Classifying Sequences by the Optimized Dissimilarity Space Embedding Approach: a Case Study on the Solubility Analysis of the E. coli Proteome

We evaluate a version of the recently-proposed Optimized Dissimilarity Space Embedding (ODSE) classification system that operates in the input space of sequences of generic objects. The ODSE system has been originally presented as a labeled graph classification system. However, since it 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. We demonstrate the effectiveness of the ODSE classifier for sequences considering an application dealing with recognition of the solubility degree of the Escherichia coli proteome. Overall, the obtained 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.