Graph biased feature selection of genes is better than random for many genes

Abstract

Recent work suggests that gene expression dependencies can be predicted almost as well by using random networks as by using experimentally derived interaction networks. We hypothesize that this effect is highly variable across genes, as useful and robust experimental evidence exists for some genes but not others. To explore this variation, we take the k-core decomposition of the STRING network, and compare it to a degree-matched random model. We show that when low-degree nodes are removed, expression dependencies in the remaining genes can be predicted better by the resulting network than by the random model.