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

Cancers Originate from Somatic Mutations in a Single Cell02:21

Cancers Originate from Somatic Mutations in a Single Cell

12.5K
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...
12.5K
Cancer-Critical Genes II: Tumor Suppressor Genes01:05

Cancer-Critical Genes II: Tumor Suppressor Genes

7.7K
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...
7.7K
Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

5.9K
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,...
5.9K
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

17.9K
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%...
17.9K
Cancer Survival Analysis01:21

Cancer Survival Analysis

415
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...
415
Cancer02:18

Cancer

49.2K
Cancers arise due to mutations in genes involved in the regulation of cell division, which leads to unrestricted cell proliferation. Modern science and medicine have made great strides in the understanding and treatment of cancer, including eradicating cancer in some patients. However, there is still no cure for cancer. This is largely due to the fact that cancer is a large group of many diseases.
49.2K

You might also read

Related Articles

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

Sort by
Same author

Identification of the gene cluster for the dithiolopyrrolone antibiotic holomycin in Streptomyces clavuligerus.

Proceedings of the National Academy of Sciences of the United States of America·2010
Same author

Safety evaluation of tea (Camellia sinensis (L.) O. Kuntze) flower extract: assessment of mutagenicity, and acute and subchronic toxicity in rats.

Journal of ethnopharmacology·2010
Same author

Influences of soil properties and leaching on nickel toxicity to barley root elongation.

Ecotoxicology and environmental safety·2010
Same author

Effects of CO2 insufflation on cerebrum during endoscopic thyroidectomy in a porcine model.

Surgical endoscopy·2010
Same author

Plants' use of different nitrogen forms in response to crude oil contamination.

Environmental pollution (Barking, Essex : 1987)·2010
Same author

Overexpression of p35 in Min6 pancreatic beta cells induces a stressed neuron-like apoptosis.

Journal of the neurological sciences·2010
Same journal

A computational model of chemically- and mechanically-induced thrombus formation in cerebral aneurysms.

Computers in biology and medicine·2026
Same journal

An improved catch fish optimization based deep learning model for Parkinson disease classification using EEG signal.

Computers in biology and medicine·2026
Same journal

Assessing the robustness of evaluation metrics for synthetic ECG signal quality.

Computers in biology and medicine·2026
Same journal

Integrating stemness and epithelial-mesenchymal transition signatures with machine learning identifies RUNX1 as a therapeutic vulnerability in colorectal cancer.

Computers in biology and medicine·2026
Same journal

Differential regional textural attributes of tongue in normal and acidity patients in the light of traditional Chinese medicine.

Computers in biology and medicine·2026
Same journal

SC-MSDNet: Spatial-consistent multi-view self-distillation for retinal OCT classification.

Computers in biology and medicine·2026
See all related articles

Related Experiment Video

Updated: Aug 20, 2025

Comparative Lesions Analysis Through a Targeted Sequencing Approach
08:16

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Published on: November 5, 2019

6.8K

Cancer classification based on multiple dimensions: SNV patterns.

Bo Li1, Liang Yu1, Lin Gao1

  • 1School of Computer Science and Technology, Xidian University, Xi'an, 710071, Shaanxi, China.

Computers in Biology and Medicine
|November 17, 2022
PubMed
Summary
This summary is machine-generated.

Multidimensional single nucleotide variants (SNVs) features effectively classify cancer types with high accuracy. This approach enhances cancer diagnosis and aids in identifying potential oncogenes.

Keywords:
Cancer classificationKNNMultidimensional SNV featureOncogene

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
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

177

Related Experiment Videos

Last Updated: Aug 20, 2025

Comparative Lesions Analysis Through a Targeted Sequencing Approach
08:16

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Published on: November 5, 2019

6.8K
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
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

177

Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Cancer occurrence is linked to single nucleotide variants (SNVs), which present distinct patterns across different cancer types.
  • Traditional methods using trinucleotides for mutation signature extraction may lead to information loss and reduced model performance due to single-dimensional feature extraction.

Purpose of the Study:

  • To develop and validate a novel method for cancer classification using multidimensional SNV (M-SNV) features.
  • To improve the accuracy of cancer diagnosis and treatment strategies by leveraging comprehensive SNV pattern analysis.

Main Methods:

  • Defined multidimensional SNV (M-SNV) features incorporating first- and second-order neighboring nucleotides.
  • Utilized a dataset from The Cancer Genome Atlas (TCGA) with 2761 samples across 12 cancer types.
  • Employed k-nearest neighbors (KNN) classification with leave-one-out cross-validation.

Main Results:

  • Extracted M-SNV features showed distinct distributions correlating with cancer types.
  • Preprocessing raw data enhanced cancer subtype focus at the SNV level.
  • Achieved a stable classification accuracy of approximately 97%, reaching up to 97.43%.

Conclusions:

  • The defined M-SNV features are feasible for accurate cancer classification.
  • This methodology holds potential for the discovery of novel oncogenes.