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2022;10 e13613.

Genomics enters the deep learning era.

Etienne Routhier, Julien Mozziconacci
Author Information
  1. Etienne Routhier: LPTMC, Sorbonne Université, Paris, France.
  2. Julien Mozziconacci: StrInG Lab, Museum National d'Histoire Naturelle, Paris, France.
PMID: 35769139 DOI: 10.7717/peerj.13613

Abstract

The tremendous amount of biological sequence data available, combined with the recent methodological breakthrough in deep learning in domains such as computer vision or natural language processing, is leading today to the transformation of bioinformatics through the emergence of deep genomics, the application of deep learning to genomic sequences. We review here the new applications that the use of deep learning enables in the field, focusing on three aspects: the functional annotation of genomes, the sequence determinants of the genome functions and the possibility to write synthetic genomic sequences.

Keywords

Bioinformatics Deep learning Epigenomics Genetics Genomics Metagenomics Neural networks Personalized medecine Review Synthetic genomes

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MeSH Term

Deep Learning
Neural Networks, Computer
Genomics
Computational Biology
Review Journal Article
Article has an altmetric score of 15

Word Cloud

Created with Highcharts 10.0.0deeplearningsequencegenomicsequencesgenomesGenomicstremendousamountbiologicaldataavailablecombinedrecentmethodologicalbreakthroughdomainscomputervisionnaturallanguageprocessingleadingtodaytransformationbioinformaticsemergencegenomicsapplicationreviewnewapplicationsuseenablesfieldfocusingthreeaspects:functionalannotationdeterminantsgenomefunctionspossibilitywritesyntheticenterseraBioinformaticsDeepEpigenomicsGeneticsMetagenomicsNeuralnetworksPersonalizedmedecineReviewSynthetic

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