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...

You might also read

Related Articles

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

Sort by

Same author

Acupoint electrical stimulation with implantable carbon nanotube electrodes mitigates sciatic nerve injury-induced muscle atrophy.

Biomedical materials (Bristol, England)·2026

Same author

Carbon nanotube fiber electrode-based implantable electroacupuncture ameliorates myocardial ischemia in rats via the FXR/SHP pathway.

Nanotechnology·2026

Same author

EnzymeMiner 2.0: advancing automated enzyme discovery with expansive sequence mining and smart property analysis.

Nucleic acids research·2026

Same author

Tissue clock-guided prediction and intervention of futile recanalization: towards precision therapy in mechanical thrombectomy.

Frontiers in neurology·2026

Same author

Molecular PET imaging of tumor-associated macrophages in precision oncology.

Cancer letters·2026

Same author

Stepwise engineering of AmpR-based whole-cell biosensors for broad-spectrum detection and high-throughput screening of β-lactam compounds.

Biosensors & bioelectronics·2026

Same journal

A novel pathogenic synonymous DHCR7 variant unveiled by aberrant splicing in Smith-Lemli-Opitz syndrome.

Human genetics·2026

Same journal

Blending borders: reconstructing the genetic history of the Sindhi population.

Human genetics·2026

Same journal

Within-sibling attenuation of polygenic risk score accuracy: investigating the effects of principal component analysis, LD score regression, and mixed model association in the UK Biobank.

Human genetics·2026

Same journal

Long-read genome sequencing resolves a de novo complex 18q12.1q21.2 triplication causing partial tetrasomy and reveals its underlying mechanism.

Human genetics·2026

Same journal

A genetic variant of adenylate cyclase 7 associated with ulcerative colitis shows impaired function and G-protein-coupled receptor signaling.

Human genetics·2026

Same journal

AI in variant analysis: fast track to genetic diagnoses.

Human genetics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Nov 22, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

A network-based machine-learning framework to identify both functional modules and disease genes.

Kuo Yang^1,2, Kezhi Lu^1,3, Yang Wu⁴

¹School of Computer and Information Technology, Institute of Medical Intelligence, Beijing Jiaotong University, Beijing, 100044, China.

|January 7, 2021

Summary

This summary is machine-generated.

This study introduces MapGene, a novel machine learning framework for identifying disease-associated genes and functional modules. MapGene effectively addresses challenges posed by incomplete biological networks, improving candidate gene discovery.

More Related Videos

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Related Experiment Videos

Last Updated: Nov 22, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Area of Science:

Computational biology
Genomics
Machine learning

Background:

Identifying disease genes is crucial for understanding disease mechanisms and phenotypes.
Current computational methods struggle with incomplete interactome networks, hindering novel candidate gene discovery.

Purpose of the Study:

To develop an integrated network-based machine learning framework for identifying functional modules and disease candidate genes.
To propose a novel disease gene-prioritizing method, MapGene, that leverages functional modules and network proximity.

Main Methods:

Developed a semi-supervised non-negative matrix factorization model to identify disease-related functional modules.
Proposed MapGene, a method integrating functional module correlations and network closeness for gene prioritization.
Validated the framework on large-scale benchmark datasets and specific disease cases (Parkinson's, diabetes).

Main Results:

Identified functional modules with high homogeneity and strong gene interactions.
MapGene demonstrated superior performance compared to state-of-the-art algorithms in candidate gene prioritization.
The framework effectively mitigated the impact of incomplete interactome data, yielding reliable gene rankings.

Conclusions:

The proposed integrated framework successfully predicts both functional modules and disease candidate genes.
MapGene offers a robust approach for discovering underlying functional modules and reliable candidate genes in human diseases.
This methodology has the potential to significantly advance disease gene identification and molecular mechanism research.