Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Applications of Integration to Probability Density Functions

Applications of Integration to Probability Density Functions

Continuous probability distributions are used to model random variables that can take on any real value within a specified range. These variables do not take on isolated or countable values but rather exist on a continuum. For example, the height of an individual can be measured with increasing precision—such as 163.5 or 165.25 centimeters—demonstrating that height is a continuous random variable.The behavior of such variables is described using a probability density function (PDF), which...

Growth Models with Integration: Problem Solving

Growth Models with Integration: Problem Solving

In population modeling, integration provides a systematic way to determine accumulated quantities from known rates of change. One such application arises in ecology, where the total weight of a fish population in a body of water is referred to as its biomass. When the rate of growth of this biomass is known as a function of time, calculus can be used to determine the total biomass at a future date.Growth Rate and Biomass FunctionLet the growth rate of the fish population be represented by a...

Approximate Integration

Approximate Integration

In many practical and theoretical contexts, the exact value of a definite integral may be inaccessible. This limitation typically arises when the antiderivative of a function is either unknown or cannot be expressed in a closed mathematical form. Alternatively, it can occur when a function is defined not by a formula but by a finite set of empirical data points, such as those collected during experiments. In these cases, approximate integration techniques provide a valuable solution.One of the...

Protein Networks

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 Networks

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,...

Propagation of Uncertainty from Systematic Error

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...

You might also read

Related Articles

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

Sort by

Same author

Transcriptomic profiling and machine learning uncover gene signatures of psoriasis endotypes and disease severity.

Communications medicine·2026

Same author

Single-cell characterization of the gastrointestinal HIV reservoir reveals heterogeneous cellular phenotypes.

The Journal of clinical investigation·2025

Same author

Systematic and proactive evaluation of AIRE missense variant effects.

bioRxiv : the preprint server for biology·2025

Same author

The functional landscape of coding variation in the familial hypercholesterolemia gene <i>LDLR</i>.

Science (New York, N.Y.)·2025

Same author

Landscapes of missense variant impact for human superoxide dismutase 1.

American journal of human genetics·2025

Same author

Creating an atlas of variant effects to resolve variants of uncertain significance and guide cardiovascular medicine.

Nature reviews. Cardiology·2025

Search research articles

Related Experiment Video

Updated: May 23, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Bayesian integration of networks without gold standards.

Jochen Weile¹, Katherine James, Jennifer Hallinan

¹Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.

Bioinformatics (Oxford, England)

|April 12, 2012

Summary

This study introduces a new algorithm to accurately assess biological network interactions without needing reference data. This method surpasses existing approaches, enhancing the reliability of analyzing cellular processes from experimental data.

Related Experiment Videos

Last Updated: May 23, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Area of Science:

Systems Biology
Bioinformatics

Background:

Biological experiments provide insights into cellular processes but are prone to errors.
Assessing the accuracy of experimental data is crucial for understanding biological networks.
Existing methods for data validation often require high-quality 'gold standard' reference networks, which are not always available.

Purpose of the Study:

To develop a novel algorithm for calculating the probability of network interactions.
To overcome the limitation of requiring gold standard reference data for validation.
To improve the accuracy and reliability of biological network analysis.

Main Methods:

Development of a new computational algorithm for probability assessment of network interactions.
Algorithm operates independently of predefined 'gold standard' reference datasets.
Implementation as a plug-in for the Ondex data integration framework.

Main Results:

The novel algorithm demonstrates superior performance compared to existing gold standard-based methods.
The algorithm successfully computes the probability of network interactions without reference data.
Application to large-scale genetic and protein-protein interaction datasets validated the algorithm's efficacy.

Conclusions:

The new algorithm offers a robust and data-driven approach to biological network analysis.
It enhances the ability to assess the reliability of experimental findings in systems biology.
The method provides a valuable tool for researchers working with large biological datasets.