Name
Title | ||
|---|---|---|
A comparative study on feature selection in regression for predicting the affinity of TAP binding peptides |
Abstract | ||
|---|---|---|
In this study, we compare six feature selection methods, i.e. five feature selection methods for k Nearest Neighborhood regression (kNNReg) and a rough set model based forward feature selection (FARNeM) for Support Vector Regression (SVR) for predicting the affinity of TAP binding peptides. The peptides were represented with binary, sequence associated amino acid properties, and binary plus properties of amino acids derived vectors, respectively. The weighted peptide features are input to the regression model and ranked according to the corresponding weights or the occurrence frequency, respectively. We find that SVR model performs better than kNNReg model for the prediction of the affinity of TAP transporter binding peptides. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-14932-0_9 | ICIC (2) |
Keywords | Field | DocType |
binding peptides,comparative study,knnreg model,amino acid,feature selection method,svr model,rough set model,regression model,tap binding peptides,weighted peptide feature,tap transporter,transporter associated with antigen processing,rough set,feature selection,binary sequence,antigen processing,support vector regression | Pattern recognition,Regression,Feature selection,Regression analysis,Amino acid,Support vector machine,Peptide,Rough set,Transporter associated with antigen processing,Artificial intelligence,Mathematics,Machine learning | Conference |
Volume | ISSN | ISBN |
6216 | 0302-9743 | 3-642-14931-6 |
Citations | PageRank | References |
3 | 0.49 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xueling Li | 1 | 74 | 5.80 |
| Shulin Wang | 2 | 27 | 7.13 |