| Evolutionary inference for function-valued traits: Gaussian process regression on phylogenies. | |
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MedLine Citation:
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PMID: 23135249 Owner: NLM Status: In-Data-Review |
Abstract/OtherAbstract:
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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. |
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Authors:
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Nick S Jones; John Moriarty |
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Publication Detail:
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Type: Journal Article |
Journal Detail:
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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:
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Created Date: 2012-11-08 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 101217269 Medline TA: J R Soc Interface Country: England |
Other Details:
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Languages: eng Pagination: 20120616 Citation Subset: IM |
Affiliation:
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Department of Mathematics, Imperial College London, London SW7 2AZ, UK. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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