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A probabilistic classification system for predicting the cellular localization sites of proteins.

A probabilistic classification system for predicting the cellular localization sites of proteins. We have defined a simple model of classification which combines human provided expert knowledge with probabilistic reasoning. We have developed software to implement this model and have applied it to the problem of classifying proteins into their various cellular localization sites based on their amino acid sequences. Since our system requires no hand tuning to learn training data, we can now evaluate the prediction accuracy of protein localization sites by a more objective cross-validation method than earlier studies using production rule type expert systems. 336 E. coli proteins were classified into 8 classes with an accuracy of 81% while 1484 yeast proteins were classified into 10 classes with an accuracy of 55%. Additionally we report empirical results using three different strategies for handling continuously valued variables in our probabilistic reasoning system. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Proceedings. International Conference on Intelligent Systems for Molecular Biology Pubmed

A probabilistic classification system for predicting the cellular localization sites of proteins.

Proceedings. International Conference on Intelligent Systems for Molecular Biology , Volume 4: 7 – Jan 30, 1997

A probabilistic classification system for predicting the cellular localization sites of proteins.


Abstract

We have defined a simple model of classification which combines human provided expert knowledge with probabilistic reasoning. We have developed software to implement this model and have applied it to the problem of classifying proteins into their various cellular localization sites based on their amino acid sequences. Since our system requires no hand tuning to learn training data, we can now evaluate the prediction accuracy of protein localization sites by a more objective cross-validation method than earlier studies using production rule type expert systems. 336 E. coli proteins were classified into 8 classes with an accuracy of 81% while 1484 yeast proteins were classified into 10 classes with an accuracy of 55%. Additionally we report empirical results using three different strategies for handling continuously valued variables in our probabilistic reasoning system.

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ISSN
1553-0833
pmid
8877510

Abstract

We have defined a simple model of classification which combines human provided expert knowledge with probabilistic reasoning. We have developed software to implement this model and have applied it to the problem of classifying proteins into their various cellular localization sites based on their amino acid sequences. Since our system requires no hand tuning to learn training data, we can now evaluate the prediction accuracy of protein localization sites by a more objective cross-validation method than earlier studies using production rule type expert systems. 336 E. coli proteins were classified into 8 classes with an accuracy of 81% while 1484 yeast proteins were classified into 10 classes with an accuracy of 55%. Additionally we report empirical results using three different strategies for handling continuously valued variables in our probabilistic reasoning system.

Journal

Proceedings. International Conference on Intelligent Systems for Molecular BiologyPubmed

Published: Jan 30, 1997

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