Binary classification of proteins by a Machine Learning approach

• URL: http://arxiv.org/abs/2111.01975v1
• Date: Wed, 3 Nov 2021 01:58:16 GMT
• Title: Binary classification of proteins by a Machine Learning approach
• Authors: Damiano Perri, Marco Simonetti, Andrea Lombardi, Noelia Faginas-Lago, Osvaldo Gervasi
• Abstract summary: We present a system 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 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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