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

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

Protein-protein Interfaces

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...

Pharmacogenomics: Identification of New Drug Targets

Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...

Genomics

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...

Genome Annotation and Assembly

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.

You might also read

Related Articles

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

Sort by

Same author

Evaluation of analysis modes for RNA coexpression in single-cell and bulk tissue.

bioRxiv : the preprint server for biology·2026

Same author

Persistent hindrances to data re-use in single-cell genomics.

Scientific data·2026

Same author

Cluster replicability in single-cell and single-nucleus atlases of the mouse brain.

Nature communications·2026

Same author

On the state of protein function prediction: a report on the fourth CAFA challenge.

bioRxiv : the preprint server for biology·2026

Same author

Cell type-agnostic transcriptomic signatures enable uniform comparisons of neural maturation.

PLoS biology·2026

Same author

The plant hormone, 6-benzylaminopurine, ameliorates obesity in male and female mice while on a high-fat diet.

Molecular metabolism·2026

Same journal

Exploring potential strategies to enhance memory and cognition in aging mice.

F1000Research·2026

Same journal

Construction an Implicit Block Multi-Steps Approach for Solving Sixth-Order Fractional Differential Equations.

F1000Research·2026

Same journal

Kansei Engineering in the Evolving Service Sector: A Decade of Insights.

F1000Research·2026

Same journal

A Safety-First Mindset: Role of Patient Safety Culture in Enhancing Healthcare Workers' Emotional Intelligence.

F1000Research·2026

Same journal

Decoding Decisions: Personality-Interest Motivational Sequences as Predictors of Career Paths.

F1000Research·2026

Same journal

Beyond the Transparent Barrier: A Domain Visualization and Integrative Review of Contemporary Research on Gender-Based Professional Stasis.

F1000Research·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 8, 2026

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Progress and challenges in the computational prediction of gene function using networks.

Paul Pavlidis¹, Jesse Gillis²

¹Centre for High-Throughput Biology and Department of Psychiatry, University of British Columbia, Vancouver, V6T1Z4, Canada.

|August 13, 2013

Summary

This summary is machine-generated.

Serious confounds in network data hinder accurate gene function prediction. Current methods often overstate success by re-annotating known genes, limiting reliable computational genomics advancements.

More Related Videos

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 8, 2026

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

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:

Computational genomics
Bioinformatics
Systems biology

Background:

Computational approaches are crucial for determining gene function.
Progress in gene function prediction beyond BLAST has been slow.
Network data analysis, particularly 'guilt by association,' is a common approach.

Purpose of the Study:

To unify arguments regarding confounds in network data for gene function prediction.
To address limitations in current computational genomics methods.
To increase the reliability and specificity of gene function prediction.

Main Methods:

Analysis of network data and its application in gene function prediction.
Critique of 'guilt by association' methods.
Review of cross-validation performance and generalizability.

Main Results:

Reported successes in network-based gene function prediction are often inflated.
Methods tend to re-annotate already well-annotated genes, leading to generic predictions.
Cross-validation performance often fails to generalize to new predictions.

Conclusions:

Significant confounds affect the reliability of network data for gene function prediction.
Recommendations are proposed to improve the accuracy and specificity of computational predictions.
Further research is needed to overcome current limitations in the field.