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...

You might also read

Related Articles

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

Sort by

Same author

Application of a Blood-Brain Barrier Organ-on-a-Chip Model for Assessment of Countermeasure Efficiency Against Eastern Equine Encephalitis Virus.

Viruses·2026

Same author

Computational and Proteomic Analyses Reveal Cardiac Dysfunction and Heart Failure-Associated Biomarker Secretion from Venezuelan Equine Encephalitis Virus TC83-infected human IPSC-derived cardiomyocytes.

bioRxiv : the preprint server for biology·2026

Same author

Artificial Intelligence in Bulk RNA-Seq: Challenges and Potential Solutions.

Computational and structural biotechnology journal·2026

Same author

Broad-spectrum inhibitor of non-structural protein 2 protease shows therapeutic efficacy against Venezuelan equine encephalitis viral infection.

European journal of medicinal chemistry·2026

Same author

Transcriptomic profiling of human endothelial cells infected with venezuelan equine encephalitis virus reveals NRF2 driven host reprogramming mediated by omaveloxolone treatment.

Frontiers in genetics·2026

Same author

Mass production of IgY-containing tablets for COVID-19 transmission control.

Scientific reports·2025

Same journal

Literature-informed gene extraction and ranking for multimodal data fusion.

Briefings in bioinformatics·2026

Same journal

SA-MTP: a structure-aware framework for multifunctional therapeutic peptide annotation.

Briefings in bioinformatics·2026

Same journal

Genome assemblies and annotations are not static and need support for tracking their evolution.

Briefings in bioinformatics·2026

Same journal

A historical journey of metabolite-protein interaction discovery: from data harmonization to AI-driven prediction.

Briefings in bioinformatics·2026

Same journal

Bridging local-global transmembrane protein contexts with contrastive pretraining for alignment-free pathogenicity prediction.

Briefings in bioinformatics·2026

Same journal

Prediction of drug hypersensitivity by comprehensive modeling of HLA-peptidomes.

Briefings in bioinformatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 30, 2025

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

Classification-based pathway analysis using GPNet with novel P-value computation.

Hao Lu¹, Mostafa Rezapour¹, Haseebullah Baha²

¹Center for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC 27101, United States.

Briefings in Bioinformatics

|January 29, 2025

Summary

This summary is machine-generated.

Gene PointNet (GPNet), a deep learning method, improves pathway analysis for large datasets. It outperforms traditional and other machine learning techniques, especially in low signal-to-noise ratio scenarios, enhancing accuracy and reliability.

Keywords:

P-value computation Gene PointNet bioinformatics deep learning gene interactions pathway analysis

More Related Videos

Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish

Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish

Published on: October 27, 2017

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Related Experiment Videos

Last Updated: May 30, 2025

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish

Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish

Published on: October 27, 2017

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Pathway analysis is crucial for identifying biological pathways linked to diseases using gene expression data.
Traditional methods like Over-Representation Analysis (ORA) and Functional Class Scoring (FCS) face challenges with large, multi-center datasets due to low signal-to-noise ratios (SNR) and high sample counts.

Purpose of the Study:

To introduce Gene PointNet (GPNet), a novel deep learning approach for pathway analysis.
To address the limitations of existing methods in handling large-scale, low-SNR biological datasets.
To develop a robust pathway analysis tool suitable for multi-center studies.

Main Methods:

Utilized a deep learning-based classification method, Gene PointNet (GPNet).
Developed a novel P-value computation approach incorporating confusion matrix analysis.
Validated the method using simulated data and The Cancer Genome Atlas (TCGA) breast cancer RNA-Seq data.

Main Results:

GPNet demonstrated superior performance compared to ORA, FCS, logistic regression, support vector machines, DeepHisCom, and PASNet.
The method proved robust and reliable on low-SNR, large-sample datasets.
GPNet significantly reduced Type I errors while improving statistical power.

Conclusions:

Gene PointNet (GPNet) offers a powerful and reliable solution for pathway analysis in large, multi-center studies.
The deep learning approach effectively overcomes the limitations of traditional methods in complex genomic datasets.
GPNet enhances the accuracy and efficiency of identifying biological pathways associated with various conditions.