Mining association rules from biological databases

Andrés Rodríguez, José María Carazo and Oswaldo Trelles

Journal of the American Society for Information Science and Technology, 2005, vol. 56, issue 5, 493-504

Abstract: 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.

Date: 2005
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1002/asi.20138

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jamist:v:56:y:2005:i:5:p:493-504

Ordering information: This journal article can be ordered from
https://doi.org/10.1002/(ISSN)1532-2890

Access Statistics for this article

More articles in Journal of the American Society for Information Science and Technology from Association for Information Science & Technology
Bibliographic data for series maintained by Wiley Content Delivery ().