Article

Improved Prediction Accuracy for Late-Onset Preeclampsia Using cfRNA Profiles: A Comparative Study of Marker Selection Strategies

2025-03-27

Abstract excerpt

<h4>Background: </h4> Late-onset preeclampsia (LO-PE) poses substantial risks to maternal–fetal health yet remains more challenging to predict than early-onset preeclampsia (PE), partly due to its stronger association with maternal factors such as obesity and chronic hyper-tension. <h4>Methods:</h4> We leveraged cell-free RNA (cfRNA) sequencing of maternal plasma in 48 samples—comprising early-onset PE, late-onset...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: bcd27a76-ec5e-5968-8867-1262d265ab18
DOI: 10.20944/preprints202503.2118.v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Improved Prediction Accuracy for Late-Onset Preeclampsia Using cfRNA Profiles: A Comparative Study of Marker Selection StrategiesDOI 10.20944/preprints202503.2118.v1Read

Development and validation of preeclampsia predictive models using key genes from bioinformatics and machine learning approachesSemantic proximity Read

Blood biomarkers representing maternal-fetal interface tissues used to predict early-and late-onset preeclampsia but not COVID-19 infectionSemantic proximity Read

Early pathways, biomarkers, and four distinct molecular subclasses of preeclampsia: The intersection of clinical, pathological, and high-dimensional biology studiesSemantic proximity Read

Early prediction of preeclampsia in pregnancy with circulating cell-free RNASemantic proximity Read

Rethinking Risk Prediction in Preeclampsia: From Biomarkers to Mechanistic Phenotypes and Longitudinal Models.Semantic proximity Read

Establishment and Verification of a Predictive Model for Preeclampsia Based on Bioinformatics AnalysisSemantic proximity Read

A cost-effective machine learning-based method for preeclampsia risk assessment and driver genes discoverySemantic proximity Read

Multi‑algorithm deconvolution and entropy‑based tracing identify maternal‑fetal transcriptional drivers of preeclampsiaSemantic proximity Read

Identification of key extracellular proteins as biomarkers for prediction and assessment of preeclampsiaSemantic proximity Read

Early prediction of preeclampsia in pregnancy with cell-free RNASemantic proximity Read

Characterizing immune variation and diagnostic indicators of preeclampsia by single-cell RNA sequencing and machine learningSemantic proximity Read

Distinct molecular processes in placentae involved in two major subtypes of preeclampsiaSemantic proximity Read