TAPESTRY
Huey Eng Chua, Sourav Saha Bhowmick, Jie Zheng, Lisa Tucker‐Kellogg · 2016
Target prioritization ranks molecules in biological networks according to a score that seeks to identify molecules that fulfill particular roles (e.g., drug targets). We study this problem in the context of partial information (e.g., unknown targets) and present TAPESTRY, a network-based approach that prioritizes candidate targets in a given signaling network with unknown targets by utilizing knowledge (target characteristics) gained from curated targets in another set of signaling networks. We consider both topological and dynamic features and use a weighted sum approach to examine the relative influence of these two classes of features on the prioritization results. TAPESTRY exploits a knowledge base of characterization models and predictive topological features of a set of signaling networks (candidate networks) with curated targets. Then, given a signaling network G with unknown targets, TAPESTRY identifies a candidate network most similar to G and selects its characterization model as prioritization model for computing a topological feature-based rank of each candidate node in G. Next, a dynamic feature-based rank is computed for these nodes by leveraging the time-series curves of ODEs associated with the edges in G. Finally, these two ranks are integrated and used for prioritizing candidate targets. We experimentally study the performance of TAPESTRY using signaling networks from BioModels with real-world curated outcomes. Our results demonstrate its effectiveness and superiority in comparison to state-of-the-art approaches.