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...

DNA Microarrays

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

You might also read

Related Articles

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

Sort by

Same author

Towards an ecosystem of clinical decision support tools for precision cancer medicine.

NPJ precision oncology·2026

Same author

European cancer data sharing: Analyses of an international survey.

International journal of medical informatics·2026

Same author

Proteomic Profiling Using a Proximity Extension Assay Panel on Serum Samples Stored at -25°C Over Several Decades.

Proteomics·2026

Same author

Systematic interrogation of drug sensitivities in melanoma reveals potent synergistic and antagonistic drug combinations with translational potential.

Communications medicine·2026

Same author

Exploring Taq polymerase induced mutations in part of BRAF exon 15 by sequencing and mutation enrichment.

Mutation research·2026

Same author

TrialMatchAI: an end-to-end AI-powered clinical trial recommendation system to streamline patient-to-trial matching.

Nature communications·2026

Same journal

SNPio: a Python interface for population genomic data processing.

BMC bioinformatics·2026

Same journal

SpaHNR: a spatial domain identification method via sparse attention-based hierarchical node representation and multi-view contrastive learning.

BMC bioinformatics·2026

Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026

Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026

Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026

Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 30, 2026

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

Identifying pathogenic processes by integrating microarray data with prior knowledge.

Ståle Nygård¹, Trond Reitan, Trevor Clancy

¹Bioinformatics Core Facility, Institute for Medical Informatics, Oslo University Hospital, Oslo, Norway. staaln@ifi.uio.no.

BMC Bioinformatics

|April 25, 2014

Summary

This summary is machine-generated.

This study introduces a new Bayesian method to identify functional gene groups from microarray data, improving disease pathway discovery. The approach reveals known pathogenic processes and novel molecular connections for better disease understanding.

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

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

Related Experiment Videos

Last Updated: Apr 30, 2026

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

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

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

Area of Science:

Bioinformatics
Systems Biology
Computational Biology

Background:

Identifying molecular pathways in disease etiology is crucial but challenging.
High-throughput methods often yield gene sets with poor overlap to known disease pathways.
Understanding molecular processes underlying disease progression requires novel analytical approaches.

Purpose of the Study:

To present a novel Bayesian statistical method for identifying functional molecular groups from differentially expressed genes.
To improve the interpretation of genome-wide screen findings in disease research.
To uncover novel connections and functional modules involved in disease etiology.

Main Methods:

Utilizing Bayesian statistics to group co-regulated genes from microarray data.
Incorporating external molecular interaction data as priors for group assignments.
Employing Markov chain Monte Carlo (MCMC) sampling for reliable grouping.

Main Results:

Simulations demonstrated improved group identification accuracy compared to traditional clustering, particularly with small sample sizes.
Application to a heart failure dataset identified clusters related to extracellular matrix and carbohydrate metabolism.
Analysis of a melanoma dataset revealed a main cluster associated with keratinocyte differentiation.

Conclusions:

The developed method successfully identified clusters overlapping with known pathogenic processes.
The approach also highlighted novel molecular connections extending beyond classical disease pathways.
This facilitates a deeper understanding of molecular mechanisms in disease.