Affiliations: [a] Department of Computer Science, Ryerson University, Toronto, ON, Canada | [b] Department of Information Engineering, Electronics, and Telecommunications, SAPIENZA University of Rome, via Eudossiana, Rome, Italy
Correspondence:
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Correspondence to: Lorenzo Livi, Department of ComputerScience, Ryerson University, 350 Victoria Street, Toronto, ONM5B 2K3, Canada. Tel./Fax: +1 416 979 5000; [email protected]
Abstract: We evaluate a version of the recently-proposed classification system named Optimized Dissimilarity Space Embedding (ODSE) that operates in the input space of sequences of generic objects. The ODSE system has been originally presented as a classification system for patterns represented as labeled graphs. However, since ODSE is founded on the dissimilarity space representation of the input data, the classifier can be easily adapted to any input domain where it is possible to define a meaningful dissimilarity measure. Here we demonstrate the effectiveness of the ODSE classifier for sequences by considering an application dealing with the recognition of the solubility degree of the Escherichia coli proteome. Solubility, or analogously aggregation propensity, is an important property of protein molecules, which is intimately related to the mechanisms underlying the chemico-physical process of folding. Each protein of our dataset is initially associated with a solubility degree and it is represented as a sequence of symbols, denoting the 20 amino acid residues. The herein obtained computational results, which we stress that have been achieved with no context-dependent tuning of the ODSE system, confirm the validity and generality of the ODSE-based approach for structured data classification.
Keywords: Dissimilarity representation, sequence matching and classification, E. coli proteome analysis, entropy-based data characterization
