Tag: Network Inference

NIR – Network Inference by Reverse-engineering

NIR

:: DESCRIPTION

In order to estimate the coefficient of the gene interactions NIR solves a linear regression problem for each gene considering a fixed number of k regressors. The regressor set is chosen according the residual sum of square error (RSS) minimization criterion. In this version, NIR exhaustively searches the best regressors in the space of all the possible k-tuples of genes.

::DEVELOPER

di Bernardo Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • MatLab / OCTAVE

:: DOWNLOAD

 NIR

:: MORE INFORMATION

Citation

Pac Symp Biocomput. 2004:486-97.
Robust identification of large genetic networks.
Di Bernardo D, Gardner TS, Collins JJ.

Cyni 1.0.0.beta6 – Cytoscape Network Inference Toolbox

Cyni 1.0.0.beta6

:: DESCRIPTION

Cyni (Cytoscape Network Inference Toolbox) is a new Cytoscape App that puts together several tools that allow infering networks from biological data. Each of the tools can be used independently or together to perform several tasks.

::DEVELOPER

Systems Biology Lab, Institut Pasteur, Paris

:: SCREENSHOTS

Cyni

:: REQUIREMENTS

  • Linux/ Windows/ MacOsX
  • Java
  • Cytoscape

:: DOWNLOAD

 Cyni

:: MORE INFORMATION

Citation

The Cyni framework for network inference in Cytoscape.
Guitart-Pla O, Kustagi M, Rügheimer F, Califano A, Schwikowski B.
Bioinformatics. 2014 Dec 18. pii: btu812.

Sets2Networks – Network Inference from Repeated Observations of Sets

Sets2Networks

:: DESCRIPTION

Sets2Networks (S2N) is general method for network inference from repeated observations of sets of related entities. Given experimental observations of sets of related entities, S2N infers the underlying network of binary interactions between these entities by generating an ensemble of networks consistent with the data; the frequency of occurrence of a given interaction throughout this ensemble is interpreted as the probability that the interaction is present in the underlying real network.

::DEVELOPER

Ma’ayan Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

BMC Syst Biol. 2012 Jul 23;6:89. doi: 10.1186/1752-0509-6-89.
Sets2Networks: network inference from repeated observations of sets.
Clark NR, Dannenfelser R, Tan CM, Komosinski ME, Ma’ayan A.

GeneNetWeaver 3.1.3 Beta – in silico benchmark Generation and performance profiling of Network Inference Methods

GeneNetWeaver 3.1.3 Beta

:: DESCRIPTION

GeneNetWeaver (GNW) is an open-source tool for in silico benchmark generation and performance profiling of network inference methods.

::DEVELOPER

GeneNetWeaver team

:: SCREENSHOTS

GeneNetWeaver

:: REQUIREMENTS

  • Linux/ Windows/ MacOsX
  • Java

:: DOWNLOAD

 GeneNetWeaver

:: MORE INFORMATION

Citation:

Schaffter, T. et al. (2011).
GeneNetWeaver: In silico benchmark generation and performance profiling of network inference methods.
Bioinformatics, 27(16):2263-70

GEMINI – Gene Expression and Metabolism Integrated for Network Inference

GEMINI

:: DESCRIPTION

GEMINI produces a regulatory network that is simultaneously consistent with observed gene knockout phenotypes, gene expression data, and the corresponding metabolic network.

::DEVELOPER

The Hood-Price Lab for Systems Biomedicine

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ WIndows/ MacOsX
  • Matlab

:: DOWNLOAD

GEMINI

:: MORE INFORMATION

ebdbNet 1.2.3 – Empirical Bayes Dynamic Bayesian Network Inference

ebdbNet 1.2.3

:: DESCRIPTION

ebdbNet is an R package for reverse-engineering directed gene regulatory networks from time-course gene expression data using Dynamic Bayesian networks (DBN) and empirical Bayes methodology.

::DEVELOPER

Andrea Rau

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ MacOsX/ Windows
  • R package

:: DOWNLOAD

 ebdbNet

:: MORE INFORMATION

Citation

Andrea Rau, Florence Jaffrézic, Jean-Louis Foulley, and R. W. Doerge.
An empirical Bayesian method for estimating biological networks from temporal microarray data.
Statistical Applications in Genetics and Molecular Biology 9: 1-28.

NETI 1.2 – Network Inference

NETI 1.2

:: DESCRIPTION

NETI (Network Inference) implements the generalized additive regulation model with adjustable kernel functions for network inference from time series data.

::DEVELOPER

Eugene Novikov and Emmanuel Barillot @ Bioinformatics Laboratory of Institut Curie (Paris).

:: SCREENSHOTS

:: REQUIREMENTS

:: DOWNLOAD

  NETI

:: MORE INFORMATION

Citation:

Eugene Novikov and Emmanuel Barillot
Regulatory network reconstruction using an integral additive model with flexible kernel functions
BMC Systems Biology 2008, 2:8

N-Sieve 0.1 – Network Inference tool that uses an attenuation based structural prior

N-Sieve 0.1

:: DESCRIPTION

N-Sieve is a network inference tool that uses an attenuation based structural prior. N-Sieve infers transcriptional networks from gene expression data comprised of both steady state and time course measurements.

::DEVELOPER

The Brent Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 N-Sieve

:: MORE INFORMATION