A comparative study on feature selection in regression for predicting the affinity of TAP binding peptides

Xue Ling Li, Shu Lin Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationAdvanced Intelligent Computing Theories and Applications
Subtitle of host publicationWith Aspects of Artificial Intelligence - 6th International Conference on Intelligent Computing, ICIC 2010, Proceedings
EditorsDe-Shuang Huang, Xiang Zhang
Pages69-75
Number of pages7
DOIs
StatePublished - Sep 6 2010
Event6th International Conference on Intelligent Computing, ICIC 2010 - Changsha, China
Duration: Aug 18 2010Aug 21 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6216 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other6th International Conference on Intelligent Computing, ICIC 2010
CountryChina
CityChangsha
Period8/18/108/21/10

Keywords

  • Transporter associated with antigen processing
  • feature selection
  • k-nearest neighborhood regression
  • neighborhood rough set model
  • peptides
  • support vector regression

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Li, X. L., & Wang, S. L. (2010). A comparative study on feature selection in regression for predicting the affinity of TAP binding peptides. In D-S. Huang, & X. Zhang (Eds.), Advanced Intelligent Computing Theories and Applications: With Aspects of Artificial Intelligence - 6th International Conference on Intelligent Computing, ICIC 2010, Proceedings (pp. 69-75). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6216 LNAI). https://doi.org/10.1007/978-3-642-14932-0_9