title: Identifying the topology of signaling networks from partial RNAi data.
abstract: Methods for inferring signaling networks using single gene knockdown RNAi experiments and reference networks have been proposed in recent years. These methods assume that RNAi information is available for all the genes in the signal transduction pathway, i.e., complete. This assumption does not always hold up since RNAi experiments are often incomplete and information for some genes is missing.
  
  
  
  In this article, we develop two methods to construct signaling networks from incomplete RNAi data with the help of a reference network. These methods infer the RNAi constraints for the missing genes such that the inferred network is closest to the reference network. We perform extensive experiments with both real and synthetic networks and demonstrate that these methods produce accurate results efficiently.
  
  
  
  Application of our methods to Wnt signal transduction pathway has shown that our methods can be used to construct highly accurate signaling networks from experimental data in less than 100 ms. The two methods that produce accurate results efficiently show great promise of constructing real signaling networks.
authors.affiliation: Department of Computer & Information Science & Engineering, University of Florida, Gainesville, 32611, FL, USA. yuanfang@cise.ufl.edu.
authors.forename: Yuanfang
authors.initials: Y
authors.lastname: Ren
authors.affiliation: Department of Computer & Information Science & Engineering, University of Florida, Gainesville, 32611, FL, USA.
authors.forename: Qiyao
authors.initials: Q
authors.lastname: Wang
authors.affiliation: Department of Computer & Information Science & Engineering, University of Florida, Gainesville, 32611, FL, USA.
authors.forename: Md Mahmudul
authors.initials: MM
authors.lastname: Hasan
authors.affiliation: Department of Biology & Mathematics, Colgate University, Hamilton, 13346, NY, USA.
authors.forename: Ahmet
authors.initials: A
authors.lastname: Ay
authors.affiliation: Department of Computer & Information Science & Engineering, University of Florida, Gainesville, 32611, FL, USA.
authors.forename: Tamer
authors.initials: T
authors.lastname: Kahveci
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods* / Q000592:standards; D034622:RNA Interference; D012015:Reference Standards; D015398:Signal Transduction / Q000235:genetics*; D060449:Wnt Signaling Pathway / Q000235:genetics
keywords: Missing data; Network inference; RNAi data; Signal transduction networks