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

Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
Biostatistics: Overview01:20

Biostatistics: Overview

Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...

You might also read

Related Articles

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

Sort by
Same author

SNP-based prediction of schizophrenia using machine learning.

Bioinformatics advances·2026
Same author

SOLVE: A structured orthogonal latent variable framework for disentangling confounding in matrix data.

Biology methods & protocols·2026
Same author

Predicting Intensive Care Unit Admission in COVID-19-Infected Pregnant Women Using Machine Learning.

Journal of clinical medicine·2025
Same author

From theory to practice: Harmonizing taxonomies of trustworthy AI.

Health policy OPEN·2024
Same author

Development and Validation of a Machine Learning COVID-19 Veteran (COVet) Deterioration Risk Score.

Critical care explorations·2024
Same author

Tryptophan Metabolism in Alzheimer's Disease with the Involvement of Microglia and Astrocyte Crosstalk and Gut-Brain Axis.

Aging and disease·2024

Related Experiment Video

Updated: May 20, 2026

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

An automated bayesian framework for integrative gene expression analysis and predictive medicine.

Neena Parikh1, Amin Zollanvari, Gil Alterovitz

  • 1Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA;

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science
|July 11, 2012
PubMed
Summary

Researchers developed a new Bayesian Network framework for predictive medicine using gene expression data. This automated pipeline accurately predicts disease outcomes, applicable to various complex disorders.

Keywords:
Bayesian NetworkGene Expression OmnibusIntegrative GenomicsMulti-network Model

More Related Videos

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
03:08

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

Related Experiment Videos

Last Updated: May 20, 2026

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
03:08

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

Area of Science:

  • Computational biology
  • Genomics
  • Biostatistics

Background:

  • Predictive medicine requires robust analytical frameworks for complex diseases.
  • Gene expression data offers insights into disease mechanisms but requires integrative analysis.
  • Publicly available datasets enable large-scale computational studies.

Purpose of the Study:

  • To construct a closed-loop Bayesian Network framework for predictive medicine.
  • To integrate diverse gene expression datasets for disease analysis.
  • To develop an automated pipeline for predictive modeling.

Main Methods:

  • Utilized Bayesian statistical methods for model construction.
  • Developed an automated pipeline for integrative analysis of gene expression data.
  • Applied the framework to GEO datasets from four distinct diseases.

Main Results:

  • Successfully constructed an automated pipeline for integrative analysis.
  • Developed Bayesian Network models with high accuracy and predictive ability.
  • Demonstrated the framework's applicability to multiple complex diseases.

Conclusions:

  • The developed Bayesian Network framework provides a powerful tool for predictive medicine.
  • Integrative analysis of gene expression data can yield accurate disease predictions.
  • The automated pipeline is adaptable to various complex disorders and genomic studies.