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

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.

journal_name

Exp Ther Med

authors

Ruan Y,Li Y,Liu Y,Zhou J,Wang X,Zhang W

doi

10.3892/etm.2019.7410

subject

Has Abstract

pub_date

2019-05-01 00:00:00

pages

4139-4143

issue

5

eissn

1792-0981

issn

1792-1015

pii

ETM-0-0-7410

journal_volume

17

pub_type

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