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An Improved Opposition-Based Learning Particle Swarm Optimization for the Detection of SNP-SNP Interactions.

Shang J, Sun Y, Li S, Liu JX, Zheng CH, Zhang J - Biomed Res Int (2015)

Bottom Line: In this study, an improved opposition-based learning particle swarm optimization (IOBLPSO) is proposed for the detection of SNP-SNP interactions.The postprocedure is used to carry out a deep search in highly suspected SNP sets.IOBLPSO might be an alternative to existing methods for detecting SNP-SNP interactions.

View Article: PubMed Central - PubMed

Affiliation: School of Information Science and Engineering, Qufu Normal University, Rizhao 276826, China.

ABSTRACT
SNP-SNP interactions have been receiving increasing attention in understanding the mechanism underlying susceptibility to complex diseases. Though many works have been done for the detection of SNP-SNP interactions, the algorithmic development is still ongoing. In this study, an improved opposition-based learning particle swarm optimization (IOBLPSO) is proposed for the detection of SNP-SNP interactions. Highlights of IOBLPSO are the introduction of three strategies, namely, opposition-based learning, dynamic inertia weight, and a postprocedure. Opposition-based learning not only enhances the global explorative ability, but also avoids premature convergence. Dynamic inertia weight allows particles to cover a wider search space when the considered SNP is likely to be a random one and converges on promising regions of the search space while capturing a highly suspected SNP. The postprocedure is used to carry out a deep search in highly suspected SNP sets. Experiments of IOBLPSO are performed on both simulation data sets and a real data set of age-related macular degeneration, results of which demonstrate that IOBLPSO is promising in detecting SNP-SNP interactions. IOBLPSO might be an alternative to existing methods for detecting SNP-SNP interactions.

No MeSH data available.


Related in: MedlinePlus

Detection power of compared methods on simulation data sets.
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Related In: Results  -  Collection


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fig3: Detection power of compared methods on simulation data sets.

Mentions: Detection power of compared methods on simulation data sets is reported in Figure 3. Detection power of IOBLPSO and the PSO with different numbers of particles is shown in Figure 4, and that with different numbers of iterations is shown in Figure 5. The average running time of the methods on simulation data sets is recorded in Table 1. From Figures 3, 4, and 5 and Table 1, we have the following observations.


An Improved Opposition-Based Learning Particle Swarm Optimization for the Detection of SNP-SNP Interactions.

Shang J, Sun Y, Li S, Liu JX, Zheng CH, Zhang J - Biomed Res Int (2015)

Detection power of compared methods on simulation data sets.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4509494&req=5

fig3: Detection power of compared methods on simulation data sets.
Mentions: Detection power of compared methods on simulation data sets is reported in Figure 3. Detection power of IOBLPSO and the PSO with different numbers of particles is shown in Figure 4, and that with different numbers of iterations is shown in Figure 5. The average running time of the methods on simulation data sets is recorded in Table 1. From Figures 3, 4, and 5 and Table 1, we have the following observations.

Bottom Line: In this study, an improved opposition-based learning particle swarm optimization (IOBLPSO) is proposed for the detection of SNP-SNP interactions.The postprocedure is used to carry out a deep search in highly suspected SNP sets.IOBLPSO might be an alternative to existing methods for detecting SNP-SNP interactions.

View Article: PubMed Central - PubMed

Affiliation: School of Information Science and Engineering, Qufu Normal University, Rizhao 276826, China.

ABSTRACT
SNP-SNP interactions have been receiving increasing attention in understanding the mechanism underlying susceptibility to complex diseases. Though many works have been done for the detection of SNP-SNP interactions, the algorithmic development is still ongoing. In this study, an improved opposition-based learning particle swarm optimization (IOBLPSO) is proposed for the detection of SNP-SNP interactions. Highlights of IOBLPSO are the introduction of three strategies, namely, opposition-based learning, dynamic inertia weight, and a postprocedure. Opposition-based learning not only enhances the global explorative ability, but also avoids premature convergence. Dynamic inertia weight allows particles to cover a wider search space when the considered SNP is likely to be a random one and converges on promising regions of the search space while capturing a highly suspected SNP. The postprocedure is used to carry out a deep search in highly suspected SNP sets. Experiments of IOBLPSO are performed on both simulation data sets and a real data set of age-related macular degeneration, results of which demonstrate that IOBLPSO is promising in detecting SNP-SNP interactions. IOBLPSO might be an alternative to existing methods for detecting SNP-SNP interactions.

No MeSH data available.


Related in: MedlinePlus