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Integrated bioinformatics analysis and machine learning identifies FZD4, SRPX2, and COL8A1 as angiogenesis hub genes in endometriosis.

This study aims to identify angiogenesis-associated genes (AAGs) in endometriosis (EM) by integrating bioinformatics analysis with machine learning, and to investigate their underlying mechanisms. Differentially expressed genes (DEGs) were screened …

Published: Oct. 26, 2025, midnight
SPP1 as a key modulator of M2 macrophage polarization promotes endometriosis progression via activation of the FAK/PI3K/AKT pathway: A bioinformatics and experimental study.

Endometriosis (EMs) is a gynecological disorder characterized by chronic inflammation and an aberrant immune microenvironment. In this study, we integrated the GSE6364 dataset from the GEO database to identify differentially …

Published: Sept. 17, 2025, midnight
Association between dietary inflammatory index and endometriosis in the US population: A cross-sectional study.

Endometriosis is a chronic inflammatory disorder affecting reproductive-aged women. The Dietary Inflammatory Index (DII), a measure of diet-related inflammation, has been implicated in various inflammatory diseases, but its role in …

Published: July 24, 2025, midnight
Bioinformatics analysis to identify environmental endocrine chemicals that target endometriosis genes.

Endometriosis (EMS) significantly impacts women's health and is influenced by genetic factors and environmental endocrine-disrupting chemicals (EDCs), which interfere with hormonal balance. Using the Gene Expression Omnibus database, we identified …

Published: April 4, 2025, midnight
Identification of common diagnostic genes and molecular pathways in endometriosis and systemic lupus erythematosus by machine learning approach and in vitro experiment.

Growing research suggests that endometriosis and systemic lupus erythematosus (SLE) are both chronic inflammatory diseases and closely related, but no studies have explored their common molecular characteristics and underlying mechanisms. …

Published: Jan. 1, 2025, midnight
Identification and verification of diagnostic biomarkers for deep infiltrating endometriosis based on machine learning algorithms.

This study addresses the challenges in the early diagnosis of deep infiltrating endometriosis (DIE) by exploring the potential role of the deubiquitinating enzyme USP14. By analyzing the GSE141549 dataset from …

Published: Nov. 25, 2024, midnight
Machine learning-based integrated identification of predictive combined diagnostic biomarkers for endometriosis.

Background: Endometriosis (EM) is a common gynecological condition in women of reproductive age, with diverse causes and a not yet fully understood pathogenesis. Traditional diagnostics rely on single diagnostic biomarkers …

Published: Nov. 27, 2023, midnight
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