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.
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Li, XL., Wang, SL. (2010). A Comparative Study on Feature Selection in Regression for Predicting the Affinity of TAP Binding Peptides. In: Huang, DS., Zhang, X., Reyes GarcÃa, C.A., Zhang, L. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2010. Lecture Notes in Computer Science(), vol 6216. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14932-0_9
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DOI: https://doi.org/10.1007/978-3-642-14932-0_9
Publisher Name: Springer, Berlin, Heidelberg
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