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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,...
Genetic Screens02:46

Genetic Screens

Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...
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...
Combinatorial Gene Control02:33

Combinatorial Gene Control

Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
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: Jun 6, 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

Genetic networks and soft computing.

Sushmita Mitra1, Ranajit Das, Yoichi Hayashi

  • 1Machine Intelligence Unit, Indian Statistical Institute, Kolkata 700108, India. sushmita@isical.ac.in

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|November 13, 2010
PubMed
Summary
This summary is machine-generated.

This study explores soft computing methods for analyzing gene regulatory networks from gene expression data. These approaches help overcome data noise and ambiguity to reveal cellular processes and identify drug targets.

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Last Updated: Jun 6, 2026

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

  • Systems Biology
  • Bioinformatics
  • Computational Biology

Background:

  • Gene regulatory networks (GRNs) are crucial for understanding fundamental cellular processes like growth, development, and communication.
  • Extracting GRNs from gene expression data is challenging due to noise and ambiguity inherent in biological data.
  • Reverse engineering GRNs provides insights into cellular activity, aiding in drug target identification and prediction of drug side effects.

Purpose of the Study:

  • To survey the application of soft computing methodologies in the generation of gene regulatory networks.
  • To highlight the role of soft computing tools in addressing uncertainties in biological data analysis.
  • To review hybrid approaches combining soft computing techniques for enhanced GRN inference.

Main Methods:

  • Utilizing soft computing tools such as fuzzy sets, evolutionary strategies, and neurocomputing.
  • Applying classification, clustering, and feature selection techniques for knowledge mining from gene expression profiles.
  • Investigating hybridizations of soft computing methodologies for robust GRN construction.

Main Results:

  • Soft computing tools effectively handle noise and ambiguity in biological data.
  • These methodologies facilitate the extraction of meaningful gene interactions for network generation.
  • Hybrid approaches show promise in improving the accuracy and reliability of inferred GRNs.

Conclusions:

  • Soft computing offers powerful solutions for the challenging problem of gene regulatory network inference.
  • These computational approaches are vital for advancing systems biology and drug discovery.
  • Further research into hybrid soft computing methods can enhance our understanding of complex genetic interactions.