Microarray-based classification and clinical predictors: on combined classifiers and additional predictive value.
BACKGROUND
In the context of clinical bioinformatics methods are needed for assessing the additional predictive value of microarray data compared to simple clinical parameters alone. Such methods should also provide an optimal prediction rule making use of all potentialities of both types of data: they should ideally be able to catch subtypes which are not identified by clinical parameters alone. Moreover, they should address the question of the additional predictive value of microarray data in a fair framework.


RESULTS
We propose a novel but simple two-step approach based on random forests and partial least squares (PLS) dimension reduction embedding the idea of pre-validation suggested by Tibshirani and colleagues, which is based on an internal cross-validation for avoiding overfitting. Our approach is fast, flexible and can be used both for assessing the overall additional significance of the microarray data and for building optimal hybrid classification rules. Its efficiency is demonstrated through simulations and an application to breast cancer and colorectal cancer data.


BACKGROUND
Our method is implemented in the freely available R package 'MAclinical' which can be downloaded from http://www.stat.uni-muenchen.de/~socher/MAclinical
Sylvia Lawry Centre for MS Research, Hohenlindenerstr. 1, D-81677 Munich, Germany. boulesteix@slcmsr.org
Anne-Laure
AL
Boulesteix
Christine
C
Porzelius
Martin
M
Daumer
D000465:Algorithms; D001185:Artificial Intelligence; D014408:Biomarkers, Tumor / Q000032:analysis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D009363:Neoplasm Proteins / Q000032:analysis*; D009369:Neoplasms / Q000175:diagnosis* / Q000378:metabolism*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods
D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins