title: Hidden Markov Models of the G-protein-coupled receptor family.
abstract: Hidden Markov Model techniques are used to derive a new model of the G-protein-coupled receptor family. The transition and emission parameters of the model are adjusted using a training set comprising 142 sequences. The resulting model is shown to perform well on a number of tasks, including multiple alignments, discrimination, large data base searches, classification, and fragment detection. General analytical results on the expectation and standard deviation of the likelihood of random sequences are also presented.
authors.affiliation: Division of Biology, California Institute of Technology, Pasadena 91125, USA.
authors.forename: P
authors.initials: P
authors.lastname: Baldi
authors.forename: Y
authors.initials: Y
authors.lastname: Chauvin
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D000818:Animals; D016208:Databases, Factual; D019143:Evolution, Molecular; D019204:GTP-Binding Proteins / Q000737:chemistry / Q000378:metabolism*; D006801:Humans; D016013:Likelihood Functions; D008390:Markov Chains; D008962:Models, Theoretical; D008969:Molecular Sequence Data; D011956:Receptors, Cell Surface / Q000737:chemistry* / Q000145:classification / Q000378:metabolism; D016415:Sequence Alignment / Q000379:methods* / Q000639:trends
chemical_list: D011956:Receptors, Cell Surface; D019204:GTP-Binding Proteins