Learning to predict DNA hydration patterns

Dawn Cohen, Casimir Kulikowski, Bohdan Schneider, Helen Berman

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

Abstract

The authors examine the problem of learning to predict hydration patterns around DNA molecules. It is assumed that there is a limited, but so far unknown, set of hydration patterns, and that there is a set of features of a DNA molecule which determines its pattern. Since the patterns for the DNA molecules in the database were not known a priori, most traditional classifier learners cannot be applied directly. The authors have combined cluster analysis with a decision tree learner to develop classifiers, even though training examples were not initially labeled with classes. Some empirical results of this learning are presented, and it is shown how the learned decision trees are being used to gain insight into the domain of DNA crystallography.

Original languageEnglish (US)
Title of host publicationProceedings of the Conference on Artificial Intelligence Applications
PublisherPubl by IEEE
Pages204-210
Number of pages7
ISBN (Print)0818626902
StatePublished - Feb 1 1992
EventProceedings of the 8th Conference on Artificial Intelligence for Applications - Monterey, CA, USA
Duration: Mar 2 1992Mar 6 1992

Other

OtherProceedings of the 8th Conference on Artificial Intelligence for Applications
CityMonterey, CA, USA
Period3/2/923/6/92

All Science Journal Classification (ASJC) codes

  • Software

Fingerprint Dive into the research topics of 'Learning to predict DNA hydration patterns'. Together they form a unique fingerprint.

Cite this