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Data-Driven Theory Refinement Algorithms for Bioinformatics
Zoology and Genetics Proceedings and Presentations
  • Jihoon Yang, HRL Laboratories
  • Rajesh Parekh, Allstate Research & Planning Center
  • Vasant Honavar, Iowa State University
  • Drena Dobbs, Iowa State University
Document Type
Conference Proceeding
Publication Version
Accepted Manuscript
Publication Date
Conference Title
International Joint Conference on Neural Networks, 1999
Conference Date
July 10-16, 1999
(38.9071923, -77.03687070000001)
Bioinformatics and related applications call for efficient algorithms for knowledge intensive learning and data driven knowledge refinement. Knowledge based artificial neural networks offer an attractive approach to extending or modifying incomplete knowledge bases or domain theories. We present results of experiments with several such algorithms for data driven knowledge discovery and theory refinement in some simple bioinformatics applications. Results of experiments on the ribosome binding site and promoter site identification problems indicate that the performance of KBDistAl and Tiling Pyramid algorithms compares quite favorably with those of substantially more computationally demanding techniques.

This is a proceeding from International Joint Conference on Neural Networks (1999): 4064, doi: 10.1109/IJCNN.1999.830811. Posted with permission.

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Citation Information
Jihoon Yang, Rajesh Parekh, Vasant Honavar and Drena Dobbs. "Data-Driven Theory Refinement Algorithms for Bioinformatics" Washington, DC(1999) p. 4064 - 4068
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