ArticleFrontiers in immunology2024
Development and validation of preeclampsia predictive models using key genes from bioinformatics and machine learning approaches.
Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- Machine learning models for predicting postpartum convulsions using clinical indicators from PMA Ethiopia data.Scientific reports · 2026Article
- Identification of potential biomarkers and therapeutic targets for liver cirrhosis based on Mendelian randomization and machine learning.Biochemistry and biophysics reports · 2026Article
- Precision Biomarker Identification in Gynecological Cancers Using Coexpression Networks and Attention-Based LSTM in Healthcare 4.0.Diagnostics (Basel, Switzerland) · 2026Article
- Article
- Transcriptome Analysis, Machine Learning, and Experimental Identification of CDK7 Affecting the Progression of Pregnancy-induced Hypertension by Influencing Macrophage Polarization.Current molecular medicine · 2026Article
- Decoding Dementia Mechanisms: Identification of Key Oligodendrocyte- Associated Genes through Integrative Bioinformatics and Machine Learning.Current topics in medicinal chemistry · 2026Article
- Machine learning-based prediction of 30-day unplanned readmission risk in day surgery lung cancer patients after lobectomy or sublobectomy: a real-world study.Frontiers in medicine · 2026Article
- Single-cell mapping of maternal-fetal cross-talk in preeclampsia.Research square · 2025Article
- Single-cell RNA-seq reveals gene expression heterogeneity in NSCLC and its link to the immune microenvironment.Discover oncology · 2025Article
- AttBiomarker: unveiling preeclampsia biomarkers and molecular pathways through two-stage gene selection techniques and attention-based CNN with gene regulatory network analysis.Briefings in bioinformatics · 2025Article
- Identifying preeclampsia-associated key module and hub genes via weighted gene co-expression network analysis.Scientific reports · 2025Article
- Development and validation of a multi-modality system combining radiomics and deep learning for predicting mid-pregnancy complications and enabling timely pregnancy care.Frontiers in pediatrics · 2025Article
- Article
- Identification of Key Genes Involved in Seed Germination ofInternational journal of molecular sciences · 2024Article
- Reimagining Placental Perfusion in Preeclampsia: Integrating Doppler Ultrasound, Three-dimensional Vascular Indices, and Predictive Artificial Intelligence.Journal of medical ultrasoundReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Preeclampsia (PE) poses significant diagnostic and therapeutic challenges. This study aims to identify novel genes for potential diagnostic and therapeutic targets, illuminating the immune mechanisms involved. Methods: Three GEO datasets were analyzed, merging two for training set, and using the third for external validation. Intersection analysis of differentially expressed genes (DEGs) and WGCNA highlighted candidate genes. These were further refined through LASSO, SVM-RFE, and RF algorithms to identify diagnostic hub genes. Diagnostic efficacy was assessed using ROC curves. A predictive nomogram and fully Connected Neural Network (FCNN) were developed for PE prediction. ssGSEA and correlation analysis were employed to investigate the immune landscape. Further validation was provided by qRT-PCR on human placental samples. Result: Five biomarkers were identified with validation AUCs: Conclusion:
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.