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One feature doesn't fit all: characterizing topological features of targets in signaling networks

A key challenge facing drug discovery is the identification of target(s) in a signaling network whose perturbation results in a desired therapeutic outcome. Recent studies have shown that analysis of biological networks based on topology can facilitate target identification by providing valuable information on characteristics of targets. In this paper, we present an algorithm called Differ that discovers the discriminative topological features (dtf) from a signaling network to distinguish the targets from the non-targets. Our empirical study on five signaling networks reveals that the majority of dtfs are able to identify most of the known targets in these networks. Furthermore, they are distinct for different networks. That is, no single topological feature can characterise targets in all signaling networks. This is in contrast to the findings in [28] where bridging nodes are considered to be good targets with low lethality across several ppi networks.