fuzzyClustering – K-partite Graph Clustering algorithm that allows for Overlapping (Fuzzy) Clusters

fuzzyClustering

:: DESCRIPTION

fuzzyClustering is a fast and efficient k-partite graph clustering algorithm that allows for overlapping (fuzzy) clusters. It is based on multiplicative update rules commonly used in non-negative matrix factorization.

::DEVELOPER

Institute of Computational Biology, German Research Center for Environmental Health (GmbH)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOsX
  • MatLab

:: DOWNLOAD

 fuzzyClustering

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2010 Oct 20;11:522. doi: 10.1186/1471-2105-11-522.
Structuring heterogeneous biological information using fuzzy clustering of k-partite graphs.
Hartsperger ML, Blöchl F, Stümpflen V, Theis FJ.

iVUN 1.2 – interactive Visualization of Uncertain Biochemical Reaction Networks

iVUN 1.2

:: DESCRIPTION

iVUN is a visualization toolbox which supports uncertainty-aware analysis of static and dynamic attributes of biochemical reaction networks

::DEVELOPER

iVUN Team

:: SCREENSHOTS

iVUN

:: REQUIREMENTS

  • Windows / Linux / MacOsX
  • Java

:: DOWNLOAD

 iVUN

:: MORE INFORMATION

Citation

C. Vehlow, J. Hasenauer, A. Kramer, J. Heinrich, N. Radde, F. Allgoewer, and D. Weiskopf.
Uncertainty-aware visual analysis of biochemical reaction networks.
In Proceedings of IEEE Symposium on Biological Data Visualization(Biovis), pages 91–98, 2012.

BioAnnote 2.0.0 – Annotate Biomedical Texts by using different high-quality online Resources

BioAnnote 2.0.0

:: DESCRIPTION

BioAnnote is a desktop application is able to annotate biomedical texts by using different high-quality online resources, such as Medlineplus and Freebase.

::DEVELOPER

SING Group.

:: SCREENSHOTS

BioAnnote

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • java

:: DOWNLOAD

 BioAnnote

:: MORE INFORMATION

Citation

Comput Methods Programs Biomed. 2013 Jul;111(1):139-47. doi: 10.1016/j.cmpb.2013.03.007.
BioAnnote: a software platform for annotating biomedical documents with application in medical learning environments.
López-Fernández H, Reboiro-Jato M, Glez-Peña D, Aparicio F, Gachet D, Buenaga M, Fdez-Riverola F.

TSNI / TSNI-integral – Time Series Network Identification

TSNI / TSNI-integral

:: DESCRIPTION

TSNI assumes that the gene network can be modeled by the following system of ordinary differential equation to represent the rate of synthesis of a transcript as a function of the concentrations of every other transcript in a cell and the external perturbation.

TSNI-integral

::DEVELOPER

di Bernardo Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • MatLab

:: DOWNLOAD

  TSNI / TSNI-integral 

:: MORE INFORMATION

Citation

IET Syst Biol. 2007 Sep;1(5):306-12.
Inference of gene networks from temporal gene expression profiles.
Bansal M, di Bernardo D.

MNI – Identify the Gene Targets of a Drug Treatment based on Gene-expression data

MNI

:: DESCRIPTION

The MNI (Mode-of-action by Network Inference ) is an algorithm to identify the gene targets of a drug treatment based on gene-expression data. In a typical use of the algorithm, a single expression profile, say obtained as a result of a treatment under study, is used as the test profile while a set of hundreds of expression profiles is used as the training set. The MNI algorithm uses the large training data set of expression profiles to construct a statistical model of gene-regulatory networks in a cell or tissue. The model describes combinatorial influences of genes on one another.

::DEVELOPER

di Bernardo Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • MatLab

:: DOWNLOAD

 MNI

:: MORE INFORMATION

Citation

Nat Protoc. 2006;1(6):2551-4.
The mode-of-action by network identification (MNI) algorithm: a network biology approach for molecular target identification.
Xing H, Gardner TS.

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.

ddbRNA – RNA Secondary Structure Prediction

ddbRNA

:: DESCRIPTION

ddbRNA is a software for detection of conserved secondary structures in multiple alignments.

::DEVELOPER

di Bernardo Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • Java

:: DOWNLOAD

 ddbRNA

:: MORE INFORMATION

Citation

Bioinformatics. 2003 Sep 1;19(13):1606-11.
ddbRNA: detection of conserved secondary structures in multiple alignments.
di Bernardo D, Down T, Hubbard T.

bioSDP 0.3 – Analysis of uncertain Biochemical Networks via Semidefinite Programming

bioSDP 0.3

:: DESCRIPTION

bioSDP is a Matlab Toolbox for the analysis of uncertain biochemical networks via semidefinite programming.

::DEVELOPER

Institute of Computational Biology, German Research Center for Environmental Health (GmbH)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOsX
  • MatLab

:: DOWNLOAD

 bioSDP

:: MORE INFORMATION

Citation

  • Hasenauer, J.; Waldherr, S.; Wagner, K. & Allgöwer, F.: Parameter Identification, Experimental Design and Model Falsification for Biological Network Models Using Semidefinite Programming. IET Systems Biology 4:119-130, 2010.

D2D – Quantitative Dynamic Modeling of Biochemical processes

D2D

:: DESCRIPTION

D2D (Data 2 Dynamics) is a collection of numerical methods for quantitative dynamic modeling of biochemical processes, which provides reliable and efficient model calibration methods

::DEVELOPER

Institute of Computational Biology, German Research Center for Environmental Health (GmbH)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOsX
  • MatLab

:: DOWNLOAD

 D2D

:: MORE INFORMATION

Citation

Raue A., et al.
Lessons Learned from Quantitative Dynamical Modeling in Systems Biology.
PLOS ONE, 8(9), e74335, 2013.

BioClass – tool for Biomedical Text Classification

BioClass

:: DESCRIPTION

BioClass is a tool for biomedical text classification. Through it, a researcher can split a document set, directly related with a specific topic, into relevant or irrelevant documents. BioClass also supports several algorithms in order to increase the classification process efficiency and provides a set of powerful interfaces to analyse, filter and compare obtained results. In addition, all the operations than can be performed in BioClass are connected between them, so that the classification process is completely guided.

::DEVELOPER

SING Group.

:: SCREENSHOTS

BioClass

:: REQUIREMENTS

  • Linux / Windows
  • java

:: DOWNLOAD

 BioClass

:: MORE INFORMATION