Some structural motifs, like tetra-loops, in ribosomal RNA are known to functionally implicate in virtually every aspect of protein synthesis. Our aim in this study is to discover common structural motifs (CSMs), which are related to specific domain or functions, within the secondary structures of ribosomal RNAs in a data set constructed. After applying data mining techniques to mine the common structural motifs, a machine learning approach is used to find significant discriminating common structural motifs from groups of organisms. By applying to several data sets constructed in this study, it suggests that the CSMs can provide effectiveinformation to classify organisms and help biologists understand the functions of ribosomal RNA. From the experiments of the classification of organisms and the construction of phylogenetic trees by CSMs mined, we find our approach is promising.
Relation:
International Journal on Artificial Intelligence Tools 14 (4): 621-639
