A decision graph explanation of protein secondary structure prediction

David L. Dowe, Jonathan Oliver, Trevor I. Dix, Lloyd Allison, Christopher S. Wallace

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

8 Citations (Scopus)


The machine-learning technique of decision graphs, a generalization of decision trees, is applied to the prediction of protein secondary structure to infer a theory for this problem. The resulting decision graph provides both a prediction method and an explanation for the problem. Many decision graphs are possible for the problem. A particular graph is just one theory or hypothesis of secondary structure formation. Minimum message length encoding is used to judge the quality of different theories. It is a general technique of inductive inference and is resistant to learning the noise in the training data. The method was applied to 75 sequences from nonhomologous proteins comprising 13 K amino acids. The predictive accuracy for three states (extended, helix, other) was in the range achieved by current methods.

Original languageEnglish
Title of host publicationProceedings of the 26th Hawaii International Conference on System Sciences, HICSS 1993
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages10
ISBN (Electronic)0818632305
Publication statusPublished - 1 Jan 1993
EventHawaii International Conference on System Sciences 1993 - Wailea, United States of America
Duration: 8 Jan 19938 Jan 1993
Conference number: 26th
https://ieeexplore.ieee.org/xpl/conhome/449/proceeding?isnumber=6718 (Proceedings)

Publication series

NameProceedings of the Annual Hawaii International Conference on System Sciences
ISSN (Print)1530-1605


ConferenceHawaii International Conference on System Sciences 1993
Abbreviated titleHICSS 1993
Country/TerritoryUnited States of America
Internet address

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