BANFF 2.0 – Gene Network Feature Selection

BANFF 2.0

:: DESCRIPTION

BANFF (Bayesian Network Feature Finder) is an R package for gene network feature selection.

::DEVELOPER

Tianwei Yu

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • R

:: DOWNLOAD

BANFF

:: MORE INFORMATION

Citation

Bayesian network feature finder (BANFF): an R package for gene network feature selection.
Lan Z, Zhao Y, Kang J, Yu T.
Bioinformatics. 2016 Dec 1;32(23):3685-3687.

gkm-SVM 2.0 – Enhanced Regulatory Sequence Prediction Using Gapped k-mer Features

gkm-SVM 2.0

:: DESCRIPTION

gkm-SVM is a new classifier and a general method for robust estimation of k-mer frequencies.

::DEVELOPER

BeerLab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux
  • C++ COmpiler

:: DOWNLOAD

 gkm-SVM

:: MORE INFORMATION

Citation

PLoS Comput Biol. 2014 Jul 17;10(7):e1003711. doi: 10.1371/journal.pcbi.1003711. eCollection 2014.
Enhanced regulatory sequence prediction using gapped k-mer features.
Ghandi M, Lee D, Mohammad-Noori M, Beer MA

mRMRe 2.0.9 – Parallelized mRMR Ensemble Feature Selection

mRMRe 2.0.9

:: DESCRIPTION

mRMRe contains a set of function to compute mutual information matrices from continuous, categorical and survival variables. It also contains function to perform feature selection with minimum Redundancy, Maximum Relevance (mRMR) and a new ensemble mRMR technique.

::DEVELOPER

Princess Margaret Bioinformatics and Computational Genomics Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • R

:: DOWNLOAD

 mRMRe

:: MORE INFORMATION

Citation:

Bioinformatics. 2013 Sep 15;29(18):2365-8. doi: 10.1093/bioinformatics/btt383. Epub 2013 Jul 3.
mRMRe: an R package for parallelized mRMR ensemble feature selection.
De Jay N, Papillon-Cavanagh S, Olsen C, El-Hachem N, Bontempi G, Haibe-Kains B.

ZMap 2.8.0 – Feature Annotation Viewer

ZMap 2.8.0

:: DESCRIPTION

ZMap is a genome browser written in C with the aim of providing fast access to high volume data. Data may be requested from a variety of disparate sources in parallel and cached locally allowing new tracks to be loaded or the view of current data adjusted without delay. Multiple views of the data may be presented and tracks configured for different levels of detail.

::DEVELOPER

the Annotools team at the Sanger Institute zmap@sanger.ac.uk

:: SCREENSHOTS

:: REQUIREMENTS

:: DOWNLOAD

 ZMap 

:: MORE INFORMATION

Fizzy v1.4 – Feature Selection for Biological Data Formats

Fizzy v1.4

:: DESCRIPTION

Fizzy is a feature subset selection tool that uses FEAST in the background to run feature selection on biological data formats.

::DEVELOPER

Drexel’s EESI Lab.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/ Linux /MacOsX
  • Python

:: DOWNLOAD

 Fizzy

:: MORE INFORMATION

Citation

Fizzy: feature subset selection for metagenomics.
Ditzler G, Morrison JC, Lan Y, Rosen GL.
BMC Bioinformatics. 2015 Nov 4;16:358. doi: 10.1186/s12859-015-0793-8.

repDNA 1.1.4 – Generate various modes of Feature Vectors for DNA Sequences

repDNA 1.1.4

:: DESCRIPTION

repDNA (Representations of DNAs) is a Python package for generating the widely used features reflecting the physicochemical properties and sequence-order effects of DNAs and nucleotides.

::DEVELOPER

Liu Lab, Harbin Institute of Technology Shenzhen Graduate School.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • Python

:: DOWNLOAD

 repDNA 

:: MORE INFORMATION

Citation

repDNA: a Python package to generate various modes of feature vectors for DNA sequences by incorporating user-defined physicochemical properties and sequence-order effects.
Liu B, Liu F, Fang L, Wang X, Chou KC.
Bioinformatics. 2014 Dec 10. pii: btu820.

ProFET – Protein Feature Engineering Toolkit

ProFET

:: DESCRIPTION

ProFET: Protein Feature Engineering Toolkit for Machine Learning

::DEVELOPER

Dan Ofer , Michal Linial

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

 ProFET

:: MORE INFORMATION

Citation

ProFET: Feature engineering captures high-level protein functions.
Ofer D, Linial M.
Bioinformatics. 2015 Jun 30. pii: btv345.

Spial – Analysis of Subtype-specific Features in multiple Sequence Alignments of Proteins

Spial

:: DESCRIPTION

Spial (Specificity in alignments) is a web-server that takes a pair of multiple sequence alignments as input and produces an output that highlights positions which are conserved in both alignments (the consensus), and positions which are specific to either alignment.

::DEVELOPER

Spial team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Bioinformatics. 2010 Nov 15;26(22):2906-7. doi: 10.1093/bioinformatics/btq552. Epub 2010 Sep 29.
Spial: analysis of subtype-specific features in multiple sequence alignments of proteins.
Wuster A1, Venkatakrishnan AJ, Schertler GF, Babu MM.

EFFECT 2013 – Automated Construction and Extraction of Features for Classification of Biological Sequences

EFFECT 2013

:: DESCRIPTION

EFFECT is an algorithmic framework for automated detection of functional signals in biological sequences.

::DEVELOPER

Computational Biology lab, George Mason University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • Java
  • BioJava

:: DOWNLOAD

 EFFECT

:: MORE INFORMATION

Citation

Uday Kamath, Kenneth A. De Jong, and Amarda Shehu.
“Effective Automated Feature Construction and Selectionfor Classification of Biological Sequences.”
PLoS One, 2014 (in press).

FEATURE 3.1 – Examine Biological Structures

FEATURE 3.1

:: DESCRIPTION

FEATURE is an automated tools that examines biological structures and produces useful representations of the key biophysical and biochemical features of these structures that are critical for understanding function. The utility of this system extends from medical/pharmaceutical applications (model-based drug design, comparing pharmacological activities) to industrial applications (understanding structural stability, protein engineering).

::DEVELOPER

FEATURE Team

:: SCREENSHOTS

 

:: REQUIREMENTS

  • Windows/ Linux / Mac OsX

:: DOWNLOAD

 FEATURE

:: MORE INFORMATION

Citation:

Halperin I, Glazer DS, Wu S, Altman RB.
The FEATURE framework for protein function annotation: modeling new functions, improving performance, and extending to novel applications.”
BMC Genomics. 9 Suppl 2 S2.