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

Genome-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

Classification of Illness

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...

End Point Prediction: Gran Plot

End Point Prediction: Gran Plot

A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...

You might also read

Related Articles

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

Sort by

Same author

High mobility group box 1 (HMGB1) levels in the placenta and in serum in preeclampsia.

American journal of reproductive immunology (New York, N.Y. : 1989)·2011

Same author

Destabilization of coxsackievirus b3 genome integrated with enhanced green fluorescent protein gene.

Intervirology·2011

Same author

[Clinicopathological features of primary splenic histiocytic sarcoma: a case report and literature review].

Zhonghua xue ye xue za zhi = Zhonghua xueyexue zazhi·2011

Same author

[Comparison of treatment with micro endoscopic discectomy and posterior lumbar interbody fusion using single and double B-Twin expandable spinal spacer].

Zhonghua wai ke za zhi [Chinese journal of surgery]·2011

Same author

Virtual transplantation in designing a facial prosthesis for extensive maxillofacial defects that cross the facial midline using computer-assisted technology.

The International journal of prosthodontics·2011

Same author

Total synthesis of phorboxazole A via de novo oxazole formation: convergent total synthesis.

Journal of the American Chemical Society·2010

Search research articles

Related Experiment Video

Updated: Sep 17, 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

Enhancing microbe-disease association prediction via multi-view graph convolution and latent feature learning.

Bo Wang¹, Peilong Wu¹, Xiaoxin Du¹

¹School of Computer and Control Engineering, Qiqihar University, Qiqihar, Heilongjiang 161006, China; Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis, Qiqihar University, Qiqihar, Heilongjiang 161006, China.

Computational Biology and Chemistry

|July 2, 2025

Summary

This study introduces MVGCVAE, a novel computational model for predicting microbe-disease associations. It effectively integrates multi-view graph convolutional networks and variational autoencoders to improve accuracy in understanding microbe-disease relationships.

Keywords:

Dynamic kernel matrix weighting Feature fusion Microbe-disease associations, Graph convolutional networks (GCNs)Variational autoencoder (VAE)

More Related Videos

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

Related Experiment Videos

Last Updated: Sep 17, 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: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

Area of Science:

Microbiology
Computational Biology
Bioinformatics

Background:

Microbes are integral to disease development, progression, and treatment.
Accurate microbe-disease association prediction faces challenges due to missing data and inadequate feature fusion.

Purpose of the Study:

To develop an innovative computational model, MVGCVAE, for enhanced microbe-disease association prediction.
To address limitations in existing methods concerning data sparsity and feature integration.

Main Methods:

MVGCVAE synergistically integrates multi-view graph convolutional networks (GCNs), variational autoencoders (VAEs), and dynamic kernel matrix weighting.
An attention mechanism fuses features from multiple similarity networks, followed by GCN-based representation learning.
Variational inference via VAEs optimizes node representations, while a dynamic weighted kernel strategy adaptively integrates embeddings.

Main Results:

MVGCVAE demonstrated superior performance compared to six existing methods across multiple evaluation metrics.
The model effectively handles sparse data and nonlinear relationships in microbe-disease associations.
Case studies confirmed the reliability and predictive accuracy of the MVGCVAE model.

Conclusions:

MVGCVAE offers a significant advancement in computational models for microbe-disease association prediction.
The model's innovative integration of GCNs, VAEs, and dynamic weighting enhances predictive capabilities.
MVGCVAE provides a reliable tool for exploring the complex interplay between microbes and diseases.