firstly, the cluster centers are set randomly and the initial clustering results are obtained by using fuzzy membership matrix.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science, Shaanxi Normal University, Xi'an, Shaanxi 710062, China ; School of Electronics Engineering and Computer Science, Peking University (Visiting Scholar), Beijing 100871, China.

ABSTRACT
Many clustering algorithms are unable to solve the clustering problem of protein-protein interaction (PPI) networks effectively. A novel clustering model which combines the optimization mechanism of artificial bee colony (ABC) with the fuzzy membership matrix is proposed in this paper. The proposed ABC-IFC clustering model contains two parts: searching for the optimum cluster centers using ABC mechanism and forming clusters using intuitionistic fuzzy clustering (IFC) method. Firstly, the cluster centers are set randomly and the initial clustering results are obtained by using fuzzy membership matrix. Then the cluster centers are updated through different functions of bees in ABC algorithm; then the clustering result is obtained through IFC method based on the new optimized cluster center. To illustrate its performance, the ABC-IFC method is compared with the traditional fuzzy C-means clustering and IFC method. The experimental results on MIPS dataset show that the proposed ABC-IFC method not only gets improved in terms of several commonly used evaluation criteria such as precision, recall, and P value, but also obtains a better clustering result.

Show MeSH
Related in: MedlinePlus