title: TENET: topological feature-based target characterization in signalling networks.
abstract: BACKGROUND
  Target characterization for a biochemical network is a heuristic evaluation process that produces a characterization model that may aid in predicting the suitability of each molecule for drug targeting. These approaches are typically used in drug research to identify novel potential targets using insights from known targets. Traditional approaches that characterize targets based on their molecular characteristics and biological function require extensive experimental study of each protein and are infeasible for evaluating larger networks with poorly understood proteins. Moreover, they fail to exploit network connectivity information which is now available from systems biology methods. Adopting a network-based approach by characterizing targets using network features provides greater insights that complement these traditional techniques. To this end, we present Tenet (Target charactErization using NEtwork Topology), a network-based approach that characterizes known targets in signalling networks using topological features.
  
  
  RESULTS
  Tenet first computes a set of topological features and then leverages a support vector machine-based approach to identify predictive topological features that characterizes known targets. A characterization model is generated and it specifies which topological features are important for discriminating the targets and how these features should be combined to quantify the likelihood of a node being a target. We empirically study the performance of Tenet from a wide variety of aspects, using several signalling networks from BioModels with real-world curated outcomes. Results demonstrate its effectiveness and superiority in comparison to state-of-the-art approaches.
  
  
  METHODS
  Our software is available freely for non-commercial purposes from: https://sites.google.com/site/cosbyntu/softwares/tenet
  
  
  BACKGROUND
  hechua@ntu.edu.sg or assourav@ntu.edu.sg
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: School of Computer Engineering, Nanyang Technological University.
authors.forename: Huey Eng
authors.initials: HE
authors.lastname: Chua
authors.affiliation: School of Computer Engineering, Nanyang Technological University.
authors.forename: Sourav S
authors.initials: SS
authors.lastname: Bhowmick
authors.affiliation: Duke-NUS Graduate Medical School, National University of Singapore, Singapore and.
authors.forename: Lisa
authors.initials: L
authors.lastname: Tucker-Kellogg
authors.affiliation: Biological Engineering Department, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
authors.forename: C Forbes
authors.initials: CF
authors.lastname: Dewey
mesh_terms: D000465:Algorithms; D006801:Humans; D025941:Protein Interaction Mapping; D015398:Signal Transduction; D012984:Software; D060388:Support Vector Machine