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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

Example of simulated networks with different parameter settings. M and Eg indicate the number of genes and expected number of edges per node, respectively.
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fig1: Example of simulated networks with different parameter settings. M and Eg indicate the number of genes and expected number of edges per node, respectively.

Mentions: Figure 1 displays the examples of networks, where SNP nodes are excluded. For the evaluation, true positive (TP), false positive (FP), true negative (TN), and false negative (FN) edges are counted to measure the accuracy criteria such as true positive rate (TPR) and false discovery rate (FDR) that are defined as


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)

Example of simulated networks with different parameter settings. M and Eg indicate the number of genes and expected number of edges per node, respectively.
© Copyright Policy
Related In: Results  -  Collection

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

fig1: Example of simulated networks with different parameter settings. M and Eg indicate the number of genes and expected number of edges per node, respectively.
Mentions: Figure 1 displays the examples of networks, where SNP nodes are excluded. For the evaluation, true positive (TP), false positive (FP), true negative (TN), and false negative (FN) edges are counted to measure the accuracy criteria such as true positive rate (TPR) and false discovery rate (FDR) that are defined as

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