Mining association rules from biological databases
Article first published online: 19 JAN 2005
Copyright © 2005 Wiley Periodicals, Inc.
Journal of the American Society for Information Science and Technology
Special Issue: Bioinformatics
Volume 56, Issue 5, pages 493–504, March 2005
How to Cite
Rodríguez, A., Carazo, J. M. and Trelles, O. (2005), Mining association rules from biological databases. J. Am. Soc. Inf. Sci., 56: 493–504. doi: 10.1002/asi.20138
- Issue published online: 9 FEB 2005
- Article first published online: 19 JAN 2005
- Manuscript Accepted: 22 APR 2004
We present a novel application of knowledge discovery technology to a developing and challenging application area such as bioinformatics. This methodology allows the identification of relationships between low-magnitude similarity (LMS) sequence patterns and other well-contrasted protein characteristics, such as those described by database annotations. We start with the identification of these signals inside protein sequences by exhaustive database searching and automatic pattern recognition strategies. In a second step we address the discovering of association rules that will allow tagging sequences that hold LMS signals with consequent functional keywords. We have designed our own algorithm for discovering association rules, meeting the special necessities of bioinformatics problems, where the patterns we search lie in sparse datasets and are uncommon and thus difficult to locate. Computational efficiency has been verified both with synthetic and real biological data showing that the algorithm is well suited to this application area compared to state of the art algorithms. The usefulness of the method is confirmed by its ability to produce previously unknown and useful knowledge in the area of biological sequence analysis. In addition, we introduce a new and promising application of the rule extraction algorithm on gene expression databases.