PANI: an interactive data-driven tool for target prioritization in signaling networks

Biological network analysis often aims at the target identification problem, which is to predict which molecule to inhibit (or activate) for a disease treatment to achieve optimum efficacy and safety. A related goal, arising from the increasing availability of high-throughput screening (hts), is to suggest many molecules as potential targets. The target prioritization problem is to predict a subset of molecules in a given disease-associated network which is likely to include successful drug targets. Sensitivity analysis prioritizes targets in a dynamic network model according to principled criteria, but fails to penalize off-target effects, and does not scale for large networks. In this demonstration, we present Pani (Putative TArget Nodes PrIoritization), a novel interactive system that addresses these limitations. It prunes and ranks the possible target nodes by exploiting concentration-time profiles and network structure (topological) information and visually display them in t...

• Type: Journal
• Year: 2012
• Where: IHI
• Authors: Huey-Eng Chua, Sourav S. Bhowmick, Lisa Tucker-Kellogg, Yingqi Wang, Charles Forbes Dewey Jr., Hanry Yu
• Keywords: Drug Targets, Healthcare, IHI 2012, Interactive User, Putative Target