Probabilistic Prediction of Protein Secondary Structure Using Causal Networks

Arthur L. Delcher, Simon Kasif, Harry R. Goldberg, William H. Hsu

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.


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