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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,...
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
Outliers and Influential Points01:08

Outliers and Influential Points

An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the vertical...
Fluid Mosaic Model01:19

Fluid Mosaic Model

Scientists identified the plasma membrane in the 1890s and its principal chemical components (lipids and proteins) by 1915. The model for plasma membrane structure, proposed in 1935 by Hugh Davson and James Danielli, was the first model to be widely accepted in the scientific community. The model was based on the plasma membrane's "railroad track" appearance in early electron micrographs. Davson and Danielli theorized that the plasma membrane's structure resembled a sandwich with the analogy of...
Network Function of a Circuit01:25

Network Function of a Circuit

Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.

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Related Experiment Video

Updated: Jul 16, 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

MOSAIC: MOtif Set with mAximal InfluenCe on network.

Murat Calis, Volkan Altuntas, Tamer Kahveci

    IEEE Transactions on Computational Biology and Bioinformatics
    |July 14, 2026
    PubMed
    Summary

    This study introduces a new method, MOSAIC, to analyze how groups of network motifs influence biological network function. MOSAIC efficiently identifies key gene sets, improving disease gene discovery, like for Alzheimer's disease.

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    Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
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    Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

    Published on: August 21, 2019

    Related Experiment Videos

    Last Updated: Jul 16, 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

    Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
    07:57

    Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

    Published on: August 21, 2019

    Area of Science:

    • Systems Biology
    • Bioinformatics
    • Computational Biology

    Background:

    • Biological networks exhibit complex interactions, often modeled as graphs.
    • Network motifs, recurring subgraph patterns, offer insights into network function.
    • Current research lacks understanding of how motif collections collectively impact network functionality.

    Purpose of the Study:

    • To address the gap in understanding collective motif influence on biological networks.
    • To introduce the Closest k-Motif Set Selection problem.
    • To develop an efficient algorithm for selecting influential motif collections.

    Main Methods:

    • The study defines the NP-hard Closest k-Motif Set Selection problem.
    • A novel greedy algorithm, MOSAIC (MOtif Set with mAximal InfluenCe), is developed.
    • MOSAIC employs a two-phase approach: initialization and incremental updates.

    Main Results:

    • MOSAIC demonstrates optimal or near-optimal performance in selecting motif sets.
    • The algorithm exhibits efficient scalability on large biological networks, including the human interactome.
    • MOSAIC effectively identifies Alzheimer's disease genes, including those missed by existing methods.

    Conclusions:

    • MOSAIC provides an efficient and effective solution for analyzing collective motif influence.
    • This work enhances motif-based network analysis and disease gene identification.
    • The findings open new research directions for exploring functional implications of network motifs.