Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

5.0K
Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved...
5.0K
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

17.1K
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...
17.1K
Genomics02:02

Genomics

42.0K
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...
42.0K

You might also read

Related Articles

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

Sort by
Same author

Helical quaternary amine polypeptide programs membrane stress to drive immunogenic cell death and cytosolic gene delivery for cancer immunotherapy.

Biomaterials·2026
Same author

Explaining hidden mechanisms: a generative model for causal graphs with nonlinear latent factors.

Frontiers in artificial intelligence·2026
Same author

MetaCCI: meta cell-cell interaction inference and its application to CCIs characteristics of MDS.

Bioinformatics (Oxford, England)·2026
Same author

Statistical Inference of Phenotype-Specific Molecular Mechanisms from Cell Line-Specific Gene Regulatory Networks with Application to Quizartinib Sensitivity.

International journal of molecular sciences·2026
Same author

Network-constrained Random Lasso for biologically interpretable gene network inference across unequal sample sizes.

PloS one·2026
Same author

Gene Network Enrichment Analysis and Its Application to Explore Enriched Immune Disease Pathways for Gene Network of Acute Myeloid Leukemia Cell Lines.

Journal of computational biology : a journal of computational molecular cell biology·2026

Related Experiment Video

Updated: Apr 18, 2026

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

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

Published on: September 20, 2024

2.4K

Sparse overlapping group lasso for integrative multi-omics analysis.

Heewon Park1, Atushi Niida, Satoru Miyano

  • 1Human Genome Center, the Institute of Medical Science, the University of Tokyo , Tokyo, Japan .

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|January 29, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for identifying cancer driver genes by analyzing gene networks. The approach enhances biological interpretability and prediction accuracy in genomic data analysis.

Keywords:
gene networksgraphgroup sparse regularizationmulti-omics analysisuncovering driver genes

More Related Videos

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
08:27

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

Published on: July 27, 2021

5.1K
RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level
11:04

RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level

Published on: May 19, 2019

10.6K

Related Experiment Videos

Last Updated: Apr 18, 2026

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

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

Published on: September 20, 2024

2.4K
Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
08:27

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

Published on: July 27, 2021

5.1K
RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level
11:04

RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level

Published on: May 19, 2019

10.6K

Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Cancer is a complex disease involving gene combinations, not individual genes.
  • Gene networks are vital for understanding cancer's heterogeneous nature.
  • Current methods for gene network analysis often lead to overfitting and lack biological interpretability.

Purpose of the Study:

  • To develop a novel method for identifying driver genes and their interactions within cancer gene networks.
  • To achieve both "groupwise sparsity" and "within group sparsity" for more accurate driver gene identification.
  • To improve the biological interpretability of genomic data analysis in cancer research.

Main Methods:

  • Proposed a sparse overlapping group lasso method using duplicated predictors in an extended space.
  • Incorporated biological pathway information and predefined overlapping feature groups.
  • Validated the method using Monte Carlo simulations and The Cancer Genome Atlas (TCGA) project data.

Main Results:

  • The proposed method effectively identifies driver genes and their interactions.
  • Demonstrated improved regression model fitting, including feature selection and prediction accuracy.
  • Successfully uncovered potential cancer driver genes from TCGA multi-omics data, with strong supporting evidence.

Conclusions:

  • The novel sparse overlapping group lasso method is a powerful tool for identifying cancer driver genes.
  • The method facilitates integrative multi-omics analysis and enhances biological understanding of cancer.
  • This approach offers improved accuracy and interpretability compared to existing gene network analysis techniques.