title: Bioinformatics models for predicting antigenic variants of influenza A/H3N2 virus.
abstract: BACKGROUND
  Continual and accumulated mutations in hemagglutinin (HA) protein of influenza A virus generate novel antigenic strains that cause annual epidemics.
  
  
  RESULTS
  We propose a model by incorporating scoring and regression methods to predict antigenic variants. Based on collected sequences of influenza A/H3N2 viruses isolated between 1971 and 2002, our model can be used to accurately predict the antigenic variants in 1999-2004 (agreement rate = 91.67%). Twenty amino acid positions identified in our model contribute significantly to antigenic difference and are potential immunodominant positions.
authors.affiliation: Division of Biostatistics and Bioinformatics, National Health Research Institutes, Zhunan 350, Taiwan.
authors.forename: Yu-Chieh
authors.initials: YC
authors.lastname: Liao
authors.forename: Min-Shi
authors.initials: MS
authors.lastname: Lee
authors.forename: Chin-Yu
authors.initials: CY
authors.lastname: Ko
authors.forename: Chao A
authors.initials: CA
authors.lastname: Hsiung
mesh_terms: D000595:Amino Acid Sequence; D000940:Antigenic Variation / Q000235:genetics*; D000956:Antigens, Viral / Q000737:chemistry / Q000235:genetics*; D019295:Computational Biology / Q000379:methods*; D019267:Hemagglutinin Glycoproteins, Influenza Virus / Q000737:chemistry; D053122:Influenza A Virus, H3N2 Subtype / Q000235:genetics*; D008958:Models, Molecular; D015233:Models, Statistical; D008969:Molecular Sequence Data; D010802:Phylogeny; D012044:Regression Analysis
chemical_list: D000956:Antigens, Viral; D019267:Hemagglutinin Glycoproteins, Influenza Virus