Deep Learning concepts for genomics : an overview

Merouane Elazami Elhassani, Loic Maisonnasse, Antoine Olgiati, Rey Jerome, Majda Rehali, Patrice Duroux, Veronique Giudicelli, Sofia Kossida

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.


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

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

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