Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Modern Molecular Taxonomy

Modern Molecular Taxonomy

Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...

Applications of Molecular Taxonomy

Applications of Molecular Taxonomy

Molecular taxonomy has revolutionized the understanding and classification of bacteria, providing precise insights into their diversity, evolutionary relationships, and ecological roles. By utilizing molecular techniques such as DNA sequencing and fingerprinting, researchers have made significant strides in various fields related to bacterial studies.Resolving Taxonomic AmbiguitiesMolecular taxonomy has been instrumental in distinguishing closely related bacterial species initially thought to...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Evaluation of fatty liver disease self-care applications: a systematic review and evaluation of apps using the mobile application rating scale (MARS).

BMC gastroenterology·2026

Same author

Design and Validation of a Minimum Dataset for a Self-Care Mobile Application for Patients With Diabetic Retinopathy: Descriptive-Validation Study.

Health science reports·2026

Same author

Sensor-Equipped Digital Technologies for Monitoring and Detecting Depressive Disorders: A Systematic Review.

Health science reports·2026

Same author

The development and use of chatbots in enhancing health care access for underserved and vulnerable populations: a scoping review.

BMC public health·2025

Same author

Mobile health and postoperative self-care following glaucoma, cataract, or corneal transplant surgeries.

BMC ophthalmology·2025

Same author

Elevated serum apelin levels in breast cancer patients: A potential biomarker with limited histopathological correlations.

Frontiers in oncology·2025

Search research articles

Related Experiment Video

Updated: Jul 1, 2026

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

Identification of Important Diagnostic Genes in the Uterine Using Bioinformatics and Machine Learning.

Hossein Valizadeh Laktarashi¹, Milad Rahimi², Kimia Abrishamifar³

¹Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Medical Journal of the Islamic Republic of Iran

|June 9, 2025

Summary

New biomarkers MEX3B, CTRP2 (C1QTNF2), and AASS show promise for early diagnosis and improved prognosis in uterine corpus endometrial cancer (UCEC). Bioinformatics and deep learning identified these key genes for UCEC detection.

Keywords:

Bioinformatic Analysis Biomarker Deep learning UCEC Uterine Corpus Endometrial Carcinoma

More Related Videos

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

Published on: July 22, 2020

Author Spotlight: Advancing Reproductive Immunology with a Protocol for the Quantitative Evaluation of Endometrial Immune Cells

Author Spotlight: Advancing Reproductive Immunology with a Protocol for the Quantitative Evaluation of Endometrial Immune Cells

Published on: October 13, 2023

Related Experiment Videos

Last Updated: Jul 1, 2026

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

Published on: July 22, 2020

Author Spotlight: Advancing Reproductive Immunology with a Protocol for the Quantitative Evaluation of Endometrial Immune Cells

Author Spotlight: Advancing Reproductive Immunology with a Protocol for the Quantitative Evaluation of Endometrial Immune Cells

Published on: October 13, 2023

Area of Science:

Genomics
Bioinformatics
Machine Learning

Background:

Uterine corpus endometrial cancer (UCEC) is a prevalent malignancy.
Bioinformatics and deep learning offer powerful tools for analyzing genomic data and identifying disease biomarkers.
This study focuses on discovering critical genes for UCEC diagnosis and prognosis.

Purpose of the Study:

To identify novel diagnostic and prognostic biomarkers for UCEC using bioinformatics and machine learning.
To analyze RNA expression profiles and identify differentially expressed genes (DEGs) in UCEC patients.
To validate the efficacy of identified biomarkers through deep learning models and survival analysis.

Main Methods:

Differential gene expression analysis of UCEC patient RNA profiles using deep learning.
Survival analysis with COMBIO-ROC to assess prognostic biomarkers.
Examination of molecular pathways, protein-protein interaction (PPI) networks, and gene co-expression patterns.
Deep learning-based identification of diagnostic markers.

Main Results:

MEX3B, CTRP2 (C1QTNF2), and AASS were identified as novel biomarkers for UCEC.
The deep learning model demonstrated high accuracy with an R-squared value of 0.99, AUC of 1, and 97% accuracy.
Minimal MSE (5.1096067E-5) and RMSE (0.007) indicate precise predictive capabilities.

Conclusions:

MEX3B, CTRP2 (C1QTNF2), and AASS are validated as significant diagnostic biomarkers for UCEC.
These identified biomarkers hold promise for enhancing UCEC treatment strategies, improving patient outcomes, and enabling earlier diagnosis.