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

Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

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,...
Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
Cancer Survival Analysis01:21

Cancer Survival Analysis

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...

You might also read

Related Articles

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

Sort by
Same author

The tree labeling polytope: A unified approach to ancestral reconstruction problems.

Cell systems·2026
Same author

General-purpose topology-aware embedding of tumor phylogenetic trees with graph neural networks.

Bioinformatics advances·2026
Same author

Spatial Mapping of the Precancer-to-Cancer Transition in Breast and Prostate.

Cancer discovery·2026
Same author

LAML-Pro: Joint Maximum Likelihood Inference of Cell Genotypes and Cell Lineage Trees.

bioRxiv : the preprint server for biology·2026
Same author

Multimodal spatial alignment and morphology mapping with MOSAICField.

bioRxiv : the preprint server for biology·2026
Same author

Genomic evolution of pancreatic cancer at single-cell resolution.

Nature genetics·2026
Same journal

Haplotype-aware long-read error correction.

Algorithms for molecular biology : AMB·2026
Same journal

Extension of partial atom-to-atom maps: uniqueness and algorithms.

Algorithms for molecular biology : AMB·2026
Same journal

Lossless pangenome indexing using tag arrays.

Algorithms for molecular biology : AMB·2026
Same journal

Dolphyin: a combinatorial algorithm for identifying 1-Dollo phylogenies in cancer.

Algorithms for molecular biology : AMB·2026
Same journal

Probing transcription factor subsets in gene regulatory networks.

Algorithms for molecular biology : AMB·2026
Same journal

Comparing the ability of embedding methods on metabolic hypergraphs for capturing taxonomy-based features.

Algorithms for molecular biology : AMB·2026
See all related articles

Related Experiment Video

Updated: May 18, 2026

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres
06:52

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

Published on: July 22, 2020

Finding driver pathways in cancer: models and algorithms.

Fabio Vandin1, Eli Upfal, Benjamin J Raphael

  • 1Department of Computer Science, and Center for Computational Molecular Biology Brown University, 115 Waterman St,, 4th Flr, Providence, RI 02912, USA. vandinfa@cs.brown.edu.

Algorithms for Molecular Biology : AMB
|September 8, 2012
PubMed
Summary
This summary is machine-generated.

Identifying cancer driver pathways is crucial for understanding tumor development. This study presents a new method to discover driver pathways directly from mutation data, outperforming traditional single-gene tests by not requiring background mutation rate estimation.

More Related Videos

Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts
10:27

Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts

Published on: July 25, 2020

Related Experiment Videos

Last Updated: May 18, 2026

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres
06:52

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

Published on: July 22, 2020

Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts
10:27

Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts

Published on: July 25, 2020

Area of Science:

  • Genomics
  • Computational Biology
  • Cancer Research

Background:

  • Cancer sequencing projects generate vast amounts of somatic mutation data.
  • Distinguishing driver mutations from passenger mutations is a key challenge in cancer genomics.
  • Current single-gene tests for driver mutations are limited by background mutation rate estimation and mutational heterogeneity.

Purpose of the Study:

  • To develop methods for discovering driver pathways directly from cancer mutation data.
  • To address the limitations of single-gene tests in identifying driver mutations.
  • To investigate algorithmic approaches for driver pathway discovery without prior pathway knowledge.

Main Methods:

  • Introduced two generative models for somatic mutations in cancer.
  • Studied the algorithmic complexity of discovering driver pathways within these models.
  • Developed an algorithmic approach to maximize a measure of driver pathway mutational properties.

Main Results:

  • Single-gene tests for driver genes are highly sensitive to background mutation rate (BMR) estimates.
  • The proposed algorithmic approach successfully discovers driver pathways without BMR estimation.
  • This method is effective even when passenger and driver mutation frequencies are indistinguishable, a scenario where single-gene tests fail.

Conclusions:

  • Accurate estimation of the background mutation rate (BMR) is challenging.
  • Methods that bypass BMR estimation, like the one presented, enhance the power of driver gene discovery.
  • This work provides a more robust approach to identifying functionally significant mutation groups in cancer genomes.