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Support Vector Machines for predicting protein structural class
TLDR
It is expected that the Support Vector Machine method and the elegant component-coupled method, if complemented with each other, can provide a powerful computational tool for predicting the structural classes of proteins. Expand
Using Functional Domain Composition and Support Vector Machines for Prediction of Protein Subcellular Location*
TLDR
With such a novel representation for a protein, the support vector machine (SVM) algorithm is introduced for predicting protein subcellular location and high success rates are obtained by the self-consistency test, jackknife test, and independent dataset test. Expand
Prediction of Protein Structural Classes by Support Vector Machines
TLDR
The support vector machine method is applied to approach the prediction of protein structural class and indicates that the structural class of a protein inconsiderably correlated with its amino and composition can be referred as a powerful computational tool for predicting the structural classes of proteins. Expand
Support vector machines for predicting rRNA-, RNA-, and DNA-binding proteins from amino acid sequence.
TLDR
This work introduces SVM and the pseudo-amino acid composition, a collection of nonlinear features extractable from protein sequence, to the field of protein function prediction and develops prototype SVMs for binary classification of rRNA-, RNA-, and DNA-binding proteins. Expand
Using LogitBoost classifier to predict protein structural classes.
TLDR
The LogitBoost, one of the boosting algorithms developed recently, is introduced for predicting protein structural classes using a regression scheme as the base learner, which can handle multi-class problems and is particularly superior in coping with noisy data. Expand
Prediction of Membrane Protein Types by Incorporating Amphipathic Effects
TLDR
The amphiphilic pseudo amino acid composition has been formulated that contains a series of hydrophobic and hydrophilic correlation factors that have been remarkably enhanced in identifying the types of membrane proteins, as demonstrated by the jackknife test and independent data set test. Expand
Prediction of protein subcellular locations by GO-FunD-PseAA predictor.
TLDR
A powerful predictor has been developed by hybridizing the gene ontology approach with the pseudo-amino acid composition approach, and the overall success rate of prediction obtained by the jackknife cross-validation was 92%. Expand
Support vector machines for prediction of protein subcellular location by incorporating quasi‐sequence‐order effect
TLDR
Support Vector Machine (SVM), which is one class of learning machines, was applied to predict the subcellular location of proteins by incorporating the quasi‐sequence‐order effect, with good results for self‐consistency and jackknife testing. Expand
Boosting classifier for predicting protein domain structural class.
TLDR
It was demonstrated thru jackknife cross-validation tests that LogitBoost outperformed other classifiers including "support vector machine," a very powerful classifier widely used in biological literatures. Expand
Predicting protein localization in budding Yeast
TLDR
A new method has been developed that can be used to predict subcellular localization of proteins with multiplex location feature by hybridizing gene ontology, functional domain and pseudo amino acid composition approaches. Expand
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