Document Detail

Evolutionary inference for function-valued traits: Gaussian process regression on phylogenies.
MedLine Citation:
PMID:  23135249     Owner:  NLM     Status:  MEDLINE    
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.
Nick S Jones; John Moriarty
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Journal of the Royal Society, Interface / the Royal Society     Volume:  10     ISSN:  1742-5662     ISO Abbreviation:  J R Soc Interface     Publication Date:  2013 Jan 
Date Detail:
Created Date:  2012-11-08     Completed Date:  2013-04-19     Revised Date:  2014-01-10    
Medline Journal Info:
Nlm Unique ID:  101217269     Medline TA:  J R Soc Interface     Country:  England    
Other Details:
Languages:  eng     Pagination:  20120616     Citation Subset:  IM    
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MeSH Terms
Models, Biological*

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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