title: Improving Hidden Markov Models for classification of human immunodeficiency virus-1 subtypes through linear classifier learning.
abstract: Profile Hidden Markov Models (pHMMs) are widely used to model nucleotide or protein sequence families. In many applications, a sequence family classified into several subfamilies is given and each subfamily is modeled separately by one pHMM. A major drawback of this approach is the difficulty of coping with subfamilies composed of very few sequences.Correct subtyping of human immunodeficiency virus-1 (HIV-1) sequences is one of the most crucial bioinformatic tasks affected by this problem of small subfamilies, i.e., HIV-1 subtypes with a small number of known sequences.  To deal with small samples for particular subfamilies of HIV-1, we employ a machine learning approach. More precisely, we make use of an existing HMM architecture and its associated inference engine, while replacing the unsupervised estimation of emission probabilities by a supervised method. For that purpose, we use regularized linear discriminant learning together with a balancing scheme to account for the widely varying sample size. After training the multiclass linear discriminants, the corresponding weights are transformed to valid probabilities using a softmax function.We apply this modified algorithm to classify HIV-1 sequence data (in the form of partial-length HIV-1 sequences and semi-artificial recombinants) and show that the performance of pHMMs can be significantly improved by the proposed technique.
authors.affiliation: University of Greifswald.
authors.forename: Ingo
authors.initials: I
authors.lastname: Bulla
authors.forename: Anne-Kathrin
authors.initials: AK
authors.lastname: Schultz
authors.forename: Peter
authors.initials: P
authors.lastname: Meinicke
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016208:Databases, Factual; D015497:HIV-1 / Q000235:genetics*; D006801:Humans; D008390:Markov Chains; D010363:Pattern Recognition, Automated / Q000379:methods