Parallel algorithms for mining frequent structural motifs in scientific data
Proceedings of the 18th annual international conference on Supercomputing
CanTree: a canonical-order tree for incremental frequent-pattern mining
Knowledge and Information Systems
Clustering multidimensional sequences in spatial and temporal databases
Knowledge and Information Systems
Frequent subgraph mining on a single large graph using sampling techniques
Proceedings of the Eighth Workshop on Mining and Learning with Graphs
A multiobjective evolutionary programming framework for graph-based data mining
Information Sciences: an International Journal
| Hi-index | 0.00 |
Biochemical research often involves examining structural relationships in molecules since scientists strongly believe in the causal relationship between structure and function. Traditionally, researchers have identified these patterns, or motifs, manually using domain expertise. However, with the massive influx of new biochemical data and the ability to gather data for very large molecules, there is great need for techniques that automatically and efficiently identify commonly occurring structural patterns in molecules. Previous automated substructure discovery approaches have each introduced variations of similar underlying techniques and have embedded domain knowledge. While doing so improves performance for the particular domain, this complicates extensibility to other domains. Also, they do not address scalability or noise, which is critical for macromolecules such as proteins. In this paper, we present MotifMiner, a general framework for efficiently identifying common motifs in most scientific molecular datasets. The approach combines structure-based frequent-pattern discovery with search space reduction and coordinate noise handling. We describe both the framework and several algorithms as well as demonstrate the flexibility of our system by analyzing protein and drug biochemical datasets.