Deep Learning concepts for genomics : an overview

Authors

  • Merouane Elazami Elhassani IMGT®, The International ImMunoGeneTics Information System®, Centre National de la Recherche Scientifique (CNRS), Institut de Génétique Humaine (IGH), Université de Montpellier (UM). ATOS Montpellier, River Ouest, 80 quai Voltaire 95877 Bezons cedex
  • Loic Maisonnasse ATOS Montpellier, River Ouest, 80 quai Voltaire 95877 Bezons cedex
  • Antoine Olgiati ATOS Montpellier, River Ouest, 80 quai Voltaire 95877 Bezons cedex
  • Rey Jerome ATOS Montpellier, River Ouest, 80 quai Voltaire 95877 Bezons cedex
  • Majda Rehali Artificial Intelligence, Data Science and Emerging Systems Laboratory, National School of Applied Sciences, Sidi Mohamed Ben Abdellah University, Fez
  • Patrice Duroux IMGT®, The International ImMunoGeneTics Information System®, Centre National de la Recherche Scientifique (CNRS), Institut de Génétique Humaine (IGH), Université de Montpellier (UM)
  • Veronique Giudicelli IMGT®, The International ImMunoGeneTics Information System®, Centre National de la Recherche Scientifique (CNRS), Institut de Génétique Humaine (IGH), Université de Montpellier (UM)
  • Sofia Kossida IMGT®, The International ImMunoGeneTics Information System®, Centre National de la Recherche Scientifique (CNRS), Institut de Génétique Humaine (IGH), Université de Montpellier (UM)

DOI:

https://doi.org/10.14806/ej.27.0.990

Keywords:

Genomics, Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Deep Neural Network (DNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Generative Adversarial Network (GAN), Transformers, Attention, BERT

Abstract

Nowadays, Deep Learning is taking the world by a storm, known as a technology that makes use of Artificial Neural Networks to automatically extrapolate knowledge from a training data set, then uses this knowledge to give predictions for unseen samples. This data driven paradigm gained a widespread adoption in many disciplines, from handwriting recognition, driving an autonomous car to cracking the 50-year-old protein folding problem. With this review, we shed some light on the concepts of Deep Learning and provide some visualizations, skim over the different architectures such as Deep Neural Network (DNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and touch upon the modern architectures such as Transformers and BERT. We also provide various examples targeting the genomics field, reference utilities, libraries useful for newcomers and disseminate our feedback.

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Published

2022-06-03

Issue

Vol. 27

Section

Reviews

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