Bayesian parameter estimation for automatic annotation of gene functions using observational data and phylogenetic trees

Gene function annotation is important for a variety of downstream analyses genetic data. But experimental characterization remains costly and slow, making computational prediction an endeavor. Phylogenetic approaches to have been developed, but implementation practical Bayesian framework parameter estimation outstanding challenge. We developed computationally efficient model evolution gene annotations using phylogenies based on Markov Chain Monte Carlo estimation. Unlike previous approaches, our method able estimate parameters over many different phylogenetic trees functions. The resulting agree with biological intuition, such as the increased probability change following duplication. performs well leave-one-out cross-validation, we further validated some predictions in scientific literature.