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Serum Fingerprinting-Based Integrative Dual-Omics Machine Learning for Endometriosis-Associated Ovarian Cancer.

Dual-omics, by integrating molecular information from two distinct dimensions, can offer more comprehensive perspective for complex disease. Herein, we developed an efficient functionalized mesoporous nanoparticle-coupled laser desorption/ionization mass spectrometry (fMNPLDI-MS) …

Published: Oct. 16, 2025, midnight
Potential biomarkers for early detection of endometriosis: current state of art (what we know so far).

Endometriosis is a chronic gynecological condition characterized by the presence of endometrial-like tissue outside the uterine cavity. Its diagnosis remains a significant clinical challenge, often delayed by 7 to 12 …

Published: Oct. 13, 2025, midnight
Identification and validation of lactate-related gene signatures in endometriosis for clinical evaluation and immune characterization by WGCNA and machine learning.

Endometriosis is a common benign gynecologic disease in women of reproductive age, and its manifestations remarkably decrease quality of life. Lactate, as a metabolite, exerts prominent effects across a wide …

Published: Oct. 7, 2025, midnight
Analysis of diagnostic apoptosis-related biomarkers and immune cell infiltration characteristics in endometriosis by integrating bioinformatics and machine learning.

Endometriosis (EMs) is a chronic disease affecting millions of women worldwide, yet its pathogenesis remains unclear, and current diagnostic methods are limited. This study based on the EMs dataset from …

Published: Sept. 29, 2025, midnight
Development and validation of a machine learning-based predictive model for live birth outcomes following fresh embryo transfer in patients with endometriosis.

This study aims to develop a machine learning-based predictive model for patients with endometriosis, with the goal of precisely identifying key factors and reliable predictive markers that influence live birth …

Published: Sept. 23, 2025, midnight
Development of a Novel Machine Learning Model for Automatic Assessment of Quality of Transvaginal Ultrasound Images From Multi-Annotator Labels.

Accurate diagnosis of pathology from ultrasound images is reliant upon images of a suitable diagnostic quality being acquired. This study aimed to create a novel machine learning model to automatically …

Published: Sept. 22, 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
Identification and Exploration of Novel B Cell Infiltration-Related Biomarkers in Endometriosis.

To explore B cell infiltration-related genes in endometriosis (EM) and investigate their potential as diagnostic biomarkers.

Published: Sept. 10, 2025, midnight
A machine learning approach towards endometriosis screening using infrared spectra of urine.

Endometriosis diagnosis is challenging due to non-specific symptoms that overlap with other gynaecological conditions. This study proposes a non-invasive Machine Learning (ML) ‒ based urine test using Attenuated Total Reflection …

Published: Sept. 6, 2025, midnight
Machine learning prediction of clinical pregnancy in endometriosis patients following fresh IVF/ICSI-ET.

Fresh embryo transfer reduces waiting time and minimizes embryo cryodamage for endometriosis (EM) patients. The current prediction models for fresh embryo transfer outcomes in EM primarily rely on logistic regression, …

Published: Sept. 3, 2025, midnight
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