An iterative method for improved protein structural motif recognition
RECOMB '97 Proceedings of the first annual international conference on Computational molecular biology
An Algorithm for Subgraph Isomorphism
Journal of the ACM (JACM)
Summarizing itemset patterns: a profile-based approach
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Summarizing itemset patterns using probabilistic models
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Geometric sieving: automated distributed optimization of 3D motifs for protein function prediction
RECOMB'06 Proceedings of the 10th annual international conference on Research in Computational Molecular Biology
International Journal of Data Mining and Bioinformatics
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The exact relationship between protein active centres and protein functions is unclear even after decades of intensive study. To improve functional prediction ability based on the local structures, we proposed three different methods. 1 We used Markov Random Field (MRF) to describe protein active region. 2 We developed filtering method that considers the local environment around the active sites. 3 We created multiple structure motifs by extending the motif to neighbouring residues. Our experiment results with enzyme families <40% sequence identity demonstrated that our methods reduced random matches and could improve up to 70% of the functional annotation ability (using area under curve).