期刊
BRIEFINGS IN BIOINFORMATICS
卷 10, 期 5, 页码 537-546出版社
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbp016
关键词
class prediction; cross-validation; feature selection; frequency of selection; stable feature set
资金
- China Medical University of Taiwan, ROC [CMU 96-127]
Recent development of high-throughput technology has accelerated interest in the development of molecular biomarker classifiers for safety assessment, disease diagnostics and prognostics, and prediction of response for patient assignment. This article reviews and evaluates some important aspects and key issues in the development of biomarker classifiers. Development of a biomarker classifier for high-throughput data involves two components: (i) model building and (ii) performance assessment. This article focuses on feature selection in model building and cross validation for performance assessment. A frequency approach to feature selection is presented and compared to the conventional approach in terms of the predictive accuracy and stability of the selected feature set. The two approaches are compared based on four biomarker classifiers, each with a different feature selection method and well-known classification algorithm. In each of the four classifiers the feature predictor set selected by the frequency approach is more stable than the feature set selected by the conventional approach.
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