Diagnostic and prognostic prediction using gene expression profiles in high-dimensional microarray data - PubMed (original) (raw)

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

R Simon. Br J Cancer. 2003.

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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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.

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References

    1. Alizadeh AA, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald A, Boldrich JC, Sabet H, Tran T, Yu X, Powell JI, Yang L, Marti GE, Moore T, Hudson J, Lu L, Lewis DB, Tibshirani R, Sherlock G, Chan WC, Greiner TC, Weisenburger DD, Armitage JO, Warnke R, Levy R, Wilson W, Grever MR, Byrd JC, Botstein D, Brown PO, Staudt LM (2000) Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403: 503–511 - PubMed
    1. Alter O, Brown PO, Botstein D (2000) Singular value decomposition for genome-wide expression data processing and modeling. Proc Natl Acad Sci 97: 10101–10106 - PMC - PubMed
    1. Ambroise C, McLachlan GJ (2002) Selection bias in gene extraction on the basis of microarray gene-expression data. Proc Natl Acad Sci 99: 6562–6566 - PMC - PubMed
    1. Ben-Dor A, Bruhn L, Friedman N, Nachman I, Schummer M, Yakhini Z (2000) Tissue classification with gene expression profiles. J Comput Biol 7: 559–584 - PubMed
    1. Bittner M, Meltzer P, Chen Y, Jiang Y, Seftor E, Hendrix M, Radmacher M, Simon R, Yakhini Z, Ben-Dor A, Dougherty E, Wang E, Marincola F, Gooden C, Lueders J, Glatfelter A, Pollock P, Gillanders E, Leja D, Dietrich K, Beaudry C, Berens M, Alberts D, VSondak Hayward N, Trent J (2000) Molecular classification of cutaneous malignant melanoma by gene expression profiling. Nature 406: 536–540 - PubMed

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