Abstract
Three dimensional protein structures determine the function of a protein within a cell. Classification of 3D structure of proteins is therefore crucial to inferring protein functional information as well as the evolution of interactions between proteins. In this paper we propose to employ a recently presented structural representation of the proteins and exploit the learning capabilities of the graph neural network model to perform the classification task. © 2013 Springer-Verlag.
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CITATION STYLE
Cantoni, V., Ferone, A., Petrosino, A., & Di Baja, G. S. (2013). A supervised approach to 3D structural classification of proteins. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8158 LNCS, pp. 326–335). https://doi.org/10.1007/978-3-642-41190-8_35
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