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

Multiple Allele Traits

Multiple Allele Traits

The Concept of Multiple Allelism

Epistasis Analysis

Epistasis Analysis

Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...

Pleiotropy

Pleiotropy

Pleiotropy is the phenomenon in which a single gene impacts multiple, seemingly unrelated phenotypic traits. For example, defects in the SOX10 gene cause Waardenburg Syndrome Type 4, or WS4, which can cause defects in pigmentation, hearing impairments, and an absence of intestinal contractions necessary for elimination. This diversity of phenotypes results from the expression pattern of SOX10 in early embryonic and fetal development. SOX10 is found in neural crest cells that form melanocytes,...

You might also read

Related Articles

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

Sort by

Same author

Laplacian Dynamics and Kron Reduction in Species-Reaction Graphs of Chemical Reaction Networks.

Bulletin of mathematical biology·2026

Same author

What Is the Best Surgical Treatment for Ulnar Styloid Impaction Syndrome? - A Systematic Review.

The journal of hand surgery Asian-Pacific volume·2026

Same author

Purinergic signaling modulates endothelial-to-mesenchymal transition in human aortic valve endothelial cells.

BBA advances·2026

Same author

Mitotic reactivation and transcriptional bursting govern transcriptional noise in the early Drosophila embryo.

Development (Cambridge, England)·2026

Same author

Screening for Alzheimer's disease in the community using an AI-driven screening platform: design of the PREDICTOM study.

The journal of prevention of Alzheimer's disease·2026

Same author

Open and sustainable AI: challenges, opportunities and the road ahead in the life sciences.

Nature methods·2026

Same journal

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026

Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026

Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

Bioinformatics (Oxford, England)·2026

Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026

Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026

Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

Bioinformatics (Oxford, England)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Feb 20, 2026

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

Linking metabolic network features to phenotypes using sparse group lasso.

Satya Swarup Samal¹, Ovidiu Radulescu², Andreas Weber³

¹Algorithmic Bioinformatics, Bonn-Aachen International Center for IT, Bonn D-53113, Germany.

Bioinformatics (Oxford, England)

|October 28, 2017

Summary

This summary is machine-generated.

This study introduces a novel method using sparse group lasso (SGL) to identify phenotype-associated extreme currents (ECs) in metabolic networks. The approach effectively links biological pathways to clinical outcomes like cancer, outperforming existing methods.

More Related Videos

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

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Related Experiment Videos

Last Updated: Feb 20, 2026

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

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

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Area of Science:

Computational Biology
Systems Biology
Bioinformatics

Background:

Metabolic networks are crucial for understanding biological phenotypes.
Integrating '-omics' data with metabolic networks aids in phenotype analysis.
Extreme currents (ECs) represent algebraically decomposed sub-pathways in metabolic networks.

Purpose of the Study:

To develop a method for statistically associating metabolic sub-pathways (ECs) with clinical outcomes.
To propose a novel gene set analysis method based on phenotype-associated ECs.
To identify phenotype-associated ECs using gene expression data and sparse group lasso (SGL).

Main Methods:

Metabolic networks were decomposed into extreme currents (ECs).
Sparse group lasso (SGL) was employed to identify phenotype-associated ECs.
The method utilizes gene expression data and correlations among EC clusters.

Main Results:

The method was applied to KEGG metabolic networks for prostate cancer and glioblastoma multiforme.
Associations between network features and clinical outcomes (tumor vs. normal, survival time) were studied.
Simulations demonstrated superior performance compared to the global test method.

Conclusions:

The proposed SGL-based method effectively identifies phenotype-associated ECs.
This approach offers a powerful tool for gene set analysis in clinical contexts.
The method shows promise in linking metabolic network features to cancer phenotypes.