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Integrating inflammatory biomarkers and demographic variables with machine learning to predict endometriosis risk.

This study explores the relationship between inflammatory biomarkers and the risk of endometriosis, aiming to develop a predictive model using National Health and Nutrition Examination Survey (1999-2006) data. The dataset …

Published: Nov. 27, 2025, midnight
Detection of peritoneal, ovarian, and bowel endometriosis using FTIR spectroscopy and machine learning.

This study evaluated the diagnostic potential of Fourier-transform infrared (FTIR) spectroscopy combined with machine learning for the detection of ovarian, bowel, and peritoneal endometriosis. The Boruta algorithm was applied to …

Published: Nov. 23, 2025, midnight
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
Unraveling shared diagnostic genes and cellular microenvironmental changes in endometriosis and recurrent implantation failure through multi-omics analysis.

Endometriosis and Recurrent Implantation Failure (RIF) are both pivotal clinical issues within the realm of reproductive medicine, sharing significant overlap in their pathophysiological mechanisms. However, research exploring the commonalities between …

Published: March 17, 2025, 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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