Article
Data-Driven Theory Refinement Algorithms for Bioinformatics
Zoology and Genetics Proceedings and Presentations
Document Type
Conference Proceeding
Disciplines
Publication Version
Accepted Manuscript
Publication Date
1-1-1999
DOI
10.1109/IJCNN.1999.830811
Conference Title
International Joint Conference on Neural Networks, 1999
Conference Date
July 10-16, 1999
Geolocation
(38.9071923, -77.03687070000001)
Abstract
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.
Rights
© 1999 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Copyright Owner
IEEE
Copyright Date
1999
Language
en
File Format
application/pdf
Citation Information
Jihoon Yang, Rajesh Parekh, Vasant Honavar and Drena Dobbs. "Data-Driven Theory Refinement Algorithms for Bioinformatics" Washington, DC(1999) p. 4064 - 4068 Available at: http://works.bepress.com/drena-dobbs/54/
This is a proceeding from International Joint Conference on Neural Networks (1999): 4064, doi: 10.1109/IJCNN.1999.830811. Posted with permission.