ArticleExperimental and therapeutic medicine2019
Investigation of optimal pathways for preeclampsia using network-based guilt by association algorithm.
Article in Experimental and therapeutic medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Long non‑coding RNA DANCR aggravates breast cancer through the miR‑34c/E2F1 feedback loop.Molecular medicine reports · 2024Article
- Integrated Analysis of the Transcriptome Profile Reveals the Potential Roles Played by Long Noncoding RNAs in Immunotherapy for Sarcoma.Frontiers in oncology · 2021Article
- A role for microRNAs in the epigenetic control of sexually dimorphic gene expression in the human placenta.Epigenomics · 2020Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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
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