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

Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

1.4K
Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...
1.4K
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

52.8K
In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
52.8K
Genetic Drift03:33

Genetic Drift

35.1K
Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.
35.1K
Genetics of Speciation02:16

Genetics of Speciation

18.9K
Speciation is the evolutionary process resulting in the formation of new, distinct species—groups of reproductively isolated populations.
18.9K
Genetic Variation01:25

Genetic Variation

1.6K
Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
Genes exist in different versions called alleles,...
1.6K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

327
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
327

You might also read

Related Articles

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

Sort by
Same author

Addressing bioenergetic deficits and restoring mitochondrial health in transplanted islets using mesenchymal stem cells.

Stem cells translational medicine·2026
Same author

A Bayesian Time-Varying Psychophysiological Interaction Model.

Data science in science·2026
Same author

Emotional words evoke region- and valence-specific patterns of concurrent neuromodulator release in human thalamus and cortex.

Cell reports·2026
Same author

A HORSESHOE MIXTURE MODEL FOR BAYESIAN SCREENING WITH AN APPLICATION TO LIGHT SHEET FLUORESCENCE MICROSCOPY IN BRAIN IMAGING.

The annals of applied statistics·2026
Same author

A transcriptional program associated with neurotransmission in the living human brain.

Molecular psychiatry·2026
Same author

Electrodiffusion in cardiac intercalated disc nanostructures alters cell-cell action potential transmission via ephaptic coupling: A model study.

The Journal of physiology·2025
Same journal

Widening Health Inequality and Causal Metabolic Drivers in Global Colorectal Cancer: A Multi-Dimensional Study.

Cancer informatics·2026
Same journal

GFAP-Dependent Transcriptional Dynamics and Cellular Heterogeneity in Primary, Recurrent, and Grade III Gliomas.

Cancer informatics·2026
Same journal

Translating Data Into Clinical Tools: An Integrative Strategy for Precision Biomarker Identification in Soft Tissue Sarcoma Diagnosis and Prognosis.

Cancer informatics·2026
Same journal

The MAPK Pathway Coordinates an Immunosuppressive Microenvironment in Colorectal Cancer: A Single-Cell Guided Prognostic Model.

Cancer informatics·2026
Same journal

Multi-Scale Cross-Attention Multiple Instance Learning Network for Automated Classification of Colorectal Polyps.

Cancer informatics·2026
Same journal

LEPR Contributes to Lung Squamous Cell Carcinoma: Insights From Mendelian Randomization and Experimental Studies.

Cancer informatics·2026
See all related articles

Related Experiment Video

Updated: Apr 22, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

10.6K

A bayesian integrative model for genetical genomics with spatially informed variable selection.

Alberto Cassese1, Michele Guindani2, Marina Vannucci3

  • 1Department of Statistics, Rice University, Houston, TX, USA. ; Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Cancer Informatics
|October 8, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian model integrating gene expression and CGH array data. The model identifies potential cancer biomarkers by linking copy number variants to gene activity, outperforming existing methods.

Keywords:
Bayesian hierarchical modelscopy number variantsgene expressionmeasurement errorvariable selection

More Related Videos

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.1K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.2K

Related Experiment Videos

Last Updated: Apr 22, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

10.6K
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.1K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.2K

Area of Science:

  • Genomics
  • Biostatistics
  • Cancer Research

Background:

  • Integrating gene expression and comparative genomic hybridization (CGH) array data is crucial for understanding cancer.
  • Existing methods for data integration often lack robust statistical frameworks for variable selection and dependency modeling.

Purpose of the Study:

  • To develop a novel Bayesian hierarchical model for joint analysis of gene expression and CGH data.
  • To identify associations between copy number variants and gene expression in cancer.

Main Methods:

  • A Bayesian hierarchical model incorporating a measurement error model and a hidden Markov model.
  • Utilized a spatial prior with a probit link for variable selection across adjacent DNA segments.
  • Employed Markov chain Monte Carlo (MCMC) stochastic search for posterior inference.

Main Results:

  • The proposed model demonstrated superior performance in simulations compared to alternative priors.
  • Application to lung squamous cell carcinoma data identified candidate associations between copy number variants and gene activity.
  • Gene Ontology (GO) analysis revealed significant enrichments in cancer-related genes.

Conclusions:

  • The developed Bayesian model effectively integrates gene expression and CGH data for cancer research.
  • The model successfully identified potential candidate biomarkers and gene associations relevant to cancer.
  • Findings provide a foundation for further experimental validation of identified biomarkers and gene targets.