:: DESCRIPTION
MetaGUN is a novel gene prediction method for metagenomic fragments based on a machine learning approach of SVM.
::DEVELOPER
ZhuLab, Peking Uiniversity, Beijing
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
BMC Bioinformatics. 2013;14 Suppl 5:S12. doi: 10.1186/1471-2105-14-S5-S12. Epub 2013 Apr 10.
Gene prediction in metagenomic fragments based on the SVM algorithm.
Liu Y, Guo J, Hu G, Zhu H.
:: DESCRIPTION
STEPP is a support vector machine (SVM) model that uses a simple descriptor space based on 35 properties of amino acid content, charge, hydrophilicity and polarity for the quantitative prediction of proteotypic peptides.
::DEVELOPER
Computational Biology & Bioinformatics, Pacific Northwest National Laboratory
:: SCREENSHOTS
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Bioinformatics. 2010 Jul 1;26(13):1677-83.
A support vector machine model for the prediction of proteotypic peptides for accurate mass and time proteomics.
Webb-Robertson BJ, Cannon WR, Oehmen CS, Shah AR, Gurumoorthi V, Lipton MS, Waters KM.
:: DESCRIPTION
SVMotif is a mechine learning based motif finder. It can be classified into K-mer enumeration based methods. the evaluation of each possible K-mer is done by Recursive SVM feature selection
::DEVELOPER
:: SCREENSHOTS
n/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Ensemble machine methods for DNA binding
(with Y. Fan, and C. DeLisi),
Machine Learning and Applications 7, M. Wani, et al., eds. IEEE, Washington (2008),709-716.
:: DESCRIPTION
svmPRAT is a general purpose protein residue annotation toolkit to allow biologists to formulate residue-wise prediction problems. svmPRAT formulates annotation problem as a classification or regression problem using support vector machines. The key features of svmPRAT are its ease of use to incorporate any user-provided information in the form of feature matrices. For every residue svmPRAT captures local information around the reside to create fixed length feature vectors. svmPRAT implements accurate and fast kernel functions, and also introduces a flexible window-based encoding scheme that allows better capture of signals for certain prediction problems.
::DEVELOPER
:: SCREENSHOTS
Command Line
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation:
BMC Bioinformatics. 2009 Dec 22;10:439.
svmPRAT: SVM-based protein residue annotation toolkit.
Rangwala H, Kauffman C, Karypis G.
