Data-Driven Theory Refinement Algorithms for Bioinformatics

Bioinformatics and related applications call for efficient algorithms for knowledgeintensive learning and data-driven knowledge refinement. Knowledge based arti- tending or modifying incomplete knowledge bases or do- main theories. we present results of experiments with sev- eral such algorithms for data-driven knowledge discovery and theory refinement in some simple bioinformatics appli- cations. Results of experiments on the ribosome binding site and promoter site identification problems indicate that the performance of KBDistAl and Tiling-Pyramid algorithms com- pares quite favorably with those of substantially more com- putationally demanding techniques. either augmenting it with new knowledge or by refining the &sting knowledge are called theory refinement systems. the following categories. ficial neural offer an attractive approach to ex- Theory refinement systems be , A~~~~~&~~ based on Rule Induction which use decision tree or rule learning algorithms for theory re vision. Examples of such systems include RTLS (6), EITHER (7), PTR (8), and TGCl (9). A~~~~~&~~ based on Inductive ~~~i~ program-