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

Integrated transcriptomic analysis and machine learning for characterizing diagnostic biomarkers and immune cell infiltration in fetal growth restriction

4 Sept 2024

Abstract excerpt

Background: Fetal growth restriction (FGR) occurs in 10% of pregnancies worldwide. Placenta dysfunction, as one of the most common causes of FGR, is associated with various poor perinatal outcomes. The main objectives of this study were to screen potential diagnostic biomarkers for FGR and to evaluate the function of immune cell infiltration in the process of FGR. Methods: Firstly, differential expression genes...

Topics

Join the communities discussing this publication.

Inflammation biomarkers and pathways
Pregnancy and preeclampsia studies
Reproductive System and Pregnancy

Source

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Identifiers

PMID: 39295860
DOI: 10.3389/fimmu.2024.1381795

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 1 Sept 2026.

Community interpretation

Discuss the evidence

Ask a question, add context or challenge an interpretation. Community activity is separate from scientific evidence strength.

0

discussions

0

replies

Citations encouraged

Claims remain attributable.

No discussion yet

Be the first to add a careful, cited interpretation.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Integrated transcriptomic analysis and machine learning for characterizing diagnostic biomarkers and immune cell infiltration in fetal growth restrictionPMID 39295860Read ↗

Identification of novel biomarkers and immune infiltration features of recurrent pregnancy loss by machine learningSemantic proximity Read ↗

Integrated Analysis of Key Genes and Pathways Involved in Fetal Growth Restriction and Their Associations With the Dysregulation of the Maternal Immune SystemSemantic proximity Read ↗

SERPINA3 and NDRG1 are critical diagnostic immune genes associated with macrophages in preeclampsiaSemantic proximity Read ↗

Identification and verification of diagnostic biomarkers in recurrent pregnancy loss via machine learning algorithm and WGCNASemantic proximity Read ↗

SERPINA3 and NDRG1 are critical diagnostic immune genes associated with macrophages in preeclampsiaSemantic proximity Read ↗

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

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

Unveiling immune tolerance pathways in preeclampsia placenta: implications for molecular targets and discovery of potential biomarkersSemantic proximity Read ↗

Role of necroptosis and immune infiltration in preeclampsia: novel insights from bioinformatics analysesSemantic proximity Read ↗

Transcriptomic analysis reveals potential biomarkers for early-onset pre-eclampsia using integrative bioinformatics and LASSO based approachSemantic proximity Read ↗

Recurrent Miscarriage: A machine learning approach to uncover key genes and immune InfiltrationSemantic proximity Read ↗

Exploring Novel Molecular Mechanisms Underlying Recurrent Pregnancy Loss in Decidual TissuesSemantic proximity Read ↗

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