In this work we set out to find a method to classify protein structures using a Deep Learning methodology. Our Artificial Intelligence has been trained to recognize complex biomolecule structures extrapolated from the Protein Data Bank (PDB) database and reprocessed as images; for this purpose various tests have been conducted with pre-trained Convolutional Neural Networks, such as InceptionResNetV2 or InceptionV3, in order to extract significant features from these images and correctly classify the molecule. A comparative analysis of the performances of the various networks will therefore be produced.

A New Method for Binary Classification of Proteins with Machine Learning / Damiano Perri; Marco Simonetti; Andrea Lombardi; Noelia Faginas-Lago; Osvaldo Gervasi. - ELETTRONICO. - 12958 LNTCS:(2021), pp. 388-397. (Intervento presentato al convegno International Conference on Computational Science and Its Applications tenutosi a Cagliari nel 13/09/2021 - 16/09/2021) [10.1007/978-3-030-87016-4_29].

A New Method for Binary Classification of Proteins with Machine Learning

Damiano Perri;Marco Simonetti
;
2021

Abstract

In this work we set out to find a method to classify protein structures using a Deep Learning methodology. Our Artificial Intelligence has been trained to recognize complex biomolecule structures extrapolated from the Protein Data Bank (PDB) database and reprocessed as images; for this purpose various tests have been conducted with pre-trained Convolutional Neural Networks, such as InceptionResNetV2 or InceptionV3, in order to extract significant features from these images and correctly classify the molecule. A comparative analysis of the performances of the various networks will therefore be produced.
2021
Computational Science and Its Applications – ICCSA 2021
International Conference on Computational Science and Its Applications
Cagliari
13/09/2021 - 16/09/2021
Damiano Perri; Marco Simonetti; Andrea Lombardi; Noelia Faginas-Lago; Osvaldo Gervasi
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1293222
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