Computational modeling and prediction of the human immunodeficiencyvirus (HIV) strains

摘要

This paper describes a stochastic approach for modeling thechanges observed in the DNA sequence of a highly mutating virus, such asthe human immunodeficiency virus (HIV). This modeling process is begunby clustering the known DNA sequences from the virus population intogroups such that the individual clusters represent biological strains ofthe modeled virus. Next, a hidden Markov model (HMM) is associated witheach cluster, and its parameters computed using Baum-Welch's expectationmaximization procedure. In this manner, the sequences within a clusterrepresent a maximally likely random sample drawn from the learned HMM.After the HMM for each strain cluster has thus been learned, it canfurther be used to generate additional samples of viral DNA sequencesthat are expected from the same underlying HMM. These newly predictedsequences would represent a maximally likely set of sequences belongingto a given viral strain modeled by the underlying HMM