title: Expression profiles of switch-like genes accurately classify tissue and infectious disease phenotypes in model-based classification.
abstract: BACKGROUND
  Large-scale compilation of gene expression microarray datasets across diverse biological phenotypes provided a means of gathering a priori knowledge in the form of identification and annotation of bimodal genes in the human and mouse genomes. These switch-like genes consist of 15% of known human genes, and are enriched with genes coding for extracellular and membrane proteins. It is of interest to determine the prediction potential of bimodal genes for class discovery in large-scale datasets.
  
  
  RESULTS
  Use of a model-based clustering algorithm accurately classified more than 400 microarray samples into 19 different tissue types on the basis of bimodal gene expression. Bimodal expression patterns were also highly effective in differentiating between infectious diseases in model-based clustering of microarray data. Supervised classification with feature selection restricted to switch-like genes also recognized tissue specific and infectious disease specific signatures in independent test datasets reserved for validation. Determination of "on" and "off" states of switch-like genes in various tissues and diseases allowed for the identification of activated/deactivated pathways. Activated switch-like genes in neural, skeletal muscle and cardiac muscle tissue tend to have tissue-specific roles. A majority of activated genes in infectious disease are involved in processes related to the immune response.
  
  
  CONCLUSIONS
  Switch-like bimodal gene sets capture genome-wide signatures from microarray data in health and infectious disease. A subset of bimodal genes coding for extracellular and membrane proteins are associated with tissue specificity, indicating a potential role for them as biomarkers provided that expression is altered in the onset of disease. Furthermore, we provide evidence that bimodal genes are involved in temporally and spatially active mechanisms including tissue-specific functions and response of the immune system to invading pathogens.
authors.affiliation: School of Biomedical Engineering, Drexel University, Philadelphia, PA, USA. michael.patrick.gormley@drexel.edu
authors.forename: Michael
authors.initials: M
authors.lastname: Gormley
authors.forename: Aydin
authors.initials: A
authors.lastname: Tozeren
mesh_terms: D000465:Algorithms; D000818:Animals; D003141:Communicable Diseases / Q000145:classification* / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling; D005812:Genes, Switch / Q000235:genetics*; D005819:Genetic Markers; D006801:Humans; D051379:Mice; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D010641:Phenotype
chemical_list: D005819:Genetic Markers