# Binary classification of proteins by a Machine Learning approach

arXiv: https://arxiv.org/abs/2111.01975 · HTML: https://mlsanity.com/paper/2111.01975/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 3 Nov 2021.

## Builds on (most cited first, 3 of 3)

- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Deep Recurrent Neural Network for Protein Function Prediction from Sequence](https://mlsanity.com/paper/1701.08318/) (2017)
- [Hardware for Machine Learning: Challenges and Opportunities](https://mlsanity.com/paper/1612.07625/) (2016)

## Built on by (most cited first, 1 of 1)

- [An example of use of Variational Methods in Quantum Machine Learning](https://mlsanity.com/paper/2208.04316/) (2022)

## Abstract (arXiv)

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.