In this work we present a system based on a Deep Learning approach, by using a Convolutional Neural Network, capable of classifying protein chains of amino acids based on the protein description contained in the Protein Data Bank. Each protein is fully described in its chemical-physical-geometric properties in a file in XML format. The aim of the work is to design a prototypical Deep Learning machinery for the collection and management of a huge amount of data and to validate it through its application to the classification of a sequences of amino acids. We envisage applying the described approach to more general classification problems in biomolecules, related to structural properties and similarities.
Binary Classification of Proteins by a Machine Learning Approach / Damiano Perri; Marco Simonetti; Andrea Lombardi; Noelia Faginas-Lago; Osvaldo Gervasi. - ELETTRONICO. - 12255 LNTCS:(2020), pp. 549-558. (Intervento presentato al convegno International Conference on Computational Science and Its Applications tenutosi a Cagliari nel 01/07/2020 - 04/07/2020) [10.1007/978-3-030-58820-5_41].
Binary Classification of Proteins by a Machine Learning Approach
Damiano Perri
;Marco Simonetti;
2020
Abstract
In this work we present a system based on a Deep Learning approach, by using a Convolutional Neural Network, capable of classifying protein chains of amino acids based on the protein description contained in the Protein Data Bank. Each protein is fully described in its chemical-physical-geometric properties in a file in XML format. The aim of the work is to design a prototypical Deep Learning machinery for the collection and management of a huge amount of data and to validate it through its application to the classification of a sequences of amino acids. We envisage applying the described approach to more general classification problems in biomolecules, related to structural properties and similarities.File | Dimensione | Formato | |
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