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

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

18.0K
Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
18.0K
Cancer-Critical Genes I: Proto-oncogenes01:33

Cancer-Critical Genes I: Proto-oncogenes

9.2K
Genes usually encode proteins necessary for the proper functioning of a healthy cell. Mutations can often cause changes to the gene expression pattern, thereby altering the phenotype.
When the function of certain critical genes, especially those involved in cell cycle regulation and cell growth signaling cascades, gets disrupted, it upsets the cell cycle progression. Such cells with unchecked cell cycles start proliferating uncontrollably and eventually develop into tumors.
Such genes that act...
9.2K
Genome Copying Errors02:46

Genome Copying Errors

4.5K
DNA replication is a well-evolved process that copies millions of base pairs with high fidelity during each cell division. Occasionally a wrong base or a long stretch of wrong bases may get added to the daughter strands. If the errors are left unchecked, cells might accumulate several mutations that might endanger their  survival. Therefore, the copying errors are checked and repaired at three levels.
4.5K
Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

6.0K
Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...
6.0K
Cancers Originate from Somatic Mutations in a Single Cell02:21

Cancers Originate from Somatic Mutations in a Single Cell

13.2K
Cancer arises from mutations in the critical genes that allow healthy cells to escape cell cycle regulation and acquire the ability to proliferate indefinitely. Though originating from a single mutation event in one of the originator cells, cancer progresses when the mutant cell lines continue to gain more and more mutations, and finally, become malignant. For example, chronic myelogenous leukemia (CML) develops initially as a non-lethal increase in white blood cells, which progressively...
13.2K
Cancer Survival Analysis01:21

Cancer Survival Analysis

472
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
472

You might also read

Related Articles

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

Sort by
Same author

DBCL-DFNet: Dual-Branch Contrastive Learning for Multi-Omics Dynamic Fusion.

Entropy (Basel, Switzerland)·2026
Same author

Time-Efficient and Informatic-Skill-Light Gap-Filling for Telomere-to-Telomere Genome Assembly.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

An efficient and interactive feature selection approach based on copula entropy for high-dimensional genetic data.

Scientific reports·2025
Same author

TRFill: synergistic use of HiFi and Hi-C sequencing enables accurate assembly of tandem repeats for population-level analysis.

Genome biology·2025
Same author

Cox-Sage: enhancing Cox proportional hazards model with interpretable graph neural networks for cancer prognosis.

Briefings in bioinformatics·2025
Same author

Melon ripeness detection by an improved object detection algorithm for resource constrained environments.

Plant methods·2024
Same journal

Untargeted metabolomics reveals the metabolic basis of sugar-acid balance and quality differentiation in melon.

Frontiers in plant science·2026
Same journal

Biogenic volatile organic compound emission characteristics of dominant tree species in temperate broad-leaved Korean pine forests in Northeast China.

Frontiers in plant science·2026
Same journal

Study on differences in flavonoid synthesis in <i>Xanthoceras sorbifolia</i> leaves based on transcriptome analysis.

Frontiers in plant science·2026
Same journal

Evolutionary diversification of the <i>STAYGREEN</i> gene family in <i>Nicotiana</i>.

Frontiers in plant science·2026
Same journal

Identification and fungicide sensitivity of <i>Monosporascus lespedezae</i> sp. nov. causing root rot of <i>Lespedeza davurica</i> in Gansu, China.

Frontiers in plant science·2026
Same journal

Editorial: Plant phenotyping for agriculture.

Frontiers in plant science·2026
See all related articles

Related Experiment Video

Updated: Sep 27, 2025

Detection of Copy Number Alterations Using Single Cell Sequencing
09:45

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

11.8K

CRIA: An Interactive Gene Selection Algorithm for Cancers Prediction Based on Copy Number Variations.

Qiang Wu1, Dongxi Li1

  • 1College of Data Science, Taiyuan University of Technology, Taiyuan, China.

Frontiers in Plant Science
|April 7, 2022
PubMed
Summary
This summary is machine-generated.

Genomic copy number variations (CNVs) are key to cancer risk. The CRIA algorithm effectively identifies cancer-related genes from CNV data, improving cancer type prediction and offering genetic insights for treatment.

Keywords:
cancers predictioncopula entropycopy number variations (CNVs)correlation-redundancy analysisgene selectioninteraction analysis

More Related Videos

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
11:02

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Published on: October 18, 2013

19.5K
Array Comparative Genomic Hybridization Array CGH for Detection of Genomic Copy Number Variants
09:16

Array Comparative Genomic Hybridization Array CGH for Detection of Genomic Copy Number Variants

Published on: February 21, 2015

19.8K

Related Experiment Videos

Last Updated: Sep 27, 2025

Detection of Copy Number Alterations Using Single Cell Sequencing
09:45

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

11.8K
Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
11:02

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Published on: October 18, 2013

19.5K
Array Comparative Genomic Hybridization Array CGH for Detection of Genomic Copy Number Variants
09:16

Array Comparative Genomic Hybridization Array CGH for Detection of Genomic Copy Number Variants

Published on: February 21, 2015

19.8K

Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Genomic copy number variations (CNVs) are significant structural gene variations linked to cancer risk.
  • Understanding CNVs is crucial for researching cancer formation and progression.

Purpose of the Study:

  • To introduce an improved computational gene selection algorithm, CRIA (correlation-redundancy and interaction analysis), for screening cancer-related genes based on CNVs.
  • To enhance cancer type prediction accuracy using selected key genetic features.

Main Methods:

  • CRIA algorithm: 1. Selects main effect features with high correlation to class labels. 2. Iteratively selects features based on correlation, redundancy, and interaction analysis.
  • Utilizes real datasets to select the top 200 genes for cancer type prediction.

Main Results:

  • CRIA effectively extracts key CNV features, outperforming existing methods in classification performance.
  • The algorithm identifies interpretable genes highly associated with cancer, providing potential genetic insights.

Conclusions:

  • The CRIA algorithm offers a robust method for gene selection in cancer research.
  • Identified genes may offer new genetic targets for cancer treatment strategies.