title: Causal reasoning on biological networks: interpreting transcriptional changes.
abstract: BACKGROUND
  The interpretation of high-throughput datasets has remained one of the central challenges of computational biology over the past decade. Furthermore, as the amount of biological knowledge increases, it becomes more and more difficult to integrate this large body of knowledge in a meaningful manner. In this article, we propose a particular solution to both of these challenges.
  
  
  METHODS
  We integrate available biological knowledge by constructing a network of molecular interactions of a specific kind: causal interactions. The resulting causal graph can be queried to suggest molecular hypotheses that explain the variations observed in a high-throughput gene expression experiment. We show that a simple scoring function can discriminate between a large number of competing molecular hypotheses about the upstream cause of the changes observed in a gene expression profile. We then develop an analytical method for computing the statistical significance of each score. This analytical method also helps assess the effects of random or adversarial noise on the predictive power of our model.
  
  
  RESULTS
  Our results show that the causal graph we constructed from known biological literature is extremely robust to random noise and to missing or spurious information. We demonstrate the power of our causal reasoning model on two specific examples, one from a cancer dataset and the other from a cardiac hypertrophy experiment. We conclude that causal reasoning models provide a valuable addition to the biologist's toolkit for the interpretation of gene expression data.
  
  
  METHODS
  R source code for the method is available upon request.
authors.affiliation: Computational Sciences Center of Emphasis, Pfizer Worldwide Research & Development, Cambridge, MA 02140, USA.
authors.forename: Leonid
authors.initials: L
authors.lastname: Chindelevitch
authors.forename: Daniel
authors.initials: D
authors.lastname: Ziemek
authors.forename: Ahmed
authors.initials: A
authors.lastname: Enayetallah
authors.forename: Ranjit
authors.initials: R
authors.lastname: Randhawa
authors.forename: Ben
authors.initials: B
authors.lastname: Sidders
authors.forename: Christoph
authors.initials: C
authors.lastname: Brockel
authors.forename: Enoch S
authors.initials: ES
authors.lastname: Huang
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000235:genetics*; D006332:Cardiomegaly / Q000235:genetics*; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling; D006801:Humans; D008954:Models, Biological