Can a serum miRNA signature serve as a potential diagnostic biomarker for endometriosis (END)?
Endometriosis (EM), a prevalent gynecological disorder in reproductive-age women, lacks reliable noninvasive diagnostic tools. EM may be detected by neutrophil extracellular traps (NETs), which are essential to inflammation and immunological …
Development of a senescence-related lncRNA signature in endometrial cancer based on multiple machine learning models Frontiers
Endometriosis is a long-term health problem that affects a significant number of women globally. Among the various forms of endometriosis, ovarian endometriosis (OEM) is the most prevalent. This research aimed …
Machine Learning Model Using CT Radiomics Achieves High Accuracy in Differentiating Malignant and Benign Endometrial Tumors geneonline.com
CT radiomics-based explainable machine learning model for accurate differentiation of malignant and benign endometrial tumors: a two-center study BioMedical Engineering OnLine
Endometriosis is a common disease among women of childbearing age, and endoplasmic reticulum stress (ERS), a response involved in regulating protein homeostasis, has been linked to its pathogenesis. To identify …
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 …
Endometriosis (EMs) and recurrent miscarriage (RM) represent major reproductive health challenges. This study investigates the involvement of endothelial-mesenchymal transition (EndMT) in these conditions through integrative bioinformatics analysis, focusing on the …
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) …