Data-driven theory refinement algorithms for bioinformatics

Jihoon Yang, Rajesh Parekh, Vasant Honavar, Drena Dobbs

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

7 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationProceedings of the International Joint Conference on Neural Networks
Number of pages5
StatePublished - 1999
EventInternational Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA
Duration: Jul 10 1999Jul 16 1999


OtherInternational Joint Conference on Neural Networks (IJCNN'99)
CityWashington, DC, USA

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence


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