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 Medjournal_title
Experimental and therapeutic medicineauthors
Ruan Y,Li Y,Liu Y,Zhou J,Wang X,Zhang Wdoi
10.3892/etm.2019.7410subject
Has Abstractpub_date
2019-05-01 00:00:00pages
4139-4143issue
5eissn
1792-0981issn
1792-1015pii
ETM-0-0-7410journal_volume
17pub_type
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