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http://dx.doi.org/10.9709/JKSS.2013.22.4.139

Feature Selection for Classification of Mass Spectrometric Proteomic Data Using Random Forest  

Ohn, Syng-Yup (한국항공대학교 컴퓨터 공학과)
Chi, Seung-Do (한국항공대학교 컴퓨터 공학과)
Han, Mi-Young (한국과학창의재단)
Publication Information
Journal of the Korea Society for Simulation / v.22, no.4, 2013 , pp. 139-147 More about this Journal
Abstract
This paper proposes a novel method for feature selection for mass spectrometric proteomic data based on Random Forest. The method includes an effective preprocessing step to filter a large amount of redundant features with high correlation and applies a tournament strategy to get an optimal feature subset. Experiments on three public datasets, Ovarian 4-3-02, Ovarian 7-8-02 and Prostate shows that the new method achieves high performance comparing with widely used methods and balanced rate of specificity and sensitivity.
Keywords
Feature Selection; Bioinformatics; Pattern Recognition; SELDI-TOF; Proteome; Spectrum; Random Forest; Pearson Correlation;
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