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Review
. 2003 Nov 3;89(9):1599-604.
doi: 10.1038/sj.bjc.6601326.

Diagnostic and prognostic prediction using gene expression profiles in high-dimensional microarray data

R Simon  1
Affiliations
Review

Diagnostic and prognostic prediction using gene expression profiles in high-dimensional microarray data

R Simon. Br J Cancer. .

Abstract

DNA microarrays are a potentially powerful technology for improving diagnostic classification, treatment selection and therapeutics development. There are, however, many potential pitfalls in the use of microarrays that result in false leads and erroneous conclusions. This paper provides a review of the key features to be observed in developing diagnostic and prognostic classification systems based on gene expression profiling and some of the pitfalls to be aware of in reading reports of microarray-based studies.

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Figures

Figure 1
Figure 1
Schematic diagram of leave-one-out cross-validation (LOOCV).
Figure 2
Figure 2
Tree structured classifier for predicting the unknown class of a tissue specimen when four classes are possible. Binary classifier A based on gene expression profile is first used to predict whether the specimen is in subset {I,III} or in subset {II,IV} of classes. For those specimens predicted to be in subset {I,III}, binary classifier B is used to predict whether the specimen is in class I or III. For those specimens predicted to be in subset {II,IV} based on classifier A, binary classifier C is used to predict whether the specimen is in class II or IV. The three binary classifiers will generally utilise different gene sets for prediction.
See this image and copyright information in PMC

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