Abstract
This article presents a discriminative approach to the protein classification in the particular case of remote homology. The protein family is modelled by a set M of motifs related to the physicochemical properties of the residues. We propose an algorithm for discovering motifs based on the ascending hierarchical classification paradigm. The set M defines a feature space of the sequences: each sequence is transformed into a vector that indicates the possible presence of the motifs belonging to M. We then use the SVM learning method to discriminate the target family. Our hierarchical motif set specifically modelises interleukins among all the structural families of the SCOP database. Our method yields a significantly better remote protein classification compared to spectrum kernel techniques.
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Mikolajczack, J., Ramstein, G., Jacques, Y. (2004). SVM-Based Classification of Distant Proteins Using Hierarchical Motifs. In: Yang, Z.R., Yin, H., Everson, R.M. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2004. IDEAL 2004. Lecture Notes in Computer Science, vol 3177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28651-6_4
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DOI: https://doi.org/10.1007/978-3-540-28651-6_4
Publisher Name: Springer, Berlin, Heidelberg
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