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.
2020
Computational Science and Its Applications - ICCSA 2020
International Conference on Computational Science and Its Applications
Cagliari
01/07/2020 - 04/07/2020
Damiano Perri; Marco Simonetti; Andrea Lombardi; Noelia Faginas-Lago; Osvaldo Gervasi
File in questo prodotto:
File Dimensione Formato  
2111.01975.pdf

accesso aperto

Tipologia: Versione finale referata (Postprint, Accepted manuscript)
Licenza: Creative commons
Dimensione 485.19 kB
Formato Adobe PDF
485.19 kB Adobe PDF

I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1293227
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 11
  • ???jsp.display-item.citation.isi??? 16
social impact