Efficient Mining of Frequent Subgraphs in the Presence of Isomorphism
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Almost-Delaunay simplices: nearest neighbor relations for imprecise points
SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
Common subgraph isomorphism detection by backtracking search
Software—Practice & Experience
The SuMo server: 3D search for protein functional sites
Bioinformatics
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We describe a new approach for inferring the functional relationships between non-homologous protein families by looking at statistical enrichment of alternative function predictions in classification hierarchies such as Gene Ontology (GO) and Structural Classification of Proteins (SCOP). Protein structures are represented by robust graphs, and the Fast frequent subgraph mining algorithm is applied to protein families to generate sets of family-specific packing motifs, i.e. amino acid residue packing patterns shared by most family members but infrequent in other proteins. The function of a protein is inferred by identifying in it motifs characteristic of a known family. We employ these familyspecific motifs to elucidate functional relationships between families in the GO and SCOP hierarchies. Specifically, we postulate that two families are functionally related if one family is statistically enriched by motifs characteristic of another family, i.e. if the number of proteins in a family containing a motif from another family is greater than expected by chance. This function inference method can help annotate proteins of unknown function, establish functional neighbors of existing families, and help specify alternate functions for known proteins.