2014/11/13 by Mark A. van de Wiel, Tonje G. Lien, van de Wiel, Mark A. +7 · 1 citation
Biochemistry, Genetics and Molecular Biology · Mathematics · Medicine · #62J07 #Bioinformatics and Genomic Networks #FOS: Computer and information sciences #Gene expression and cancer classification #Methodology (stat.ME) #Radiomics and Machine Learning in Medical Imaging #Statistical Methods and Inference #msc:62J07 #stat.ME
paper · pdf · doi:10.48550/arxiv.1411.3496
15 pages, 2 figures. Supplementary Information available on first author's web site
openalex publication_date 2014/11/13 · arxiv created 2015/05/18 · arxiv updated 2015/05/19 · openalex created_date 2025/10/24 · openalex updated_date 2026/07/28
For many high-dimensional studies, additional information on the variables, like (genomic) annotation or external p-values, is available. In the context of binary and continuous prediction, we develop a method for adaptive group-regularized (logistic) ridge regression, which makes structural use of such 'co-data'. Here, 'groups' refer to a partition of the variables according to the co-data. We derive empirical Bayes estimates of group-specific penalties, which possess several nice properties: i) they are analytical; ii) they adapt to the informativeness of the co-data for the data at hand; iii) only one global penalty parameter requires tuning by cross-validation. In addition, the method allows use of multiple types of co-data at little extra computational effort. We show that the group-specific penalties may lead to a larger distinction between `near-zero' and relatively large regression parameters, which facilitates post-hoc variable selection. The method, termed GRridge, is implemented in an easy-to-use R-package. It is demonstrated on two cancer genomics studies, which both concern the discrimination of precancerous cervical lesions from normal cervix tissues using methylation microarray data. For both examples, GRridge clearly improves the predictive performances of ordinary logistic ridge regression and the group lasso. In addition, we show that for the second study the relatively good predictive performance is maintained when selecting only 42 variables.