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

Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
Hypothesis Test for Test of Independence01:16

Hypothesis Test for Test of Independence

The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
H0: The two variables (factors)...
Mutual Inductance01:24

Mutual Inductance

Inductance is the property of a device that tells us how effectively it induces an emf in another device. In other words, it is a physical quantity that expresses the effectiveness of a given device.
When two circuits carrying time-varying currents are close to one another, the magnetic flux through each circuit varies because of the changing current in the other circuit. Consequently, an emf is induced in each circuit by the changing current in the other. Therefore, this type of emf is called...
Associative Learning01:27

Associative Learning

Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...

You might also read

Related Articles

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

Sort by
Same author

DAG-VAERL: a novel causal inference method for building causal gene regulatory networks.

BioData mining·2026
Same author

Transcriptome graph transformer: a graph transformer-based unsupervised model for transcriptome data analysis.

BMC bioinformatics·2026
Same author

Vitamin C-deficient gulo<sup>-/-</sup> mice exhibit increased susceptibility to Helicobacter pylori colonization and gastric pathology.

Microbial pathogenesis·2026
Same author

Design and thermo-structural analysis of multiple composite layers with ablative materials for passive thermal protection systems.

Scientific reports·2026
Same author

Segment Any Cell: A SAM-Based Auto-Prompting Fine-Tuning Framework for Nuclei Segmentation.

IEEE transactions on neural networks and learning systems·2025
Same author

CREB-Family Transcription Factors and Vasopressin-Mediated Regulation of Aqp2 Gene Transcription.

Journal of the American Society of Nephrology : JASN·2025
Same journal

Covariance decomposition for distance based species tree estimation.

BMC bioinformatics·2026
Same journal

SNPio: a Python interface for population genomic data processing.

BMC bioinformatics·2026
Same journal

SpaHNR: a spatial domain identification method via sparse attention-based hierarchical node representation and multi-view contrastive learning.

BMC bioinformatics·2026
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
See all related articles

Related Experiment Video

Updated: Jun 13, 2026

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

Learning biological network using mutual information and conditional independence.

Dong-Chul Kim1, Xiaoyu Wang, Chin-Rang Yang

  • 1Department of Computer Science and Engineering, The University of Texas at Arlington, 76019, USA.

BMC Bioinformatics
|May 5, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Bayesian network algorithm for analyzing biological signaling pathways using reverse-phase protein microarray (RPPM) data. The method accurately predicts biological networks, offering insights into ataxia telangiectasis mutation (ATM) signaling.

More Related Videos

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

Related Experiment Videos

Last Updated: Jun 13, 2026

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

Area of Science:

  • Systems Biology
  • Computational Biology
  • Proteomics

Background:

  • Biological networks provide a holistic approach to understanding complex cellular interactions.
  • Reverse-phase protein microarray (RPPM) enables quantitative measurement of proteomic responses.
  • Investigating signaling pathways is crucial for understanding cellular functions and diseases.

Purpose of the Study:

  • To develop a novel structure learning algorithm for Bayesian networks to identify signaling pathways responsive to RPPM data.
  • To apply this computational framework to predict biological networks, specifically focusing on ataxia telangiectasis mutation (ATM).
  • To validate the algorithm's performance by comparing predicted networks with existing protein-protein interaction databases.

Main Methods:

  • Development of a new Bayesian network structure learning algorithm based on mutual information, conditional independence, and graph theory.
  • Application of the algorithm to analyze RPPM data from ATM cell lines under varying radiation doses and time points.
  • Validation through comparison experiments with established biological networks and protein-protein interaction (PPI) databases.

Main Results:

  • The proposed algorithm reliably predicts biological networks with a reduced number of incorrect connections, particularly for mid-size networks.
  • Distinct signaling networks for ATM were identified under different radiation dosages.
  • Predicted networks showed notable agreement with results from multiple PPI databases.

Conclusions:

  • A novel computational framework combined with RPPM technology effectively studies biological networks.
  • The methodology is demonstrated for analyzing ATM cell line networks under low-dose ionizing radiation.
  • This approach enhances the understanding of cellular responses and signaling pathway dynamics.