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Inference of SNP-gene regulatory networks by integrating gene expressions and genetic perturbations.

Kim DC, Wang J, Liu C, Gao J - Biomed Res Int (2014)

Bottom Line: In the most of the network inferences named as SNP-gene regulatory network (SGRN) inference, pairs of SNP-gene are given by separately performing expression quantitative trait loci (eQTL) mappings.There are three main contributions.Second, the experimental results demonstrated that integration of multiple methods can produce competitive performances.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX 76019, USA.

ABSTRACT
In order to elucidate the overall relationships between gene expressions and genetic perturbations, we propose a network inference method to infer gene regulatory network where single nucleotide polymorphism (SNP) is involved as a regulator of genes. In the most of the network inferences named as SNP-gene regulatory network (SGRN) inference, pairs of SNP-gene are given by separately performing expression quantitative trait loci (eQTL) mappings. In this paper, we propose a SGRN inference method without predefined eQTL information assuming a gene is regulated by a single SNP at most. To evaluate the performance, the proposed method was applied to random data generated from synthetic networks and parameters. There are three main contributions. First, the proposed method provides both the gene regulatory inference and the eQTL identification. Second, the experimental results demonstrated that integration of multiple methods can produce competitive performances. Lastly, the proposed method was also applied to psychiatric disorder data in order to explore how the method works with real data.

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Related in: MedlinePlus

True positive rate and false discovery rate under different numbers of edges and nodes.
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fig2: True positive rate and false discovery rate under different numbers of edges and nodes.

Mentions: In order to evaluate our method, IAL is compared to SML [18]. As SML infers only B with known nonzero element indices of F, we consider two versions of IAL, IAL without eQTL information and IAL with eQTL information, where Steps 1 and 2 are skipped and only Step 3 is performed with nonzero element index of fi. SML is tested by using the code the author implemented in [18]. The abbreviations of algorithms to compare in Figure 2 and Table 1 are listed below:


Inference of SNP-gene regulatory networks by integrating gene expressions and genetic perturbations.

Kim DC, Wang J, Liu C, Gao J - Biomed Res Int (2014)

True positive rate and false discovery rate under different numbers of edges and nodes.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4127230&req=5

fig2: True positive rate and false discovery rate under different numbers of edges and nodes.
Mentions: In order to evaluate our method, IAL is compared to SML [18]. As SML infers only B with known nonzero element indices of F, we consider two versions of IAL, IAL without eQTL information and IAL with eQTL information, where Steps 1 and 2 are skipped and only Step 3 is performed with nonzero element index of fi. SML is tested by using the code the author implemented in [18]. The abbreviations of algorithms to compare in Figure 2 and Table 1 are listed below:

Bottom Line: In the most of the network inferences named as SNP-gene regulatory network (SGRN) inference, pairs of SNP-gene are given by separately performing expression quantitative trait loci (eQTL) mappings.There are three main contributions.Second, the experimental results demonstrated that integration of multiple methods can produce competitive performances.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX 76019, USA.

ABSTRACT
In order to elucidate the overall relationships between gene expressions and genetic perturbations, we propose a network inference method to infer gene regulatory network where single nucleotide polymorphism (SNP) is involved as a regulator of genes. In the most of the network inferences named as SNP-gene regulatory network (SGRN) inference, pairs of SNP-gene are given by separately performing expression quantitative trait loci (eQTL) mappings. In this paper, we propose a SGRN inference method without predefined eQTL information assuming a gene is regulated by a single SNP at most. To evaluate the performance, the proposed method was applied to random data generated from synthetic networks and parameters. There are three main contributions. First, the proposed method provides both the gene regulatory inference and the eQTL identification. Second, the experimental results demonstrated that integration of multiple methods can produce competitive performances. Lastly, the proposed method was also applied to psychiatric disorder data in order to explore how the method works with real data.

Show MeSH
Related in: MedlinePlus