Mining asynchronous periodic patterns in time series data
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
WABI '02 Proceedings of the Second International Workshop on Algorithms in Bioinformatics
Efficient Mining of Partial Periodic Patterns in Time Series Database
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Identifying conserved gene clusters in the presence of orthologous groups
RECOMB '04 Proceedings of the eighth annual international conference on Resaerch in computational molecular biology
Bioinformatics
Hierarchical alignment graph for gene teams finding on whole genomes
Proceedings of the 2007 ACM symposium on Applied computing
PLATCOM: current status and plan for the next stages
DILS'05 Proceedings of the Second international conference on Data Integration in the Life Sciences
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Functionally related genes co-evolve, probably due to the strong selection pressure in evolution. Thus we expect that they are present in multiple genomes. Physical proximity among genes, known as gene team, is a very useful concept to discover functionally related genes in multiple genomes. However, there are also many gene sets that do not preserve physical proximity. In this paper, we generalized the gene team model, that looks for gene clusters in a physically clustered form, to multiple genome cases with relaxed constraint. We propose a novel hybrid pattern model that combines the set and the sequential pattern models. Our model searches for gene clusters with and/or without physical proximity constraint. This model is implemented and tested with 97 genomes (120 replicons). The result was analyzed to show the usefulness of our model. Especially, analysis of gene clusters that belong to B. subtilis and E. coli demonstrated that our model predicted many experimentally verified operons andfunctionally related clusters. Our program is fast enough to provide a sevice on the web at http://platcom. informatics.indiana.edu/platcom/. Users can select any combination of 97 genomes to predict gene teams.