Phylogenetic Dependency Networks: Inferring Patterns of CTL Escape and Codon Covariation in HIV-1 Gag
A PDN is a graphical model consisting of target attributes whose outcome is a probabilistic function of predictor attributes. Each of these probabilistic functions takes the phylogeny of the sequences into account. Here, the target attributes (green nodes) are binary and represent the presence or absence of amino acids at codons. These target attributes may have dependencies on other codons (codon covariation) and/or on HLA alleles (HLA-mediated escape), which are denoted by blue nodes. Arcs represent the learned dependencies between target and predictor attributes. All target attributes are assumed to be influenced by the phylogeny (red arcs). The probability components of a PDN are the local conditional probabilities, each of which relates a single target attribute to the phylogeny and a subset of the predictor attributes. These local conditional probabilities are learned independently for each target attribute. In the hypothetical example depicted here, B*57 and B*58 predict M1 and A*02 predicts A5. A5 predicts A3, and there is a cyclical dependency among M1, G2, A3 and R4, in which most of the arcs are bidirectional.