Development of biomarker classifiers from high-dimensional data.

Abstract:

: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.

journal_name

Brief Bioinform

authors

Baek S,Tsai CA,Chen JJ

doi

10.1093/bib/bbp016

subject

Has Abstract

pub_date

2009-09-01 00:00:00

pages

537-46

issue

5

eissn

1467-5463

issn

1477-4054

pii

bbp016

journal_volume

10

pub_type

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