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Artificial Intelligence in Epigenetic Studies

2021-05-05Recensão Acesso aberto
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dc.contributor.authorBrasil, Sandra
dc.contributor.authorNeves, Cátia José
dc.contributor.authorRijoff, Tatiana
dc.contributor.authorFalcão, Marta
dc.contributor.authorValadão, Gonçalo
dc.contributor.authorVideira, Paula A.
dc.contributor.authordos Reis Ferreira, Vanessa
dc.contributor.institutionDCV - Departamento de Ciências da Vida
dc.contributor.institutionUCIBIO - Applied Molecular Biosciences Unit
dc.contributor.pblFrontiers Media
dc.date.accessioned2021-09-23T00:51:52Z
dc.date.available2021-09-23T00:51:52Z
dc.date.issued2021-05-05
dc.descriptionFunding Information: Funding. This work was supported by the CDG Professionals and Patient Associations International Network (CDG & Allies ?PPAIN) and Portuguese Association for Congenital Disorders of Glycosylation (APCDG). The authors confirmed independence from any sponsors.
dc.description.abstractMore than 7,000 rare diseases (RDs) exist worldwide, affecting approximately 350 million people, out of which only 5% have treatment. The development of novel genome sequencing techniques has accelerated the discovery and diagnosis in RDs. However, most patients remain undiagnosed. Epigenetics has emerged as a promise for diagnosis and therapies in common disorders (e.g., cancer) with several epimarkers and epidrugs already approved and used in clinical practice. Hence, it may also become an opportunity to uncover new disease mechanisms and therapeutic targets in RDs. In this “big data” age, the amount of information generated, collected, and managed in (bio)medicine is increasing, leading to the need for its rapid and efficient collection, analysis, and characterization. Artificial intelligence (AI), particularly deep learning, is already being successfully applied to analyze genomic information in basic research, diagnosis, and drug discovery and is gaining momentum in the epigenetic field. The application of deep learning to epigenomic studies in RDs could significantly boost discovery and therapy development. This review aims to collect and summarize the application of AI tools in the epigenomic field of RDs. The lower number of studies found, specific for RDs, indicate that this is a field open to expansion, following the results obtained for other more common disorders.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent934383
dc.identifier.doi10.3389/fmolb.2021.648012
dc.identifier.issn2296-889X
dc.identifier.otherPURE: 31612804
dc.identifier.otherPURE UUID: 6faa2945-5d90-4fa5-9469-e66e59634407
dc.identifier.otherScopus: 85105984241
dc.identifier.otherPubMed: 34026829
dc.identifier.otherPubMedCentral: PMC8131862
dc.identifier.otherWOS: 000651756100001
dc.identifier.urihttp://hdl.handle.net/10362/125016
dc.identifier.urlhttps://www.scopus.com/pages/publications/85105984241
dc.language.isoeng
dc.peerreviewedyes
dc.subjectartificial intelligence
dc.subjectepigenetics
dc.subjectepigenomic
dc.subjectmachine learning
dc.subjectpersonalized medicine
dc.subjectrare diseases (RD)
dc.subjectBiochemistry
dc.subjectBiochemistry, Genetics and Molecular Biology (miscellaneous)
dc.subjectMolecular Biology
dc.subjectSDG 3 - Good Health and Well-being
dc.titleArtificial Intelligence in Epigenetic Studiesen
dc.title.subtitleShedding Light on Rare Diseasesen
dc.typereview
degois.publication.titleFrontiers in Molecular Biosciences
degois.publication.volume8
dspace.entity.typePublication
rcaap.rightsopenAccess

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