This article is part of the supplement: Proceedings of the 23rd International Conference on Genome Informatics (GIW 2012)
Embracing noise to improve cross-batch prediction accuracy
1 NUS Graduate School for Integrative Sciences and Engineering, Singapore 117597
2 School of Computing, National University of Singapore, Singapore 117417
BMC Systems Biology 2012, 6(Suppl 2):S3 doi:10.1186/1752-0509-6-S2-S3Published: 12 December 2012
One important application of microarray in clinical settings is for constructing a diagnosis or prognosis model. Batch effects are a well-known obstacle in this type of applications. Recently, a prominent study was published on how batch effects removal techniques could potentially improve microarray prediction performance. However, the results were not very encouraging, as prediction performance did not always improve. In fact, in up to 20% of the cases, prediction accuracy was reduced. Furthermore, it was stated in the paper that the techniques studied require sufficiently large sample sizes in both batches (train and test) to be effective, which is not a realistic situation especially in clinical settings. In this paper, we propose a different approach, which is able to overcome limitations faced by conventional methods. Our approach uses ranking value of microarray data and a bagging ensemble classifier with sequential hypothesis testing to dynamically determine the number of classifiers required in the ensemble. Using similar datasets to those in the original study, we showed that in only one case (<2%) is our performance reduced (by more than -0.05 AUC) and, in >60% of cases, it is improved (by more than 0.05 AUC). In addition, our approach works even on much smaller training data sets and is independent of the sample size of the test data, making it feasible to be applied on clinical studies.