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Published online on November 8, 2004, 10.1073/pnas.0406011101
PNAS | November 16, 2004 | vol. 101 | no. 46 | 16138-16143


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STATISTICS
Parametric inference for biological sequence analysis

Lior Pachter {dagger}, and Bernd Sturmfels

Department of Mathematics, University of California, Berkeley, CA 94720

Communicated by Stephen E. Fienberg, Carnegie Mellon University, Pittsburgh, PA, September 10, 2004 (received for review January 25, 2004)

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.

Abbreviations: MAP, maximum a posteriori; HMM, hidden Markov model.

{dagger} To whom correspondence should be addressed. E-mail: lpachter{at}math.berkeley.edu.

© 2004 by The National Academy of Sciences of the USA


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PNAS, November 16, 2004; 101(46): 16132 - 16137.
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