Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

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

International journal of molecular sciences - 13 Apr 2026

Espino-Y-Sosa Salvador, Moreno-Verduzco Elsa Romelia, Monroy-Muñoz Irma Eloisa, Solis-Paredes Juan Mario, Pérez Durán Javier, Rojas Zepeda Lourdes, Torres-Torres Johnatan

Abstract excerpt

Preeclampsia remains a major cause of maternal and perinatal morbidity and mortality worldwide, yet progress in biomarker discovery and predictive modeling has translated only modestly into clinically meaningful risk stratification. Over the past two decades, numerous biomarkers and predictors reflecting placental-angiogenic dysfunction, maternal cardiovascular maladaptation, and inflammatory-metabolic stress...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Biomarkers
Share to Machine Learning

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Phenotype

Share to Pre-Eclampsia

Share to Prediction Algorithms

Share to Predictive Learning Models

Share to Pregnancy

Share to Risk Assessment

Source

Open in PubMed Open full text

Identifiers

PMID: 42074122
DOI: 10.3390/ijms27083480
PMCID: PMC13115805

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 8 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Rethinking Risk Prediction in Preeclampsia: From Biomarkers to Mechanistic Phenotypes and Longitudinal Models.PMID 42074122Read ↗

Contemporary prediction of preeclampsiaSemantic proximity Read ↗

Subtypes of Preeclampsia: Recognition and Determining Clinical UsefulnessSemantic proximity Read ↗

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

Development and validation of an interpretable longitudinal preeclampsia risk prediction using machine learningSemantic proximity Read ↗

An Interpretable Longitudinal Preeclampsia Risk Prediction Using Machine LearningSemantic proximity Read ↗

Improved Prediction Accuracy for Late-Onset Preeclampsia Using cfRNA Profiles: A Comparative Study of Marker Selection StrategiesSemantic proximity Read ↗

Clinical tools and biomarkers to predict preeclampsiaSemantic proximity Read ↗

Prediction of Preeclampsia from Clinical and Genetic Risk Factors in Early and Late Pregnancy Using Machine Learning and Polygenic Risk ScoresSemantic proximity Read ↗

An updated view on the origin and use of angiogenic biomarkers for preeclampsiaSemantic proximity Read ↗

Phenotypic subclassification of preeclampsia through cluster analysis of preterm birth-related factors.Semantic proximity Read ↗

Pharmacogenomics of Preeclampsia therapies: Current evidence and future challenges for clinical implementationSemantic proximity Read ↗

Machine Learning for Dynamic and Short-term Prediction of Preeclampsia Using Routine Clinical and Laboratory DataSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.