Predicting Disulfide Connectivity Based on Correlation Coefficients Selection
LIU Kun
Abstract：Disulfide connectivity is one of significant protein structural characteristic. Previous prediction methods usually used support vector regression,which didn 't consider the correlation between different features. According to traditional prediction methods,based on fisher score,this paper calculated correlation coefficient of each pair of features after feature selection,then de-leted the features with high correlation coefficient. Based on the rest features,support vector regression was used to train model and test. 4-fold validation was used on our benchmark dataset to gain a hopeful result comparing with previous results.
Keywords：bioinformaticsdisulfide bondsupport vector regressioncorrelation coefficientfeature selection
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 2093-2096,2117 )