GANN 2.0 – Machine Learning tool for the Detection of Conserved Features in DNA

GANN 2.0

:: DESCRIPTION

GANN (Genetic Algorithm Neural Networks) is a machine learning method designed with the complexities of transcriptional regulation in mind.The key principle is that regulatory regions are composed of features such as consensus strings, characterized binding sites, and DNA structural properties. GANN identifies these features in a set of sequences, and then identifies combinations of features that can differentiate between the positive set (sequences with known or putative regulatory function) and the negative set (sequences with no regulatory function). Once these features have been identified, they can be used to classify new sequences of unknown function.

::DEVELOPER

Beiko lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/Windows
  • Perl

:: DOWNLOAD

 GANN

:: MORE INFORMATION

Citation

Beiko, R.G. and Charlebois, R.L. (2005).
GANN: genetic algorithm neural networks for the detection of conserved combinations of features in DNA.
BMC Bioinformatics 6: 36.

PrePPItar 0.0.1 – Machine Learning Framework to Predict PPI Target for Drug

PrePPItar 0.0.1

:: DESCRIPTION

PrePPItar is a computational method to Predict PPIs as drug targets by uncovering the potential associations between drugs and PPIs.

::DEVELOPER

ZHANGroup

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows
  • MatLab

:: DOWNLOAD

 PrePPItar

:: MORE INFORMATION

Citation:

Computational probing protein-protein interactions targeting small molecules.
Wang YC, Chen SL, Deng NY, Wang Y.
Bioinformatics. 2015 Sep 28. pii: btv528.

DoBo – Protein Domain Boundary Prediction by integrating Evolutionary Signals and Machine Learning

DoBo

:: DESCRIPTION

DoBo (Domain Boundary) is a tool to identify domain boundaries from sequence. It works by combining the classification power of machine learning with domain boundary signals embedded in multiple sequence alignments.

::DEVELOPER

MLiD Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

NO

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2011 Feb 1;12:43. doi: 10.1186/1471-2105-12-43.
DoBo: Protein domain boundary prediction by integrating evolutionary signals and machine learning.
Eickholt J1, Deng X, Cheng J.

PCP-ML – Protein Characterization Package for Machine Learning

PCP-ML 1.0

:: DESCRIPTION

The PCP-ML contains a number of functions that are commonly used when performing ML tasks with proteins.PCP-ML has three principle components: Parsers, Characterizers and Encodes and Writers. The parsers extract commonly used data from the output of programs such as PSIPred and DSSP. Characterizers and Encoders convert this data into forms which are meaningful in ML methods. There are also a number of characterizers provide numerical representations of hydrophobicity, contact potentials, etc. The writers format and output the generated features so as to be compatible with ML programs (e.g., SVMlight).

::DEVELOPER

MLiD Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 PCP-ML

:: MORE INFORMATION

Citation

BMC Res Notes. 2014 Nov 18;7:810. doi: 10.1186/1756-0500-7-810.
PCP-ML: protein characterization package for machine learning.
Eickholt J1, Wang Z.

MLbias 1.0 – Correct Machine Learning Bias

MLbias 1.0

:: DESCRIPTION

MLbias is an R package to correct for machine learning bias when many classifiers are compared and the best is selected

::DEVELOPER

George C. Tseng 

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 MLbias

:: MORE INFORMATION

Citation:

Bias correction for selecting the minimal-error classifier from many machine learning models.
Ding Y, Tang S, Liao SG, Jia J, Oesterreich S, Lin Y, Tseng GC.
Bioinformatics. 2014 Aug 1. pii: btu520.

mlDNA 1.1 – Machine Learning-based Differential Network Analysis of Transcriptome Data

mlDNA 1.1

:: DESCRIPTION

mlDNA is a R package  for machine learning (ML)-based differential network analysis of transcriptimic data.

::DEVELOPER

Wanglab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • R package

:: DOWNLOAD

 mlDNA

 :: MORE INFORMATION

Citation

Plant Cell. 2014 Feb;26(2):520-37. doi: 10.1105/tpc.113.121913. Epub 2014 Feb 11.
Machine learning-based differential network analysis: a study of stress-responsive transcriptomes in Arabidopsis.
Ma C1, Xin M, Feldmann KA, Wang X.