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Machine learning-integrated molecular subtyping reveals two biologically distinct endometriosis subtypes in the EndometDB database.

Endometriosis affects approximately 10% of reproductive-age women, with a diagnostic delay of 7-10 years. Despite clinical heterogeneity, current rASRM staging poorly predicts treatment outcomes. Molecular subtyping may reveal biologically meaningful …

Published: Aug. 26, 2026, midnight
Deciphering the Diagnostic and Natural Therapeutic Implications of Necrosis by Sodium Overload and NK Signatures in Endometriosis Patients.

Endometriosis (EMT) is characterized by a chronic inflammatory disorder in the female reproductive system, posing significant challenges to global women's health. Necrosis by Sodium Overload (NESCO) is a novel immunogenic …

Published: May 18, 2026, midnight
Identification of biomarkers for endometriosis based on summary-data-based Mendelian randomization and machine learning.

Endometriosis (EM) significantly impacts the quality of life, and its diagnosis currently relies on surgery, which carries risks and may miss early lesions. Noninvasive biomarkers are urgently needed for early …

Published: April 8, 2025, midnight
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