Systems biology HyDRA: gene prioritization via hybrid distance-score rank aggregation

Summary: Gene prioritization refers to a family of computational techniques for inferring disease genes through a set of training genes and carefully chosen similarity criteria. Test genes are scored based on their average similarity to the training set, and the rankings of genes under various simi-larity criteria are aggregated via statistical methods. The contributions of our work are threefold: (i) first, based on the realization that there is no unique way to define an optimal aggregate for rank-ings, we investigate the predictive quality of a number of new aggregation methods and known fusion techniques from machine learning and social choice theory. Within this context, we quantify the influence of the number of training genes and similarity criteria on the diagnostic quality of the aggregate and perform in-depth cross-validation studies; (ii) second, we propose a new approach to genomic data aggregation, termed HyDRA (Hybrid Distance-score Rank Aggregation), which combines the advantages of score-based and combinatorial aggregation techniques. We also pro-pose incorporating a new top-versus-bottom (TvB) weighting feature into the hybrid schemes. The TvB feature ensures that aggregates are more reliable at the top of the list, rather than at the bottom, since only top candidates are tested experimentally; (iii) third, we propose an iterative pro