SEREND 1.1 – SEmi-supervised REgulatory Network Discoverer

SEREND 1.1

:: DESCRIPTION

SEREND is a semi-supervised learning method that uses a curated database of verified transcriptional factor-gene interactions, DNA sequence binding motifs, and a compendium of gene expression data in order to make thousands of new predictions about transcription factor-gene interactions, including whether the transcription factor activates or represses the gene.

::DEVELOPER

Jason Ernst Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux/MacOsX/ Windows
Java

:: DOWNLOAD

SEREND

:: MORE INFORMATION

Citation

Ernst J, Beg QK, Kay KA, Balazsi G, Oltvai ZN, Bar-Joseph Z.
A Semi-Supervised Method for Predicting Transcription Factor-Gene Interactions in Escherichia coli.
PLoS Computational Biology 4: e1000044, 2008.

MonoClad – Find meaningful partitions using Semi-supervised Class Discovery

MonoClad

:: DESCRIPTION

MonoClaD (Monotone Class Discovery) finds meaningful partitions using semi-supervised class discovery.

::DEVELOPER

Laboratory of Computational Biology at the Technion.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows/Linux/MacOsX
Java

:: DOWNLOAD

MonoClaD

:: MORE INFORMATION

Citation

Clinically driven semi-supervised class discovery in gene expression data.
Steinfeld I, Navon R, Ardigò D, Zavaroni I, Yakhini Z.
Bioinformatics. 2008 Aug 15;24(16):i90-7.

AMASS – MSI Analysis by Semi-supervised Segmentation

AMASS

:: DESCRIPTION

AMASS is a series of tools written in C++ that allows you to segment your mass spectrometry imaging dataset into regions with similar molecular signatures. The software allows for user input at each step of the process and returns the regions as well as the associated molecular signatures. It is composed of two dynamic libraries and a series of executables using the libraries as a base.

::DEVELOPER

UCSD Department of Computer Science and Engineering

:: SCREENSHOTS

:: REQUIREMENTS

Linux / Windows / MacOsX
Python
TCL/TK

:: DOWNLOAD

AMASS

:: MORE INFORMATION

Citation

Bruand J, Alexandrov T, Sistla S, Wisztorski M, Mériaux C, Becker M, Salzet M, Fournier I, Macagno E, Bafna V.
AMASS: Algorithm for MSI Analysis by Semi-supervised Segmentation.
J Proteome Res 10(10):4734-43