Improving Hidden Markov Models for Classification of Human Immunodeficiency Virus-1 Subtypes through Linear Classifier Learning

Bulla Ingo, Schultz Anne-Kathrin and Meinicke Peter

Statistical Applications in Genetics and Molecular Biology, 2012, vol. 11, issue 1, 1-27

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.

Keywords: HMMs; linear classifier learning; HIV-1 (search for similar items in EconPapers)
Date: 2012
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DOI: 10.2202/1544-6115.1680

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