BACKGROUND: One of the main goals in cancer studies including high-throughput microRNA (miRNA) and mRNA data is to find and assess prognostic signatures capable of predicting clinical outcome. Both mRNA and miRNA expression changes in cancer diseases are described to reflect clinical characteristics like staging and prognosis. Furthermore, miRNA abundance can directly affect target transcripts and translation in tumor cells. Prediction models are trained to identify either mRNA or miRNA signatures for patient stratification. With the increasing number of microarray studies collecting mRNA and miRNA from the same patient cohort there is a need for statistical methods to integrate or fuse both kinds of data into one prediction model in order to find a combined signature that improves the prediction. RESULTS: Here, we propose a new method to fuse miRNA and mRNA data into one prediction model. Since miRNAs are known regulators of mRNAs we used the correlations between them as well as the target prediction information to build a bipartite graph representing the relations between miRNAs and mRNAs. This graph was used to guide the feature selection in order to improve the prediction. The method is illustrated on a prostate cancer data set comprising 98 patient samples with miRNA and mRNA expression data. The biochemical relapse was used as clinical endpoint. It could be shown that the bipartite graph in combination with both data sets could improve prediction performance as well as the stability of the feature selection. CONCLUSIONS: Fusion of mRNA and miRNA expression data into one prediction model improves clinical outcome prediction in terms of prediction error and stable feature selection. The R source code of the proposed method is available in the supplement.

UNLABELLED: There is a multitude of new techniques that promise to extract predictive information in bioinformatics applications. It has been recognized that a first step for validation of the resulting model fits should rely on proper use of resampling techniques. However, this advice is frequently not followed, potential reasons being difficulty of correct implementation and computational demand. This is addressed by the R package peperr, which is designed for reliable prediction error estimation through resampling, potentially accelerated by parallel execution on a compute cluster. Its interface allows easy connection to newly developed model fitting routines. Performance evaluation of the latter is furthermore guided by diagnostic plots, which helps to detect specific problems due to high-dimensional data structures. AVAILABILITY: http://cran.r-project.org, http://www.imbi.uni-freiburg.de/parallel. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: 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. AVAILABILITY: Our method is implemented in the freely available R package 'MAclinical' which can be downloaded from http://www.stat.uni-muenchen.de/~socher/MAclinical