Probabilistic constraint network representation of biological structure

Russ B. Altman

doi:10.1117/12.179252

8 July 1994 Probabilistic constraint network representation of biological structure

Russ B. Altman

Proceedings Volume 2299, Mathematical Methods in Medical Imaging III; (1994) https://doi.org/10.1117/12.179252
Event: SPIE's 1994 International Symposium on Optics, Imaging, and Instrumentation, 1994, San Diego, CA, United States

Abstract

A constraint satisfaction paradigm is useful for modeling uncertain biological structure. Under this paradigm, we begin with a general model of a biological structure with a set of structural parameters and their uncertainty. Any new information about the structure is considered a constraint on the values of these parameters. The goal is to combine the initial model with the constraints to find a solution that is compatible with both. In this paper, we describe the basic notions of a constraint satisfaction problem and describe a method for representing biological structure that is based on the principles of Bayesian probability, and formulated as a constraint satisfaction problem. Biological structures are modeled using parameters that are assumed to be normally distributed, with a mean and a variance. We illustrate the application of this method to two different types of biological structural calculations: one in which there is a weak prior model and a large amount of data, and one in which there is a strong prior model and a relatively small amount of data. In each case, the method performs well, and produces not only good estimates of mean structure, but also a useful representation of the uncertainty in the estimate.

Citation Download Citation

Russ B. Altman "Probabilistic constraint network representation of biological structure", Proc. SPIE 2299, Mathematical Methods in Medical Imaging III, (8 July 1994); https://doi.org/10.1117/12.179252

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