title: Efficient approximations for learning phylogenetic HMM models from data.
abstract: BACKGROUND
  We consider models useful for learning an evolutionary or phylogenetic tree from data consisting of DNA sequences corresponding to the leaves of the tree. In particular, we consider a general probabilistic model described in Siepel and Haussler that we call the phylogenetic-HMM model which generalizes the classical probabilistic models of Neyman and Felsenstein. Unfortunately, computing the likelihood of phylogenetic-HMM models is intractable. We consider several approximations for computing the likelihood of such models including an approximation introduced in Siepel and Haussler, loopy belief propagation and several variational methods.
  
  
  RESULTS
  We demonstrate that, unlike the other approximations, variational methods are accurate and are guaranteed to lower bound the likelihood. In addition, we identify a particular variational approximation to be best-one in which the posterior distribution is variationally approximated using the classic Neyman-Felsenstein model. The application of our best approximation to data from the cystic fibrosis transmembrane conductance regulator gene region across nine eutherian mammals reveals a CpG effect.
authors.affiliation: Microsoft Research, Redmond, WA 98052, USA. vjojic@psi.toronto.edu
authors.forename: Vladimir
authors.initials: V
authors.lastname: Jojic
authors.forename: Nebojsa
authors.initials: N
authors.lastname: Jojic
authors.forename: Chris
authors.initials: C
authors.lastname: Meek
authors.forename: Dan
authors.initials: D
authors.lastname: Geiger
authors.forename: Adam
authors.initials: A
authors.lastname: Siepel
authors.forename: David
authors.initials: D
authors.lastname: Haussler
authors.forename: D
authors.initials: D
authors.lastname: Heckerman
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001483:Base Sequence; D003198:Computer Simulation; D030541:Databases, Genetic; D019143:Evolution, Molecular; D008390:Markov Chains; D008957:Models, Genetic; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods; D010802:Phylogeny; D017422:Sequence Analysis, DNA / Q000379:methods*; D012689:Sequence Homology, Nucleic Acid