ArticleFrontiers in genetics2022
A Joint Model of Random Forest and Artificial Neural Network for the Diagnosis of Endometriosis.
Article in Frontiers in genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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Who cites it
7 citing papers in PubMed, 15 citations in OpenAlex.
- A Machine Learning-Based Prediction Model for Deep Infiltrating Endometriosis.International journal of women's health · 2026Article
- Automatic Feature Selection for Imbalanced Echocardiogram Data Using Event-Based Self-Similarity.Diagnostics (Basel, Switzerland) · 2025Article
- Lipoproteins and metabolites in diagnosing and predicting Alzheimer's disease using machine learning.Lipids in health and disease · 2024Article
- Exploring the diagnostic value of ultrasound radiomics for neonatal respiratory distress syndrome.BMC pediatrics · 2024Article
- Identification of pyroptosis-associated genes with diagnostic value in calcific aortic valve disease.Frontiers in cardiovascular medicine · 2024Article
- Integrated analyses delineate distinctive immunological pathways and diagnostic signatures for Behcet's disease by leveraging gene microarray data.Immunologic research · 2023Article
- Short- and long-term effects of radiation exposure at low dose and low dose rate in normal human VH10 fibroblasts.Frontiers in public health · 2023Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Endometriosis (EM), an estrogen-dependent inflammatory disease with unknown etiology, affects thousands of childbearing-age couples, and its early diagnosis is still very difficult. With the rapid development of sequencing technology in recent years, the accumulation of many sequencing data makes it possible to screen important diagnostic biomarkers from some EM-related genes. In this study, we utilized public datasets in the Gene Expression Omnibus (GEO) and Array-Express database and identified seven important differentially expressed genes (DEGs) (
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Registered trials
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