Supporting data for "Extensive evaluation of the generalized relevance network approach to inferring gene regulatory networks"

The generalized relevance network approach to network inference reconstructs network links based on the strength of associations between data in individual network nodes. It can reconstruct undirected networks, i.e. relevance networks, sensu stricto approach, as well as directed networks, approach referred to as causal relevance networks. The generalized approach allows the use of an arbitrary measure of pairwise association between nodes, an arbitrary scoring scheme that transforms the associations into weights of the network links, and a method for inferring the directions of the network links. While this makes the generalized relevance network approach powerful and flexible, it introduces the challenge of finding a combination of components that would perform well on a given inference task. We address this challenge by performing an extensive empirical analysis of the performance of 114 variants of the generalized relevance network approach on 47 tasks of gene network inference from time-series data and 39 tasks of gene network inference from steady-state data. The results of the evaluation reveal that the association measures based on correlation combined with a particular scoring scheme of asymmetric weighting lead to optimal performance of the relevance network approach in the general case. In the two special cases of inference tasks involving short time-series data and/or large networks, association measures based on identifying qualitative trends in the time series are more appropriate.