Probabilistic prediction of protein secondary structure using causal networks

Authors:
Arthur L. Delcher;Simon Kasif;Harry R. Goldberg;William H. Hsu
Affiliations:
Computer Science Dept., Loyola College, Baltimore, MD;Dept. of Computer Science, Johns Hopkins University, Baltimore, MD;Mind-Brain Institute, Johns Hopkins University, Baltimore, MD;Mind-Brain Institute, Johns Hopkins University, Baltimore, MD
Venue:
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Year:
1993

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Abstract

In this paper we present a probabilistic approach to analysis and prediction of protein structure. We argue that this approach provides a flexible and convenient mechanism to perform general scientific data analysis in molecular biology. We apply our approach to an important problem in molecular biology--predicting the secondary structure of proteins--and obtain experimental results comparable to several other methods. The causal networks that we use provide a very convenient medium for the scientist to experiment with different empirical models and obtain possibly important insights about the problem being studied.