Foundations of statistical methods for multiple sequence alignment and structure prediction

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Statistical algorithms have proven to be useful in computational molecular biology. Many statistical problems are most easily addressed by pretending that critical missing data are available. For some problems statistical inference in facilitated by creating a set of latent variables, none of whose variables are observed. A key observation is that conditional probabilities for the values of the missing data can be inferred by application of Bayes theorem to the observed data. The statistical framework described in this paper employs Boltzmann like models, permutated data likelihood, EM, and Gibbs sampler algorithms. This tutorial reviews the common statistical framework behind all ... continued below

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58 p.

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Lawrence, C. December 31, 1995.

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  • Lawrence, C. New York State Dept. of Health, Albany, NY (United States). Wadsworth Center for Labs. and Research

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  • Stanford University
    Publisher Info: Stanford Univ., CA (United States)
    Place of Publication: California

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Description

Statistical algorithms have proven to be useful in computational molecular biology. Many statistical problems are most easily addressed by pretending that critical missing data are available. For some problems statistical inference in facilitated by creating a set of latent variables, none of whose variables are observed. A key observation is that conditional probabilities for the values of the missing data can be inferred by application of Bayes theorem to the observed data. The statistical framework described in this paper employs Boltzmann like models, permutated data likelihood, EM, and Gibbs sampler algorithms. This tutorial reviews the common statistical framework behind all of these algorithms largely in tabular or graphical terms, illustrates its application, and describes the biological underpinnings of the models used.

Physical Description

58 p.

Notes

OSTI as DE96014305

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  • Intelligent Systems for Molecular Biology (ISMB) conference, Cambridge (United Kingdom), 16-19 Jul 1995

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  • Other: DE96014305
  • Report No.: CONF-9507246--8
  • Grant Number: FG03-95ER62031
  • Office of Scientific & Technical Information Report Number: 414035
  • Archival Resource Key: ark:/67531/metadc685236

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Office of Scientific & Technical Information Technical Reports

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  • December 31, 1995

Added to The UNT Digital Library

  • July 25, 2015, 2:20 a.m.

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  • Feb. 17, 2017, 6:04 p.m.

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Lawrence, C. Foundations of statistical methods for multiple sequence alignment and structure prediction, article, December 31, 1995; California. (digital.library.unt.edu/ark:/67531/metadc685236/: accessed August 21, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.