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Parametric Inference for Biological Sequence Analysis

Pachter, Lior and Sturmfels, Bernd (2004) Parametric Inference for Biological Sequence Analysis. Proceedings of the National Academy of Sciences of the United States of America, 101 (46). pp. 16138-16143. ISSN 0027-8424. PMCID PMC528961. doi:10.1073/pnas.0406011101. https://resolver.caltech.edu/CaltechAUTHORS:20170307-081738298

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Abstract

One of the major successes in computational biology has been the unification, by using the graphical model formalism, of a multitude of algorithms for annotating and comparing biological sequences. Graphical models that have been applied to these problems include hidden Markov models for annotation, tree models for phylogenetics, and pair hidden Markov models for alignment. A single algorithm, the sum-product algorithm, solves many of the inference problems that are associated with different statistical models. This article introduces the polytope propagation algorithm for computing the Newton polytope of an observation from a graphical model. This algorithm is a geometric version of the sum-product algorithm and is used to analyze the parametric behavior of maximum a posteriori inference calculations for graphical models.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1073/pnas.0406011101DOIArticle
http://www.pnas.org/content/101/46/16138.abstractPublisherArticle
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC528961/PubMed CentralArticle
https://arxiv.org/abs/q-bio/0401033arXivDiscussion Paper
ORCID:
AuthorORCID
Pachter, Lior0000-0002-9164-6231
Additional Information:© 2004 The National Academy of Sciences. Communicated by Stephen E. Fienberg, Carnegie Mellon University, Pittsburgh, PA, September 10, 2004 (received for review January 25, 2004) L.P. was supported in part by National Institutes of Health Grant R01-HG02362-02. B.S. was supported by a Hewlett Packard Visiting Research Professorship 2003/2004 at the Mathematical Sciences Research Institute (MSRI) at the University of California, Berkeley, and in part by National Science Foundation Grant DMS-0200729.
Funders:
Funding AgencyGrant Number
NIHR01-HG02362-02
Hewlett-Packard CompanyUNSPECIFIED
NSFDMS-0200729
Issue or Number:46
PubMed Central ID:PMC528961
DOI:10.1073/pnas.0406011101
Record Number:CaltechAUTHORS:20170307-081738298
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20170307-081738298
Official Citation:Lior Pachter and Bernd Sturmfels Parametric inference for biological sequence analysis PNAS 2004 101 (46) 16138-16143; published ahead of print November 8, 2004, doi:10.1073/pnas.0406011101
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:74828
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:07 Mar 2017 18:10
Last Modified:11 Nov 2021 05:30

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