Molecular diagnosis. Classification, model selection and performance evaluation.

Abstract:

OBJECTIVES:We discuss supervised classification techniques applied to medical diagnosis based on gene expression profiles. Our focus lies on strategies of adaptive model selection to avoid overfitting in high-dimensional spaces. METHODS:We introduce likelihood-based methods, classification trees, support vector machines and regularized binary regression. For regularization by dimension reduction, we describe feature selection methods: feature filtering, feature shrinkage and wrapper approaches. In small sample-size situations efficient methods of data re-use are needed to assess the predictive power of a model. We discuss two issues in using cross-validation: the difference between in-loop and out-of-loop feature selection, and estimating model parameters in nested-loop cross-validation. RESULTS:Gene selection does not reduce the dimensionality of the model. Tuning parameters enable adaptive model selection. The feature selection bias is a common pitfall in performance evaluation. Model selection and performance evaluation can be combined by nested-loop cross-validation. CONCLUSIONS:Classification of microarrays is prone to overfitting. A rigorous and unbiased assessment of the predictive power of the model is a must.

journal_name

Methods Inf Med

authors

Markowetz F,Spang R

keywords:

subject

Has Abstract

pub_date

2005-01-01 00:00:00

pages

438-43

issue

3

eissn

0026-1270

issn

2511-705X

pii

05030438

journal_volume

44

pub_type

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