Classification of microarray data with penalized logistic regression

P. H.C. Eilers, J. M. Boer, G. J. Van Ommen, H. C. Van Houwelingen

Onderzoeksoutput: Bijdrage aan tijdschriftCongresartikelpeer review

43 Citaten (Scopus)


Classification of microarray data needs a firm statistical basis. In principle, logistic regression can provide it, modeling the probability of membership of a class with (transforms of) linear combinations of explanatory variables. However, classical logistic regression does not work for microarrays, because generally there will be far more variables than observations. One problem is multicollinearity: estimating equations become singular and have no unique and stable solution. A second problem is over-fitting: a model may fit well to a data set, but perform badly when used to classify new data. We propose penalized likelihood as a solution to both problems. The values of the regression coefficients are constrained in a similar way as in ridge regression. All variables play an equal role, there is no ad-hoc selection of most "relevant" or "most expressed" genes. The dimension of the resulting systems of equations is equal to the number of variables, and generally will be too large for most computers, but it can dramatically be reduced with the singular value decomposition of some matrices. The penalty is optimized with AIC (Akaike's Information Criterion), which essentially is a measure of prediction performance. We find that penalized logistic regression performs well on a public data set (the MIT ALL/AML data).

Originele taal-2Engels
Pagina's (van-tot)187-198
Aantal pagina's12
TijdschriftProceedings of SPIE - The International Society for Optical Engineering
StatusGepubliceerd - 2001
Extern gepubliceerdJa
EvenementMicroarrays: Optical Technologies and Informatics - San Jose, CA, Verenigde Staten van Amerika
Duur: 21 jan. 200022 jan. 2000


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