Multiple pattern associations for interpreting structural and functional characteristics of biomolecules
Pattern discovery from a data set can be intractable because both the detection and the interpretation of the patterns can be ill-posed and combinatorically explosive. This paper presents a knowledge exploratory method using multiple pattern associations to conjecture structural and functional characteristics of biomolecules. We first consider each site from an ensemble of aligned biomolecules as an attribute and the observed unit at the site as its value. Our method identifies those consistently observed attribute values whose associations with others deviate significantly from their null hypothesis. In addition, variables (representing molecular sites) with the detected attribute values as outcomes can be further analyzed. By integrating these associations, exploratory knowledge for interpreting the detected patterns could be discovered. During the interpretation phase, consistent and relevant descriptions of the data are searched. From the experiments using cytochrome c sequences, the discovered statistical patterns are found to be significant in relating to the location of a site with respect to its molecular structural characteristics and stability of functionality.
pattern discovery cytochrome c statistical interdependency biomolecular sequence analysis knowledge discovery