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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,...
Types of Hypothesis Testing01:11

Types of Hypothesis Testing

There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p ≠ 0.5.
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
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)...
Hypothesis: Accept or Fail to Reject?01:17

Hypothesis: Accept or Fail to Reject?

The outcome of any hypothesis testing leads to rejecting or not rejecting the null hypothesis. This decision is taken based on the analysis of the data, an appropriate test statistic, an appropriate confidence level, the critical values, and P-values. However, when the evidence suggests that the null hypothesis cannot be rejected, is it right to say, 'Accept' the null hypothesis?
There are two ways to indicate that the null hypothesis is not rejected. 'Accept' the null hypothesis and 'fail to...

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Updated: May 13, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

An inferential framework for biological network hypothesis tests.

Phillip D Yates1, Nitai D Mukhopadhyay

  • 1Pfizer Global Research and Development, Groton, CT, USA. pyatesgene@gmail.com

BMC Bioinformatics
|March 19, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a novel statistical method for testing biological networks, enabling comparisons between different conditions. The approach enhances understanding of pathway changes in diseases like diabetes and cancer.

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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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Last Updated: May 13, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Published on: December 7, 2021

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Area of Science:

  • Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Biological networks are fundamental to cell biology and physiology.
  • Existing methods for inferring biological networks lack formal hypothesis testing procedures.
  • Comparing network behavior across conditions (e.g., disease vs. healthy) requires robust statistical tools.

Purpose of the Study:

  • To develop a formal inferential method for hypothesis testing on biological networks.
  • To enable one-sample and two-sample network comparisons.
  • To identify pathways that differentiate between phenotypes.

Main Methods:

  • Proposed a dissimilarity measure incorporating neighbor information for network comparison.
  • Developed inferential methods for one- and two-sample hypothesis tests using networks as sampling units.
  • Utilized random graphs and weighted correlation networks for model construction.

Main Results:

  • Demonstrated the utility of the approach with simulated and real microarray data.
  • Applied the method to diabetes and ovarian cancer datasets.
  • Successfully elucidated co-regulation changes in pathways between clinical phenotypes.

Conclusions:

  • Formal hypothesis tests for biological networks represent a significant advancement over gene-based tests.
  • Dissimilarity-based testing methods improve the understanding of complex regulatory systems in systems biology.
  • The presented methods offer benefits for analyzing biological networks under select scenarios.