Fig. 1.

We simulate a noisy rank aggregation task with two types of datasets. For 10 ‘signal’ datasets, the values for 50 differentially expressed genes are drawn from a $\mathcal{N}$(1,1) distribution, while the values for 950 background genes are drawn from a $\mathcal{N}$(0,1) distribution. For 30 ‘noise’ datasets, the values are drawn from the same distribution. We aggregate the data using different rank aggregation methods and compare the results to those obtained with an optimal naive Bayes (i.e. using the exact conditional distributions). The BIRRA algorithm outperforms other aggregation methods producing results that are between the optimal naive Bayes and established rank aggregation methods. AUC values: Mean Ranks 0.82, RRA 0.78, Stuart 0.85, BIRRA 0.91, Naive Bayes 0.99