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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,...
Assembly of Signaling Complexes01:30

Assembly of Signaling Complexes

Multiprotein signaling complexes are formed in a dynamic process involving protein-protein interactions at the cytoplasmic domain of transmembrane receptors or enzymatic and non-enzymatic proteins associated with the receptor. These complexes ensure the activation and propagation of intracellular signals that regulate cell functions.
Interaction domains in cell signaling
Interaction domains recognize exposed features of their binding partners containing post-translationally modified sequences,...

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Related Experiment Video

Updated: May 26, 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

Sign: large-scale gene network estimation environment for high performance computing.

Yoshinori Tamada1, Teppei Shimamura, Rui Yamaguchi

  • 1Human Genome Center, Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo, Japan. tamada@ims.u-tokyo.ac.jp

Genome Informatics. International Conference on Genome Informatics
|January 11, 2012
PubMed
Summary
This summary is machine-generated.

New software, SiGN, estimates large-scale gene networks using high-performance computing. It supports multiple models and is optimized for the K computer, enabling efficient systems biology research.

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Last Updated: May 26, 2026

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A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

Published on: May 22, 2018

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Systems Biology

Background:

  • Gene expression data analysis is crucial for understanding biological systems.
  • Estimating large-scale gene networks requires significant computational power.
  • Existing methods may not fully leverage high-performance computing resources.

Purpose of the Study:

  • To develop and introduce SiGN, a software suite for estimating large-scale gene networks.
  • To optimize gene network estimation for high-performance computing environments like the K computer.
  • To provide researchers with advanced tools for systems biology analysis.

Main Methods:

  • SiGN software suite includes SiGN-BN, SiGN-SSM, and SiGN-L1.
  • Employs five distinct models: Bayesian networks (static/dynamic, non-parametric), state space models, graphical Gaussian models, and vector autoregressive models.
  • Designed to utilize petaflops-scale computational capabilities for large network inference.

Main Results:

  • SiGN is tailored for supercomputers such as the K computer and HGC system.
  • The software facilitates the estimation of complex gene networks from gene expression data.
  • Provides a scalable solution for computationally intensive network inference tasks.

Conclusions:

  • SiGN offers a powerful and efficient approach to large-scale gene network estimation.
  • The software enhances systems biology research by enabling analysis on high-performance computing platforms.
  • Accessibility for K computer and HGC users promotes broader adoption and research advancement.