2017
DOI: 10.1515/jpm-2017-0126
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Methylation differences reveal heterogeneity in preterm pathophysiology: results from bipartite network analyses

Abstract: The results demonstrate that unsupervised bipartite networks helped to identify a complex but comprehensible data-driven hypotheses related to patient subgroups and inferences about their underlying pathways, and therefore were an effective complement to supervised approaches currently used.

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Cited by 13 publications
(12 citation statements)
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References 54 publications
(64 reference statements)
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“…4. For prematurity, two different aberrant DNA hypomethylation patterns suggest a heterogeneous pathophysiology [103]. With 6-19-week cervical DNA, increased methylation of prostaglandin E receptor 2 gene (PTGER2) showed longer gestations, while repetitive long interspersed nuclear element-1 Homo sapiens-specific (LINE 1-HS) were shorter.…”
Section: With Early Pregnancy Losses Dnmt 1 Expression and Glob-mentioning
confidence: 99%
“…4. For prematurity, two different aberrant DNA hypomethylation patterns suggest a heterogeneous pathophysiology [103]. With 6-19-week cervical DNA, increased methylation of prostaglandin E receptor 2 gene (PTGER2) showed longer gestations, while repetitive long interspersed nuclear element-1 Homo sapiens-specific (LINE 1-HS) were shorter.…”
Section: With Early Pregnancy Losses Dnmt 1 Expression and Glob-mentioning
confidence: 99%
“…Study selection 999 references were imported from the search. Of the 74 abstracts that met initial inclusion criteria, the fulltext review led to inclusion of 16 studies [29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44] that drew from 13 birth cohorts and exclusion of 58 studies (Figure 1 & Table 1). Among excluded studies, two were excluded because they were transcriptomic [45] and metabolomic [46] studies.…”
Section: Resultsmentioning
confidence: 99%
“…The proteome data sets were previously described ( 19 ). Log-transformed proteome data sets were subjected to bipartite network analysis and visualization ( 49 ) using the following steps: (i) feature selection to identify which protein spots were univariably significant (after FDR correction) between different comparison groups; (ii) bipartite modularity maximization ( 50 ) to identify biclusters of patients and protein spots, with testing of significance through comparison to biclustering generated from 1,000 permutations of the data; and (iii) enrichment analysis using χ 2 to determine if there was a significantly different proportion of phenotypes in each bicluster compared to the rest of the data.…”
Section: Methodsmentioning
confidence: 99%