title: Gene expression profiles for predicting metastasis in breast cancer: a cross-study comparison of classification methods.
abstract: Machine learning has increasingly been used with microarray gene expression data and for the development of classifiers using a variety of methods. However, method comparisons in cross-study datasets are very scarce. This study compares the performance of seven classification methods and the effect of voting for predicting metastasis outcome in breast cancer patients, in three situations: within the same dataset or across datasets on similar or dissimilar microarray platforms. Combining classification results from seven classifiers into one voting decision performed significantly better during internal validation as well as external validation in similar microarray platforms than the underlying classification methods. When validating between different microarray platforms, random forest, another voting-based method, proved to be the best performing method. We conclude that voting based classifiers provided an advantage with respect to classifying metastasis outcome in breast cancer patients.
authors.affiliation: Research Unit of Human Genetics, Institute of Clinical Research, University of Southern Denmark, Sdr. Boulevard 29, 5000 Odense C, Denmark. mark.burton@ouh.regionsyddanmark.dk
authors.forename: Mark
authors.initials: M
authors.lastname: Burton
authors.forename: Mads
authors.initials: M
authors.lastname: Thomassen
authors.forename: Qihua
authors.initials: Q
authors.lastname: Tan
authors.forename: Torben A
authors.initials: TA
authors.lastname: Kruse
mesh_terms: D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000235:genetics* / Q000473:pathology*; D030541:Databases, Genetic; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D008198:Lymph Nodes / Q000473:pathology; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods* / Q000706:statistics & numerical data; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D059467:Transcriptome