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.
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Acknowledgments
A.L. and N.F.L. thank the Dipartimento di Chimica, Biologia e Biotecnologie dell’Università di Perugia (FRB, Fondo per la Ricerca di Base 2017) and the Italian MIUR and the University of Perugia for the financial support of the AMIS project through the program “Dipartimenti di Eccellenza”. A. L. acknowledges financial support from MIUR PRIN 2015 (contract 2015F59J3R\(\_\)002). A.L. thanks the OU Supercomputing Center for Education & Research (OSCER) at the University of Oklahoma, for allocated computing time.
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Perri, D., Simonetti, M., Lombardi, A., Faginas-Lago, N., Gervasi, O. (2020). Binary Classification of Proteins by a Machine Learning Approach. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12255. Springer, Cham. https://doi.org/10.1007/978-3-030-58820-5_41
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DOI: https://doi.org/10.1007/978-3-030-58820-5_41
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