Paper
8 July 1994 Probabilistic constraint network representation of biological structure
Russ B. Altman
Author Affiliations +
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.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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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KEYWORDS
Data modeling

Chemical species

Sensors

Kidney

Biological research

Image processing

Molecules

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