title: A categorical network approach for discovering differentially expressed regulations in cancer.
abstract: BACKGROUND
  The problem of efficient utilization of genome-wide expression profiles for identification and prediction of complex disease conditions is both important and challenging. Polygenic pathologies such as most types of cancer involve disregulation of many interacting genes which has prompted search for suitable statistical models for their representation. By accounting for changes in gene regulations between comparable conditions, graphical statistical models are expected to improve prediction precision.
  
  
  METHODS
  In comparison problems with two or more experimental conditions, we represent the classes by categorical Bayesian networks that share one and the same graph structure but have class-specific probability parameters. The graph structure is learned by a score-based procedure that maximizes the difference between class probabilities using a suitable measure of divergence. The proposed framework includes an indirect model selection by adhering to a principle of optimal class separation and identifies interactions presenting significant difference between the compared conditions.
  
  
  RESULTS
  We evaluate the performance of the new model against some benchmark algorithms such as support vector machine, penalized linear regression and linear Gaussian networks. The classifiers are compared by prediction accuracy across 15 different data sets from breast, lung, gastric and renal cancer studies. In addition to the demonstrated strong performance against the competitors, the proposed method is able to identify disease specific changes in gene regulations which are inaccessible by other approaches. The latter is illustrated by analyzing some gene interactions differentiating adenocarcinoma and squamous cell lung cancers.
authors.forename: Nikolay
authors.initials: N
authors.lastname: Balov
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D006801:Humans; D008957:Models, Genetic; D009369:Neoplasms / Q000145:classification / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis; D015203:Reproducibility of Results