Tag: SVM

miRPara 6.3 – SVM-based miRNA Prediction tool

miRPara 6.3

:: DESCRIPTION

miRPara predicts most probable mature miRNA coding regions from genome scale sequences in a species specific manner. We classified sequences from miRBase into animal, plant and overall categories and used a support vector machine (SVM) to train three models based on an initial set of 77 parameters related to the physical properties of the pre-miRNA and its miRNAs. By applying parameter filtering we found a subset of ~25 parameters produced higher prediction ability compared to the full set.

::DEVELOPER

miRPara team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 miRPara

:: MORE INFORMATION

Citation:

Wu Y., Wei B., Liu H., Li T., Rayner S.
MiRPara: a SVM-based software tool for prediction of most probable microRNA coding regions in genome scale sequences.
BMC Bioinformatics. 2011 Apr 19; 12(1):107

PalmPred – An SVM Based Method for Palmitoylated Peptide Prediction

PalmPred

:: DESCRIPTION

Palmpred utilizes one of the machine learning algorithm called Support Vector Machine for predicting cysteine-palmitoylation sites in proteins. It creates PSI-BLAST position-specific sequence profiles [PSSM] for each residue of protein sequence against NR90 database which is incorporated into Support Vector Machine for classification.

::DEVELOPER

Manish Kumar

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 PalmPred

:: MORE INFORMATION

Citation

PLoS One. 2014 Feb 19;9(2):e89246. doi: 10.1371/journal.pone.0089246. eCollection 2014.
PalmPred: an SVM based palmitoylation prediction method using sequence profile information.
Kumari B, Kumar R, Kumar M

LipocalinPred – SVM-based method for Prediction of Lipocalins

LipocalinPred

:: DESCRIPTION

LipocalinPred is an SVM-based prediction method to identify novel lipocalins using various protein features.

::DEVELOPER

Bioinformatics Centre ICGEB New Delhi

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2009 Dec 24;10:445. doi: 10.1186/1471-2105-10-445.
LipocalinPred: a SVM-based method for prediction of lipocalins.
Ramana J1, Gupta D.

R-SVM 2.0 – Recursive Sample Classification and Gene Selection with SVM

R-SVM 2.0

:: DESCRIPTION

R-SVM is a SVM-based method for doing supervised pattern recognition(classification) with microarray gene expression data.  The method uses SVM for both classification and for selecting a subset of relevant genes according to their relative contribution in the classification.  This process is done recursively so that a series of gene subsets and classification models can be obtained in a recursive manner, at different levels of gene selection.  The performance of the classification can be evaluated either on an independent test data set or by cross validation on the same data set.  R-SVM also includes an option for permutation experiments to assess the  significance of the performance.

::DEVELOPER

the Wong Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 R-SVM

:: MORE INFORMATION

Citation

ZHANG, X.G., LU, X., (Joint First Author) XU, X.Q., LEUNG, H.E., WONG, W.H. and LIU, J.S. (2006)
RSVM: A SVM based Strategy for Recursive Feature Selection and Sample Classification with Proteomics Mass-Spectrometry Data.
BMC Bioinformatics, 7:197

SAMSVM 1.01 – A tool for Misalignment Filtration on SAM-format Sequences with SVM

SAMSVM 1.01

:: DESCRIPTION

Applying the LIBSVM, a package of support vector machine, SAMSVM was developed to correctly detect and filter the misaligned reads of SAM format. Such filtration can reduce false positives in alignment and the following variant analysis.

::DEVELOPER

SAMSVM team

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Linux
  • C++ Compiler

:: DOWNLOAD

 SAMSVM

:: MORE INFORMATION

Citation

SAMSVM: A tool for misalignment filtration of SAM-format sequences with support vector machine.
Yang J, Ding X, Sun X, Tsang SY, Xue H.
J Bioinform Comput Biol. 2015 Aug 24:1550025.

PeakLink 1.0 – Peptide Peak Linking method in LC-MS/MS using Wavelet and SVM

PeakLink 1.0

:: DESCRIPTION

PeakLink (PL) uses information in both the time and frequency domain as inputs to a non-linear support vector machine (SVM) classifier. The PL algorithm first uses a threshold on retention time to remove candidate corresponding peaks with excessively large elution time shifts, then PL calculates the correlation between a pair of candidate peaks after removing noise through wavelet transformation. After converting retention time and peak shape correlation to statistical scores, an SVM classifier is trained and applied for differentiating corresponding and non-corresponding peptide peaks.

::DEVELOPER

PeakLink team

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Linux / Windows / MacOsX
  • MatLab

:: DOWNLOAD

 PeakLink

:: MORE INFORMATION

Citation

Bioinformatics. 2014 Sep 1;30(17):2464-70. doi: 10.1093/bioinformatics/btu299. Epub 2014 May 9.
PeakLink: a new peptide peak linking method in LC-MS/MS using wavelet and SVM.
Ghanat Bari M, Ma X, Zhang J.

CompareSVM – Support Vector Machine (SVM) Inference of Gene Regularity Networks

CompareSVM

:: DESCRIPTION

CompareSVM is a tool based on SVM to compare different kernel methods for inference of GRN.

::DEVELOPER

Ming Chen’s Bioinformatics Group, Zhejiang University.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • MatLab

:: DOWNLOAD

 CompareSVM

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2014 Nov 30;15(1):395.
CompareSVM: supervised, Support Vector Machine (SVM) inference of gene regularity networks.
Gillani Z, Akash M, Rahaman M, Chen M.

iSMP-Grey – Predicting Secretory Proteins of Malaria Parasite Using grey- PsePSSM and SVM

iSMP-Grey

:: DESCRIPTION

The web-server iSMP-Grey was establish for identifying uncharacterized proteins as secretory proteins of malaria parasite or not according to their evolutionary information in form of position specific scoring matrix (PSSM).

::DEVELOPER

Xiao Lab

:: SCREENSHOTS

n/a

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

PLoS One. 2012;7(11):e49040. doi: 10.1371/journal.pone.0049040. Epub 2012 Nov 26.
Predicting secretory proteins of malaria parasite by incorporating sequence evolution information into pseudo amino acid composition via grey system model.
Lin WZ1, Fang JA, Xiao X, Chou KC.

MEMPACK 2.0 – SVM Prediction of Membrane Helix Packing

MEMPACK 2.0

:: DESCRIPTION

MEMPACK allows users to submit a transmembrane protein sequence and returns transmembrane topology, lipid exposure, residue contacts, helix–helix interactions and helical packing arrangement predictions in both plain text and graphical formats using a number of novel machine learning-based algorithms.

MEMPACK Online Version

:DEVELOPER

Bioinformatics Group – University College London

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 MEMPACK

:: MORE INFORMATION

Citation:

Bioinformatics. 2011 May 15;27(10):1438-9. Epub 2011 Feb 23.
The MEMPACK alpha-helical transmembrane protein structure prediction server.
Nugent T, Ward S, Jones DT.

MEMSAT-SVM 1.3 – SVM Transmembrane Protein Structure Prediction

MEMSATSVM 1.3

:: DESCRIPTION

MEMSATSVM is a SVM (support vector machines) based TM (Transmembrane Protein) protein topology predictor.

:DEVELOPER

Bioinformatics Group – University College London

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 MEMSAT-SVM

:: MORE INFORMATION

Citation:

BMC Bioinformatics. 2012 Jul 17;13:169. doi: 10.1186/1471-2105-13-169.
Detecting pore-lining regions in transmembrane protein sequences.
Nugent T1, Jones DT.

Timothy Nugent and David T Jones
Transmembrane protein topology prediction using support vector machines
BMC Bioinformatics 2009, 10:159