Concept lattice-based mutation control for reactive motifs discovery

Authors:
Kitsana Waiyamai;Peera Liewlom;Thanapat Kangkachit;Thanawin Rakthanmanon
Affiliations:
Data Analysis and Knowledge Discovery Laboratory, Computer Engineering Department, Engineering Faculty, Kasetsart University, Bangkok, Thailand;Data Analysis and Knowledge Discovery Laboratory, Computer Engineering Department, Engineering Faculty, Kasetsart University, Bangkok, Thailand;Data Analysis and Knowledge Discovery Laboratory, Computer Engineering Department, Engineering Faculty, Kasetsart University, Bangkok, Thailand;Data Analysis and Knowledge Discovery Laboratory, Computer Engineering Department, Engineering Faculty, Kasetsart University, Bangkok, Thailand
Venue:
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Year:
2008

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Abstract

We propose a method for automatically discovering reactive motifs, which are motifs discovered from binding and catalytic sites, which incorporate information at binding and catalytic sites with bio-chemical knowledge. We introduce the concept of mutation control that uses amino acid substitution groups and conserved regions to generate complete amino acid substitution groups. Mutation control operations are described and formalized using a concept lattice representation. We show that a concept lattice is efficient for both representations of bio-chemical knowledge and computational support for mutation control operations. Experiments using a C4.5 learning algorithm with reactive motifs as features predict enzyme function with 72% accuracy compared with 67% accuracy using expert-constructed motifs. This suggests that automatically generating reactive motifs are a viable alternative to the time-consuming process of expert-based motifs for enzyme function prediction.