DREAM2 challenge: Integrated multi-array supervised learning algorithm for BCL6 transcriptional targets prediction

Abstract

In the Dialogue for Reverse Engineering Assessments and Methods Conference (DREAM2) BCL6 target identification challenge, we were given a list of 200 genes and tasked to identify which ones are the true targets of BCL6 using an independent panel of gene-expression data. Initial efforts using conventional motif-scanning approaches to find BCL6 binding sites in the promoters of the 200 genes as a means of identifying BCL6 true targets proved unsuccessful. Instead, we performed a large-scale comparative study of multiple expression data under different conditions. Specifically, we employed a supervised learning approach that learns and models the expression patterns under different conditions and controls from a training collection of known BCL6 targets and randomly chosen decoys. Genes in the given list whose expression matches well with that of the training set of known BCL6 targets are more likely to be BCL6 targets. Using this approach, we are able to identify BCL6 targets with high accuracy, making us joint best performers of the challenge. © 2009 New York Academy of Sciences.