Identification and verification of diagnostic biomarkers for deep infiltrating endometriosis based on machine learning algorithms

Identification and verification of diagnostic biomarkers for deep infiltrating endometriosis based on machine learning algorithms

Abstract 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 the Gene Expression Omnibus (GEO) database, using bioinformatics methods and thre...

Full description

Saved in:
Bibliographic Details
Main Authors: Shanping Shi, Chao Huang, Xiaojian Tang, Hua Liu, Weiwei Feng, Chen Chen
Format: Article
Language:English
Published: BMC 2024-11-01
Series:Journal of Biological Engineering
Subjects:
Deep infiltrating endometriosis (DIE)
USP14
Machine learning algorithms
Single-cell RNA sequencing (scRNA-seq)
Immunohistochemical staining
Online Access:https://doi.org/10.1186/s13036-024-00466-9
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://doi.org/10.1186/s13036-024-00466-9

Similar Items