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

Cancer-Critical Genes II: Tumor Suppressor Genes01:05

Cancer-Critical Genes II: Tumor Suppressor Genes

9.8K
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...
9.8K
Velocity and Position by Integral Method01:13

Velocity and Position by Integral Method

8.1K
If acceleration as a function of time is known, then velocity and position functions can be derived using integral calculus. For constant acceleration, the integral equations refer to the first and second kinematic equations for velocity and position functions, respectively.
Consider an example to calculate the velocity and position from the acceleration function. A motorboat is traveling at a constant velocity of 5.0 m/s when it starts to decelerate to arrive at the dock. Its acceleration is...
8.1K
Effects of EDTA on End-Point Detection Methods01:18

Effects of EDTA on End-Point Detection Methods

666
Different methods, such as visual observance of metal-ion indicators, spectroscopic techniques, and potentiometric methods, can determine the endpoint of an EDTA titration.
In the visual method, metal-ion indicators (metallochromic dyes), which have distinct colors in their free and complex forms, are added to the mixture to signal the titration's end point. They form stable complexes with metal ions, but these complexes are weaker than the corresponding metal–EDTA complexes. As a...
666
Cancer-Critical Genes I: Proto-oncogenes01:33

Cancer-Critical Genes I: Proto-oncogenes

11.5K
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...
11.5K
Gene Therapy00:59

Gene Therapy

27.6K
Gene therapy is a technique where a gene is inserted into a person’s cells to prevent or treat a serious disease. The added gene may be a healthy version of the gene that is mutated in the patient, or it could be a different gene that inactivates or compensates for the patient’s disease-causing gene. For example, in patients with severe combined immunodeficiency (SCID) due to a mutation in the gene for the enzyme adenosine deaminase, a functioning version of the gene can be...
27.6K
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

980
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
980

You might also read

Related Articles

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

Sort by
Same author

Dimension-controlled formation of crease patterns on soft solids.

Soft matter·2016
Same author

Modeling Day-to-day Flow Dynamics on Degradable Transport Network.

PloS one·2016
Same author

Tetramethylpyrazine Protects Against Glucocorticoid-Induced Apoptosis by Promoting Autophagy in Mesenchymal Stem Cells and Improves Bone Mass in Glucocorticoid-Induced Osteoporosis Rats.

Stem cells and development·2016
Same author

Corrigendum: Lithium-ion-based solid electrolyte tuning of the carrier density in graphene.

Scientific reports·2016
Same author

PTEN/PI3K/AKT protein expression is related to clinicopathological features and prognosis in breast cancer with axillary lymph node metastases.

Human pathology·2016
Same author

Comparing the Diagnostic Accuracy of RTE and SWE in Differentiating Malignant Thyroid Nodules from Benign Ones: a Meta-Analysis.

Cellular physiology and biochemistry : international journal of experimental cellular physiology, biochemistry, and pharmacology·2016

Related Experiment Video

Updated: Feb 4, 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

7.0K

MaxMIF: A New Method for Identifying Cancer Driver Genes through Effective Data Integration.

Yingnan Hou1,2, Bo Gao1,2, Guojun Li1,2,3

  • 1School of Mathematics Shandong University Jinan 250100 P. R. China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|September 26, 2018
PubMed
Summary

Distinguishing cancer driver genes from passenger mutations is difficult. A new method, maximal mutational impact function (MaxMIF), effectively integrates mutation and interaction data, significantly outperforming existing tools in identifying crucial cancer genes.

Keywords:
cancer driver gene predictioncandidate gene prioritizationfunctional networksmaximal mutational impact functionsomatic mutations

More Related Videos

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
07:40

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

4.6K
MEDUSA for Identifying Death Regulatory Genes in Chemo-genetic Profiling Data
07:17

MEDUSA for Identifying Death Regulatory Genes in Chemo-genetic Profiling Data

Published on: February 7, 2025

927

Related Experiment Videos

Last Updated: Feb 4, 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

7.0K
A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
07:40

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

4.6K
MEDUSA for Identifying Death Regulatory Genes in Chemo-genetic Profiling Data
07:17

MEDUSA for Identifying Death Regulatory Genes in Chemo-genetic Profiling Data

Published on: February 7, 2025

927

Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Identifying cancer driver mutations is crucial for understanding tumorigenesis.
  • Distinguishing driver mutations from passenger mutations is a significant challenge in cancer genomics.

Purpose of the Study:

  • To present a novel method, maximal mutational impact function (MaxMIF), for differentiating cancer driver genes from passenger genes.
  • To evaluate the performance of MaxMIF against existing state-of-the-art methods.

Main Methods:

  • Integration of somatic mutation data and molecular interaction data.
  • Development and application of the maximal mutational impact function (MaxMIF).
  • Evaluation on six Pan-Cancer and 19 TCGA cancer type datasets.

Main Results:

  • MaxMIF significantly outperforms existing methods in predictive accuracy, sensitivity, and specificity.
  • MaxMIF recovers approximately 30% more known cancer genes within the top 500 candidates.
  • The method demonstrates high robustness to data perturbations and identifies potential driver genes with experimental support.

Conclusions:

  • MaxMIF is a highly effective tool for identifying and prioritizing cancer driver genes.
  • The method offers significant improvements over current state-of-the-art approaches.
  • MaxMIF is valuable for analyzing large-scale cancer genomic datasets.