Tapestry is 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.
Publication: TAPESTRY: Network-centric Target Prioritization in Disease-related Signaling Networks (ACM BCB, 2016) (PDF)
A network-based approach that characterizes known targets in signaling networks using topological features.
Publication: TENET: Topological Feature-based Target Characterization in Signaling Networks (Bioinformatics, 31(20), Oxford Univ Press, October 2015) (PubMed)
A network-based approach that ranks a given set of networks based on its "similarity" to a reference network.
Publication: TINTIN: Exploiting Target Features for Signaling Network Similarity Computation and Ranking (ACM BCB, 2017) (PDF)
An integrated platform for annotating, visualizing and characterizing hallmarks in cancer signaling networks.
Publication: TROVE: A User-friendly Tool for Visualizing and Analyzing Cancer Hallmarks in Signaling Networks (To appear in Bioinformatics) (PubMed) (Video)