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An encoding method has a direct effect on the quality and the representation of the discovered knowledge in data mining systems. Biological macromolecules are encoded by strings of characters, called primary structures. Knowing that data mining systems usually use relational tables to encode data, we have then to re-encode these strings and transform them into relational tables. In this paper, we do a comparative study of the existing static encoding methods, that are based on the Biologist know-how, and our new dynamic encoding one, that is based on the construction of Discriminant and Minimal Substrings (DMS). Different classification methods are used to do this study. The experimental results show that our dynamic encoding method is more efficient than the static ones, to encode biological macromolecules within a data mining perspective. [ABSTRACT FROM AUTHOR]

Studying protein structures is a major asset for understanding the molecular mechanisms of life. The number of publicly available protein structures has increasingly become extremely large. Yet, the classification of a protein structure remains a difficult, costly, and time-consuming task. Exploring spatial information on protein structures can provide important functional and structural insights. In this context, spatial motifs may correspond to relevant fragments, which might be very useful for a better understanding of proteins. In this article, we propose AntMot, a fast algorithm, to find spatial motifs from protein three-dimensional structures by extending the Karp–Miller–Rosenberg repetition finder, originally dedicated to sequences. The extracted motifs, termed ant-motifs, follow an ant-like shape that is composed of a backbone fragment from the primary structure, enriched with spatial refinements. We show that these motifs are biologically sound, and we used them as descriptors in the classification of several benchmark datasets. Experimental results show that our approach presents a trade-off between sequential motifs and subgraph motifs in terms of the number of extracted substructures, while providing a significant enhancement in the classification accuracy over sequential and frequent-subgraph motifs as well as alignment-based approaches. [ABSTRACT FROM AUTHOR]