title: Pandora, a pathway and network discovery approach based on common biological evidence.
abstract: BACKGROUND
  Many biological phenomena involve extensive interactions between many of the biological pathways present in cells. However, extraction of all the inherent biological pathways remains a major challenge in systems biology. With the advent of high-throughput functional genomic techniques, it is now possible to infer biological pathways and pathway organization in a systematic way by integrating disparate biological information.
  
  
  RESULTS
  Here, we propose a novel integrated approach that uses network topology to predict biological pathways. We integrated four types of biological evidence (protein-protein interaction, genetic interaction, domain-domain interaction and semantic similarity of Gene Ontology terms) to generate a functionally associated network. This network was then used to develop a new pathway finding algorithm to predict biological pathways in yeast. Our approach discovered 195 biological pathways and 31 functionally redundant pathway pairs in yeast. By comparing our identified pathways to three public pathway databases (KEGG, BioCyc and Reactome), we observed that our approach achieves a maximum positive predictive value of 12.8% and improves on other predictive approaches. This study allows us to reconstruct biological pathways and delineates cellular machinery in a systematic view.
authors.affiliation: Graduate Program in Bioinformatics, University of British Columbia, Vancouver, British Columbia, V6T 1Z4, Canada.
authors.forename: Kelvin Xi
authors.initials: KX
authors.lastname: Zhang
authors.forename: B F Francis
authors.initials: BF
authors.lastname: Ouellette
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D016208:Databases, Factual; D015398:Signal Transduction / Q000235:genetics; D012984:Software; D049490:Systems Biology