Data-Driven Theory Refinement Algorithms for Bioinformatics

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1999
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Yang, Jihoon
Parekh, Rajesh
Honavar, Vasant
Dobbs, Drena
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Dobbs, Drena
University Professor Emeritus
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Computer Science

Computer Science—the theory, representation, processing, communication and use of information—is fundamentally transforming every aspect of human endeavor. The Department of Computer Science at Iowa State University advances computational and information sciences through; 1. educational and research programs within and beyond the university; 2. active engagement to help define national and international research, and 3. educational agendas, and sustained commitment to graduating leaders for academia, industry and government.

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The Computer Science Department was officially established in 1969, with Robert Stewart serving as the founding Department Chair. Faculty were composed of joint appointments with Mathematics, Statistics, and Electrical Engineering. In 1969, the building which now houses the Computer Science department, then simply called the Computer Science building, was completed. Later it was named Atanasoff Hall. Throughout the 1980s to present, the department expanded and developed its teaching and research agendas to cover many areas of computing.

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1969-present

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Zoology and GeneticsComputer Science
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.

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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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Fri Jan 01 00:00:00 UTC 1999