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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,...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Poisson Probability Distribution01:09

Poisson Probability Distribution

A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
Poisson's And Laplace's Equation01:25

Poisson's And Laplace's Equation

The electric potential of the system can be calculated by relating it to the electric charge densities that give rise to the electric potential. The differential form of Gauss's law expresses the electric field's divergence in terms of the electric charge density.
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...

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Related Experiment Video

Updated: May 8, 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

A Local Poisson Graphical Model for inferring networks from sequencing data.

Genevera I Allen1, Zhandong Liu

  • 1Department of Statistics & Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA.

IEEE Transactions on Nanobioscience
|August 20, 2013
PubMed
Summary

We introduce a new method, the Local Poisson Graphical Model, to infer gene networks from sequencing data. This approach effectively analyzes discrete count data, outperforming traditional Gaussian graphical models for next-generation sequencing applications.

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Last Updated: May 8, 2026

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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:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gaussian graphical models are standard for gene network inference from microarray data.
  • High-throughput sequencing technologies (e.g., RNA-Seq) generate discrete count data, making Gaussian models suboptimal.
  • A need exists for methods tailored to discrete sequencing count data for accurate gene network reconstruction.

Purpose of the Study:

  • To propose a novel method, the Local Poisson Graphical Model (LPGM), for inferring gene networks from RNA-sequencing data.
  • To address the limitations of Gaussian graphical models when applied to discrete count data.
  • To develop a fast and parallelizable algorithm for network estimation from next-generation sequencing (NGS) data.

Main Methods:

  • The Local Poisson Graphical Model assumes a Local Markov property, where variables are Poisson distributed conditional on others.
  • A neighborhood selection algorithm is employed, utilizing l1 penalized Poisson (log-linear) regressions for local model fitting.
  • The method is designed for efficient computation, enabling parallel processing for large-scale NGS datasets.

Main Results:

  • Simulations demonstrate the effectiveness of the L PGM in accurately recovering network structures from count data.
  • A case study on breast cancer microRNAs (miRNAs) successfully identified known gene regulators.
  • The analysis revealed novel miRNA clusters and hubs, highlighting potential new research directions in cancer genomics.

Conclusions:

  • The Local Poisson Graphical Model provides a robust framework for gene network inference from discrete sequencing data.
  • This novel approach offers a significant improvement over traditional methods for analyzing RNA-Seq and other count-based expression data.
  • The findings suggest new avenues for research into miRNA-gene interactions in breast cancer and other diseases.