title: From protein microarrays to diagnostic antigen discovery: a study of the pathogen Francisella tularensis.
abstract: BACKGROUND
  An important application of protein microarray data analysis is identifying a serodiagnostic antigen set that can reliably detect patterns and classify antigen expression profiles. This work addresses this problem using antibody responses to protein markers measured by a novel high-throughput microarray technology. The findings from this study have direct relevance to rapid, broad-based diagnostic and vaccine development.
  
  
  RESULTS
  Protein microarray chips are probed with sera from individuals infected with the bacteria Francisella tularensis, a category A biodefense pathogen. A two-step approach to the diagnostic process is presented (1) feature (antigen) selection and (2) classification using antigen response measurements obtained from F.tularensis microarrays (244 antigens, 46 infected and 54 healthy human sera measurements). To select antigens, a ranking scheme based on the identification of significant immune responses and differential expression analysis is described. Classification methods including k-nearest neighbors, support vector machines (SVM) and k-Means clustering are applied to training data using selected antigen sets of various sizes. SVM based models yield prediction accuracy rates in the range of approximately 90% on validation data, when antigen set sizes are between 25 and 50. These results strongly indicate that the top-ranked antigens can be considered high-priority candidates for diagnostic development.
  
  
  BACKGROUND
  All software programs are written in R and available at http://www.igb.uci.edu/index.php?page=tools and at http://www.r-project.org.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: School of Information and Computer Sciences, University of California, Irvine, CA, USA.
authors.forename: Suman
authors.initials: S
authors.lastname: Sundaresh
authors.forename: Arlo
authors.initials: A
authors.lastname: Randall
authors.forename: Berkay
authors.initials: B
authors.lastname: Unal
authors.forename: Jeannine M
authors.initials: JM
authors.lastname: Petersen
authors.forename: John T
authors.initials: JT
authors.lastname: Belisle
authors.forename: M Gill
authors.initials: MG
authors.lastname: Hartley
authors.forename: Melanie
authors.initials: M
authors.lastname: Duffield
authors.forename: Richard W
authors.initials: RW
authors.lastname: Titball
authors.forename: D Huw
authors.initials: DH
authors.lastname: Davies
authors.forename: Philip L
authors.initials: PL
authors.lastname: Felgner
authors.forename: Pierre
authors.initials: P
authors.lastname: Baldi
mesh_terms: D000465:Algorithms; D000942:Antigens, Bacterial / Q000097:blood*; D001185:Artificial Intelligence; D005604:Francisella tularensis / Q000276:immunology*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D040081:Protein Array Analysis / Q000379:methods*; D012680:Sensitivity and Specificity; D012698:Serologic Tests / Q000379:methods*
chemical_list: D000942:Antigens, Bacterial