Probabilistic prediction of protein secondary structure using causal networks

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationProceedings of the National Conference on Artificial Intelligence
PublisherPubl by AAAI
Pages316-321
Number of pages6
ISBN (Print)0262510715
StatePublished - Dec 1 1993
EventProceedings of the 11th National Conference on Artificial Intelligence - Washington, DC, USA
Duration: Jul 11 1993Jul 15 1993

Publication series

NameProceedings of the National Conference on Artificial Intelligence

Other

OtherProceedings of the 11th National Conference on Artificial Intelligence
CityWashington, DC, USA
Period7/11/937/15/93

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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