Work

Evolutionary inference for function-valued traits: Gaussian process regression on phylogenies

Year: 2012

Type: article

Abstract: Biological data objects often have both of the following features: (i) they are functions rather than single numbers or vectors, and (ii) they are correlated owing to phylogenetic relationships. In this paper, we give a flexible statistical model for such data, by combining assumptions from phylogenetics with Gaussian processes. We describe its use as a non-parametric Bayesian prior distribution, both for prediction (placing posterior distributions on ancestral functions) and model selection (comparing rates of evolution across a phylogeny, or identifying the most likely phylogenies consistent with the observed data). Our work is integrative, extending the popular phylogenetic Brownian motion and Ornstein-Uhlenbeck models to functional data and Bayesian inference, and extending Gaussian process regression to phylogenies. We provide a brief illustration of the application of our method.

Source: Journal of The Royal Society Interface

DOI: 10.1098/rsif.2012.0616

Authors: Nick S. Jones, John Moriarty

Institutions: Imperial College London, University of Manchester

Language: English

Topic: Gaussian Processes and Bayesian Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funder: Engineering and Physical Sciences Research Council

Awards: Functional Phylogenies, Functional Phylogenies, Mutating Messages - A Public Experiment, Functional Phylogenies