A Probabilistic Approach to Determining Biological Structure: Integrating Uncertain Data Sources

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Citation: Russ B. Altman. (1995) A Probabilistic Approach to Determining Biological Structure:  Integrating Uncertain Data Sources. In KSL-95-18, February,1995.

Publication techreport ( Edit )
type Technical Report
bibtype techreport
Bibtex basics
author Russ B. Altman
title A Probabilistic Approach to Determining Biological Structure: Integrating Uncertain Data Sources
number KSL-95-18
institution Knowledge Systems, AI Laboratory
address Stanford, CA, USA
year 1995
month February
Bibtex more
note Medical Computer Science
Access Paper
abstract Modeling the structure of biological molecules is critical for understanding how these structures perform their function, and for designing compounds to modify or enhance this function (for medicinal or industrial purposes). The determination of molecular structure involves defining three-dimensional positions for each of the constituent atoms using a variety of experimental,theoretical and empirical data sources. Unfortunately, each of these data sources can be noisy or not available in sufficient abundance to determine the precise position of each atom. Instead, some atomic positions are precisely defined by the data, and others are poorly defined. An understanding of structural uncertainty is critical for properly interpreting structural models. We have developed a Bayesian approach for determining the coordinates of atoms in a three-dimensional space. Our algorithm takes as input a set of probabilistic constraints on the coordinates of the atoms, and an a priori distribution for each atom location. The output is a maximum a posteriori (MAP) estimate of the location of each atom. We introduce constraints as updates to the prior distributions. In this paper, we describe the algorithm and show its performance on three data sets. The first data set is synthetic and illustrates the convergence properties of the method. The other data sets comprise real biological data for a protein (the trp repressor molecule) and a nucleic acid (the transfer RNA fold). Finally, we describe how we have begun to extend the algorithm to make it suitable for non-Gaussian constraints.

KSL Technical Report ID: KSL-95-18
Facts about A Probabilistic Approach to Determining Biological Structure: Integrating Uncertain Data SourcesRDF feed
Abstract Modeling the structure of biological molec … Modeling the structure of biological molecules is critical for understanding how these structures perform their function, and for designing compounds to modify or enhance this function (for medicinal or industrial purposes). The determination of molecular structure involves defining three-dimensional positions for each of the constituent atoms using a variety of experimental,theoretical and empirical data sources. Unfortunately, each of these data sources can be noisy or not available in sufficient abundance to determine the precise position of each atom. Instead, some atomic positions are precisely defined by the data, and others are poorly defined. An understanding of structural uncertainty is critical for properly interpreting structural models. We have developed a Bayesian approach for determining the coordinates of atoms in a three-dimensional space. Our algorithm takes as input a set of probabilistic constraints on the coordinates of the atoms, and an a priori distribution for each atom location. The output is a maximum a posteriori (MAP) estimate of the location of each atom. We introduce constraints as updates to the prior distributions. In this paper, we describe the algorithm and show its performance on three data sets. The first data set is synthetic and illustrates the convergence properties of the method. The other data sets comprise real biological data for a protein (the trp repressor molecule) and a nucleic acid (the transfer RNA fold). Finally, we describe how we have begun to extend the algorithm to make it suitable for non-Gaussian constraints. it suitable for non-Gaussian constraints.
Address Stanford, CA, USA  +
Author Russ B. Altman  +
Bibtype techreport  +
Has author Russ B. Altman  +
Has identifier KSL-95-18  +
Has publishing details February,1995  +
Has title A Probabilistic Approach to Determining Biological Structure: Integrating Uncertain Data Sources  +
Has where published KSL-95-18  +
Has year 1995  +
Institution Knowledge Systems, AI Laboratory  +
Ksl tr id KSL-95-18  +
Month February  +
Note Medical Computer Science
Number KSL-95-18  +
Process note NO  +
Title A Probabilistic Approach to Determining Biological Structure: Integrating Uncertain Data Sources  +
Year 1995  +
Retrieved from "http://tw.rpi.edu/wiki/A_Probabilistic_Approach_to_Determining_Biological_Structure:_Integrating_Uncertain_Data_Sources"
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