title: Optimal structural inference of signaling pathways from unordered and overlapping gene sets.
abstract: BACKGROUND
  A plethora of bioinformatics analysis has led to the discovery of numerous gene sets, which can be interpreted as discrete measurements emitted from latent signaling pathways. Their potential to infer signaling pathway structures, however, has not been sufficiently exploited. Existing methods accommodating discrete data do not explicitly consider signal cascading mechanisms that characterize a signaling pathway. Novel computational methods are thus needed to fully utilize gene sets and broaden the scope from focusing only on pairwise interactions to the more general cascading events in the inference of signaling pathway structures.
  
  
  RESULTS
  We propose a gene set based simulated annealing (SA) algorithm for the reconstruction of signaling pathway structures. A signaling pathway structure is a directed graph containing up to a few hundred nodes and many overlapping signal cascades, where each cascade represents a chain of molecular interactions from the cell surface to the nucleus. Gene sets in our context refer to discrete sets of genes participating in signal cascades, the basic building blocks of a signaling pathway, with no prior information about gene orderings in the cascades. From a compendium of gene sets related to a pathway, SA aims to search for signal cascades that characterize the optimal signaling pathway structure. In the search process, the extent of overlap among signal cascades is used to measure the optimality of a structure. Throughout, we treat gene sets as random samples from a first-order Markov chain model. We evaluated the performance of SA in three case studies. In the first study conducted on 83 KEGG pathways, SA demonstrated a significantly better performance than Bayesian network methods. Since both SA and Bayesian network methods accommodate discrete data, use a 'search and score' network learning strategy and output a directed network, they can be compared in terms of performance and computational time. In the second study, we compared SA and Bayesian network methods using four benchmark datasets from DREAM. In our final study, we showcased two context-specific signaling pathways activated in breast cancer.
  
  
  BACKGROUND
  Source codes are available from http://dl.dropbox.com/u/16000775/sa_sc.zip.
authors.affiliation: Department of Computer Science, University of New Orleans, New Orleans, LA 70148, USA.
authors.forename: Lipi R
authors.initials: LR
authors.lastname: Acharya
authors.forename: Thair
authors.initials: T
authors.lastname: Judeh
authors.forename: Guangdi
authors.initials: G
authors.lastname: Wang
authors.forename: Dongxiao
authors.initials: D
authors.lastname: Zhu
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000378:metabolism; D002450:Cell Communication; D019295:Computational Biology / Q000379:methods*; D004926:Escherichia coli / Q000378:metabolism; D005260:Female; D006801:Humans; D015398:Signal Transduction