Open Access

Investigation of optimal pathways for preeclampsia using network-based guilt by association algorithm

  • Authors:
    • Yan Ruan
    • Yuan Li
    • Yingping Liu
    • Jianxin Zhou
    • Xin Wang
    • Weiyuan Zhang
  • View Affiliations

  • Published online on: March 18, 2019     https://doi.org/10.3892/etm.2019.7410
  • Pages: 4139-4143
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Abstract

This study investigated optimal pathways for preeclampsia (PE) utilizing the network-based guilt by association (GBA) algorithm. The inference method consisted of four steps: preparing differentially expressed genes (DEGs) between PE patients and normal controls from gene expression data; constructing co-expression network (CEN) for DEGs utilizing Spearman's correlation coefficient (SCC) method; and predicting optimal pathways by network-based GBA algorithm of which the area under the receiver operating characteristics curve (AUROC) was gained for each pathway. There were 351 DEGs and 61,425 edges in the CEN for PE. Subsequently, 53 pathways were obtained with a good classification performance (AUROC >0.5). AUROC for 9 was >0.9 and defined as optimal pathways, especially microRNAs in cancer (AUROC=0.9966), gap junction (AUROC=0.9922), and pathogenic Escherichia coli infection (AUROC=0.9888). Nine optimal pathways were identified through comprehensive analysis of data from PE patients, which might shed new light on uncovering molecular and pathological mechanism of PE.
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May-2019
Volume 17 Issue 5

Print ISSN: 1792-0981
Online ISSN:1792-1015

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Spandidos Publications style
Ruan Y, Li Y, Liu Y, Zhou J, Wang X and Zhang W: Investigation of optimal pathways for preeclampsia using network-based guilt by association algorithm. Exp Ther Med 17: 4139-4143, 2019
APA
Ruan, Y., Li, Y., Liu, Y., Zhou, J., Wang, X., & Zhang, W. (2019). Investigation of optimal pathways for preeclampsia using network-based guilt by association algorithm. Experimental and Therapeutic Medicine, 17, 4139-4143. https://doi.org/10.3892/etm.2019.7410
MLA
Ruan, Y., Li, Y., Liu, Y., Zhou, J., Wang, X., Zhang, W."Investigation of optimal pathways for preeclampsia using network-based guilt by association algorithm". Experimental and Therapeutic Medicine 17.5 (2019): 4139-4143.
Chicago
Ruan, Y., Li, Y., Liu, Y., Zhou, J., Wang, X., Zhang, W."Investigation of optimal pathways for preeclampsia using network-based guilt by association algorithm". Experimental and Therapeutic Medicine 17, no. 5 (2019): 4139-4143. https://doi.org/10.3892/etm.2019.7410