Data-driven theory refinement algorithms for bioinformatics

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