:: DESCRIPTION
SVM-PSSM-DT is a web server for for identifying the DNA-binding proteins by combining Support Vector Machine and PSSM Distance Transformation.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
BMC Syst Biol. 2015;9 Suppl 1:S10. doi: 10.1186/1752-0509-9-S1-S10. Epub 2015 Feb 6.
Identifying DNA-binding proteins by combining support vector machine and PSSM distance transformation.
Xu R, Zhou J, Wang H, He Y, Wang X, Liu B.
:: DESCRIPTION
MOCSVM uses original SVM light implementation for SVM classification.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Maulik U, Mukhopadhyay A, Bandyopadhyay S (2009.)
Combining Pareto-Optimal Clusters using Supervised Learning for Identifying Co-expressed Genes.
BMC Bioinformatics, Vol.10, No. 27.
:: DESCRIPTION
MIEC-SVM method aims to characterize the energetic patterns of proteins binding to their partners.
::DEVELOPER
:: SCREENSHOTS
N/A
::REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
MIEC-SVM: Automated Pipeline for Protein Peptide/ligand Interaction Prediction.
Li N, Ainsworth RI, Wu M, Ding B, Ainsworth RI, Wang W.
Bioinformatics. 2015 Nov 14. pii: btv666.
:: 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
:: DOWNLOAD
:: 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.
:: DESCRIPTION
miRPredictor is a novel miRNA target predictor which is based on support vector machine (SVM), a widely-used machine learning approach, combined with feature selection procedure. We considered different types of features including the flanking sequences of the potential targets and pattern information. The features selected were also analyzed to dig out the intrinsic mechanism of miRNA-target interaction.
::DEVELOPER
Ming Chen’s Bioinformatics Group, Zhejiang University.
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
:: DESCRIPTION
SVMcon predicts medium- to long-range residue-residue contacts using Support Vector Machines. The contact predictions are in the CASP format (residue index 1, residue index 2, 0, 8, contact probability). The contact distance threshold is 8 angstrom. The sequence separation between two residues is at least 6 residues.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
BMC Bioinformatics. 2007 Apr 2;8:113.
Improved residue contact prediction using support vector machines and a large feature set.
Cheng J, Baldi P.
